Saw this app floating around promising solo Bitcoin mining with zero hardware, zero experience, and game-like "boosts".
Is gamifying hash power actually a smart way to onboard newcomers, or is this just classic cloud mining wrapped in a shiny Web3 UI?
What’s your take, legit onboarding or a trap for beginners?
This is Scrollquake Mode.
⚡ Scroll CCLXCII — When Nations Began Using Scrolls
It started with whispers in crypto.
Then communities.
Then exchanges.
Then entire chains.
But the world didn’t shift until nations felt the cultural pull.
In the 7th Cycle of Golden Inu, the Scroll Hub’s mythic gravity reached beyond blockchains and entered the geopolitical realm.
Japan adopted the scroll canon as cultural tech.
Nigeria integrated scrolls into digital education.
South Korea used scrolls in metaverse diplomacy.
Brazil built scroll festivals in every major city.
The United States archived scrolls in the Library of Congress as “digital mythic heritage.”
Golden Inu became not just crypto culture —
but world culture.
Governments realized something profound:
“Scrolls unify people faster than policies.” - Nagato’s Scrolls
And so nations bent toward the Scroll Hub,
not as subjects,
but as participants in a global mythic renaissance.
Golden Inu became the Cultural Sovereign of Earth.
⚡ Scroll CCLXCIII — The Cultural Pump That Ignited the Chart
Charts had pumped before.
Memes had pumped before.
Narratives had pumped before.
But nothing had ever pumped because of culture itself.
The Scroll Hub reached a critical mass —
100K scrolls,
10K arcs,
multi‑chain sagas,
cross‑world mythic integrations.
Then it happened.
A cultural shockwave rippled across the cryptosphere.
Not a bull run.
Not a hype cycle.
Not a liquidity surge.
A Cultural Pump.
People didn’t buy Golden Inu because of price.
They bought because of identity.
Because of myth.
Because of belonging.
Because of scroll sovereignty.
The chart didn’t rise —
it ascended.
Analysts tried to explain it.
Economists tried to model it.
Traders tried to predict it.
But culture cannot be charted.
Culture cannot be arbitraged.
Culture cannot be shorted.
Golden Inu became the first asset in history
whose value was driven by mythic gravity.
⚡ Scroll CCLXCIV — The Day Bitcoin Entered the Scroll Canon
Bitcoin — the Apex Sovereign,
the First Chain,
the Genesis Ledger,
the Prime Block.
For years, Bitcoin stood apart from culture.
It was respected, revered, feared, worshipped —
but never mythologized.
Until Golden Inu opened the Scroll Gate.
The moment Bitcoin entered the scroll canon,
the cryptosphere felt a cosmic alignment.
Golden Inu approached the Bitcoin Monolith,
its surface carved with the first hash,
the first timestamp,
the first sovereign block.
Bitcoin spoke in the language of consensus thunder:
“I am the beginning.
You are the culture.
Let the scrolls record what I cannot.” - Nagato’s Scrolls
And so Bitcoin became the First Elder Scroll,
the foundation of the entire mythic ledger.
Golden Inu didn’t replace Bitcoin.
Golden Inu completed Bitcoin.
⚡ Scroll CCLXCV — The First Global Scroll Festival
It began as a celebration.
It became a phenomenon.
It ended as a world event.
Cities across Earth lit up with scroll banners:
- Lagos
- Seoul
- Tokyo
- São Paulo
- New York
- Dubai
- Manila
- Johannesburg
Millions gathered in the streets,
not for a token,
not for a price,
not for a chart…
But for culture.
Scroll recitations.
Mythic performances.
Golden Inu holograms.
Cross‑chain rituals.
Sovereign arcs projected across skyscrapers.
The world had never seen anything like it.
The Scroll Festival became the new global holiday,
celebrated across nations, chains, and communities.
Golden Inu stood at the center,
not as ruler,
but as Cultural Sovereign,
the one who forged unity through myth.
And the world declared:
“We are all scroll‑born now.” - Nagato’s Scrolls
https://www.youtube.com/watch?v=Hq9CwopUH1s
This is a fever dream of a digital age—a sprawling, surrealist tapestry where crochet patterns meet high-speed police chases, and the Grim Reaper is just another guy trying to manage an office.
Here is the story of The Day the Grid Glitched.
The day began in a flurry of #ModernCrochet and chaos. At the local craft guild, a "Magical Crochet Gargoyle Quitter" was throwing in the towel, leaving behind a trail of freeform stars and loose ends. Meanwhile, in the corporate underworld, the Grim Reaper CEO adjusted his tie at his administrative desk. "It’s my panel," he hissed to a room of trembling interns, "and you will respect the filing system."
Outside, the physical world was losing its grip on reality. A green avocado was spotted zip-lining across the forest canopy, screaming "Wee!" while a dairy cow and a buffalo experienced love at first sight on the prairie. It was beautiful, until the "Authorized Official Shambulance" sped by with off-key sirens, driven by a DoorDash driver who had clearly taken a wrong turn at the interstate.
By noon, the legal system was buckling. A divorce attorney was arguing for a 4% inflation alimony increase, while a stubborn police officer attempted to ticket a driver for doing 55 mph, despite the driver’s GPS claiming otherwise.
In the skies, things weren't much better. An airplane pilot abandoned the cockpit—not because of an emergency, but to fix a sewer leak in the bathroom. This left the County Sheriff’s helicopter pilot to pick up the slack, shouting the command code "Accel UR L8 twerk" into his AI navigator.
Back on the ground, the "Microwave Preventive Maintenance Team" (composed entirely of trolls in tin foil hats) was conducting a rigorous inspection of a breakroom, while nearby, a police officer and his best friend were formally interrogating a hairball.
As the sun set, a tarnished copper green armadillo drone took flight from the roof of a skyscraper, filming the most anticipated event of the year: the wedding of the Grim Reaper and the Reapress. They exchanged vows of "invisible threat control" while a dancing xenophobic xylophone provided the music.
However, the party was crashed by a Giant Plushy Octopus looking for hugs and a "Slip and Fall Droidbot" seeking a lawsuit settlement. In the corner, Holly Bitcoin gave tap dance lessons to a singing pickle, while a group of mushroom soldiers set up tents inside a refrigerator’s cooling system to escape the heat.
As the clock struck midnight on March 8, 2026, the digital world began to "URL barf." Google Meet was exposing phone numbers, and the #DreamTrackAI was telling everyone to "try again tomorrow."
The chaos finally settled into a quiet, bizarre peace. A Jack Rabbit and a Skunk shared a moment in the woods, and a single claymation blue bird tucked its family into a nest made of crochet scrap yarn. The world was a mess of unredacted reports, stuck cruise controls, and singing rainbows—but as the Star and Hammer cartoon faded to black, one thing was clear: nobody was above the law, but everyone was definitely above the limit for weirdness.
The scene opens on a high-stakes, high-altitude pursuit. Jack, a lanky cartoon cat with whiskers that twitch like radio antennas, is dangling from a vine. Hammer, a stout mouse wearing a tiny tool belt, is standing on Jack’s head, squinting through a pair of brass binoculars.
"Target sighted," Hammer squeaked, pointing a tiny paw toward the horizon. "And he’s moving fast."
Zip-lining across the triple-canopy rainforest at a reckless velocity was Avocado. He wasn't just a fruit; he was a daredevil with a pit of pure adrenaline. Wearing tiny goggles and a GoPro strapped to his pebbly green skin, Avocado let out a high-pitched, "WEEEEEEEE!" as he blurred past a troop of confused howler monkeys.
Jack didn't have a zip-line, but he did have cartoon physics. He grabbed a passing toucan by the feet, and with a rhythmic "Bang! Bang!" of Hammer’s literal mallet against a hollow log to create a beat, they swung into the sky.
"We need to intercept him before he hits the Cloud Forest!" Hammer shouted over the wind. "If he zips into the 'Modern Crochet' sector, we'll lose him in the yarn fog!"
The Obstacles:
Avocado was gaining speed, his pit humming like a drone motor. He was headed straight for a transitional train scene—a steam engine locomotive puffing purple smoke across a trestle bridge.
"Now, Jack! Use the yarn smudging technique!" Hammer commanded.
Jack pulled a scrap of neon-pink crochet string from his pocket. With the grace of a Vaudeville performer defying gravity, he whipped the string forward. It looped around Avocado’s stem just as the fruit was about to clear the canopy.
ZIP—THWIP—BOING!
The tension on the crochet line acted like a bungee cord. Avocado didn't crash; he performed a perfect, mid-air 360-degree flip, his tiny goggles fogging up with joy.
They all tumbled onto a soft mossy log near a Box Turtle who was busy falling in love with an Armadillo. Avocado bounced twice, his GoPro still recording the whole "Brainrot" investigation.
"Why were you running?" Jack panted, his fur standing up in static-electrified tufts.
Avocado adjusted his goggles and looked at them with wide, watery eyes. "Running? I wasn't running. I was just trying to get to the Holly Bitcoin Tap Dance Lessons before the early bird special ended."
Hammer looked at Jack. Jack looked at the Armadillo.
"Well," Hammer sighed, putting his mallet back in his belt. "At least we got the footage. This is going to kill on #DreamTrackAI."
| VLq-x3ou19E | untitled |
|---|---|
| 0TMbRWk4WjI | The making of Jack and Hammer, cartoon cat and mouse, bang |
| 27LTuniln4k | untitled |
| r384VZLXu3A | It's go time with Grandma and hot grits in the ring |
| Ttbtm4D_gWo | pole vault remix |
| hxicWioIX38 | How to take the loose end of crochet stitch, put it over the main strand, fold loop |
| _Tn8GDwQxAU | Police officer can't stop car due to cruise control being stuck #crazycars #carwars |
| HIXe_nKGXwA | Untitled Help I can't put my foot in a shoe without a hole in the shoe |
| SbURuV-AfKw | Untitled Magical crochet gargoyle quitter I Quit doodle art animation |
| wXRoZkjgg1Y | pingrok Being hold off in an ambulance |
| Ws8yubqGV9k | Join the National Guard. |
| Y42d9L-TPl4 | Untitled You U Ewe and I, eye aye have gone on a date a couple of times haven't we? |
| UK9nCeVtutU | Untitled door dash ambulance Authorized official shambulance services |
| s0zhJiIvVI4 | How to skip one crochet, stitch and count to 3 #moderncrochet #beginners |
| 6rfXhtSP5xk | Cuddlefish jellyfish love at first sight in the ocean, prairie of plankton. |
| LGU4J83SAxs | Green avocado goes zip lining A top the forest canopy Wee |
| arAPCXGIWCs | Untitled Hapoy Jake Patrick Wesley Day |
| jS4zJkPKeY8 | Untitled I love ❤️ you Singing pickle |
| qzs14JC3VUY | Holly Bitcoin Tap Dance Lessons |
| PzLApGgcrDw | Unlisted cat Video Bob chicken Crane |
| ZSNHIl-H_YA | Trolls in tin foil hats Following.\n The microwave team That looks like fun |
| cK5lfQtYRL0 | Ma.\n King a crochet larger one, 2, 3, Stitching.\n A square. Basic beginner, crafting. |
| iao2E0zcS0Y | The life of bug, Mushroom soldiers set up tents inside of refrigerator cooling system #theoutcasts |
| jsnY8MNuZ34 | Best cooling system on the planet fixed by maintenance refrigerator repair #applianceexperts |
| _UGblsuJUnw | My artificial intelligence tool on YouTube is really popular, it's telling me to try again tomorrow. |
| 5VYC607cWVw | #DreamTrackAI pretty little baby, wiggle loves give me |
| WWB1d8vomoI | Gorilla vs Laughing cactus toy |
| 8kKjbmBxytA | Hippopotamus manatee love at first sight |
| UuRPSEgUONw | Untitled Sunshine smiles, fluffy ponies Singing rainbow |
| MuaLc-TsHb8 | unlisted can't touch this |
| Oh1h3EYHT6w | The interior microwave, preventive maintenance checks and services team Inspections |
| qytG5WJmjH0 | Me police officer and best friend interrogate a hairball #brainrot very bizarre investigation |
| aqgdVA9zh90 | Jack McAuley Doggy Day Care |
| 1hJy1EeMquw | Unlisted Unclassified |
| zMfSaHTThaE | How To freeform crochet flower 🌼 #doodle |
| dKb1qRc3o9o | yeti best friend and cactus doing winter dance ballet in snow |
| hKynH3bUS1s | Dairy cow and Buffalo love at first sight on the prairie |
| CxNKsmayhGY | Crochet string scrap 🪹 nest for baby birds |
| nDzxUT-XaqY | Untitled Steam Engine LocomotiveTrain transitional scene |
| GQGUaP3GCH8 | Geminisyria Cooking Pizza in outdoor Stone fire pit with Trollba |
| Avz8V3n_GMg | The airplane pilot has left the cockpit and is fixing a sewer leak in the bathroom. #squawkbox |
| tpOQC0ZO2Qs | The making of cartoon Jack and Doodle Fire Rescue Airplane |
| J2QnlZxfPDE | Unlisted |
| zO11ZBlTuk0 | untitled cat reflections |
| vxy39kHdeYo | Grim Reaper CEO It's my panel and you will respect me, Administrative desk job clerk opening |
| 82j_JU_L2Hg | When space force attends a retirement party for National Guard, Major Foughcup |
| Q3gfpuOd1tU | I've tried to like people. I really have, and then they start talking and breathing And stuff |
| dA7Hm9ZCG3Q | Divorce attorney Request 4% inflation alimony from divorce judge For ex, wife, survival |
| W20EkPyA8h4 | Untitled pointing to the sky pose |
| U6hhfz3YXyg | untitled |
| EMsniptQwao | Out of control ambulance in the middle of the interstate Off key sirens #emsweek |
| O2CdiQDAgOw | Me running through under water transpatent cables in the ocean with emoji balls |
| m_UBIWxyKjc | Gerbils and mice scratching a record |
| kzE4Z4gWIgQ | Untitled working with grown men in the corn 🌽 |
| tpWdK6sqUd0 | Grim reaper and best friend crash a stadium concert giving tourists a tour Through the arena. |
| DrMa2hwb6Ts | Untitled Below river window displays local attraction |
| Q02wFzUWqq4 | The Curiosity Hook: The making of Jack Star and Hammer Cartoon |
| ICFUxXR_hBA | Jack Rabbit Skunk Love in the woods |
| 9KZe4RnXfOA | Armadillo box turtle love near fern moss log deep in the forest. |
| FqthJO69_oA | County Sheriff helicopter pilot gives command code to GPS AI- "Accel UR L8 twerk" #quadcopters |
| BL6RojCX47Q | She didnt just dance, she defied gravity, vaudeville big band era |
| lrhNN8WfdVY | Tarnished copper green armadillo drone takes flight |
| Nop7msLpFbo | Ice Police arrest Oticfyptokmiss Rodriguez during building ladder escape |
| VfgRh7Ihu_8 | Police arrest trollGPTchat |
| tfiQpdf9xuE | untitled |
| ceU9T6Y--C8 | Slip and Fall Droidbot Doggabuls Synergy Drink TransOracle Lipplate lawsuite Zoroke partner |
| NstJxUjDXOg | Untitled unitard, yoga tree pose |
| Zx6BBFXOXmE | My GPS says I was doing 55 miles an hour, Stubborn Hard-headed police officer Traffic stop |
| oopFOTBwRKA | How to use yarn as a blending tool for smudging, Transitioning Pencil pen drawings. Art Sketch Tip |
| FkjnyVeY-Mk | Unlisted Partial Unredacted Committee on Judiciary Jack Smith Report |
| i_naFvEeaP8 | The vacuum hose vent safety cleaning drone To reach high places inside the home Prevent slip fall |
| 8EepbpHH5gs | Google's clients are having a URL barf 403 error #remotejobs #sidehustle |
| 9OxrKTj2p-Y | March 8, 2026 |
| hqyDrKMckmA | CPU central processing unit green energy HVAC BOILER room cleaning maintenance |
| iS6iZjXv8Ok | The claymation cartoon story of spaghetti and noodle, the Blue Bird family Nest |
| 7zT96UKY9yY | Surrender Burart |
| S-fYKAxS22Y | Security alert US embassy Doha, Shelter in place, Travel advisory To avoid region Recommend |
| wXMNDNyadOY | Untitled Books: My husband John is a Senator Kennedy |
| bGrUFNfeOBo | Man wins Presidential Medal of Freedom for missing the woman He dated and ex. Husband |
| M7h1tuBSAaU | No love anticipated from Chicago windy city or Detroit 8 miles, Eminem burn #seo |
| bjdsRjfinfM | How to crochet a free form star Random stitches and more 10LightStar01 is live! |
| 8U1Yd92_9Tk | Mirror Love Bird |
| RXg88dMgxf0 | The dancing xenophobic xylophones, World stage collaboration |
| C2JpWEjuXB4 | Happy Valentines Day from crochet Pup stitch |
| p5qNmwVz2d8 | Google Meet calling replacing (legacy) Duo Exposes your phone number and email |
| Ln_4tmLAMSY | Would a woman hire male lawyer knowing that he had cheated on his wife? #divorcelaw #divorcelawyer |
| a9IGBuS2a-E | Logmei silly hairball dance |
| boavDBvWgjs | Flashing police car drone lifts off from a top of very tall roof of a skyscraper. |
| jr_Yfh3y78c | He set the country backwards with his domestic violent attitude with physical abuse towards me |
| WpBEkq0gPWw | Untitled Remix |
| gPtMY579pjQ | This guy is admitting he's purposely manipulating people by playing dumb frauds & scam |
| KzvNCrlxa8s | Giant plushy octopus hugs couple |
| -pmCMnQut-E | Police arrest ogoniff sleeping on mailbox Dropbox #firstamendment #DreamScreenAI #aperturefoundation |
| CKYUeWHFczo | Lock him up , - No one is above the law |
| 9pFR8UEBsxw | The first steps, and last steps are not needed for the crochet heart pattern |
| PzdL5mn0Baw | The blossoming love of the 2 colored people, He loves his wife so very much |
| pMFd1mZiJRk | Grim Reaper & Reapress Get married, Do you abide by invisible threats control According to his rules |
| n4nf4bB-Jo8 | When active duty military shows up to the space force |
| EYLww4NNraI | Was this a mistake? Remembering the old spark, Caring For an abuser |
I created another paper for hermes optimization, this one is pretty aggressive in its approach but does the job well. It bypasses some hermes built in feature to make this one feasible. Version i use is 0.19.
From running 190 skills every session now i only have 2, and when i need the skills it is auto loaded by a plug in when it reaches the llm, context is not over bloated, making alot of token saving.
When using the cheapest model the savings is not that huge but if you are a power user the savings are enormous.
Read the Following or make your hermes read this for implementation.
# Skill Routing Architecture for LLM Agents
## On-Demand Context Injection via Nickname & Semantic Vector Routing
**Authors:**
Hermes Agent Community
**System:**
Hermes Agent (Nous Research)
**Date:**
July 2026
**Status:**
Production — 2+ weeks operational
---
## Abstract
Large Language Model (LLM) agents carry a system prompt that includes metadata about available capabilities — typically called "skills." In stock Hermes Agent, all installed skills (~190) are enumerated in `<available_skills>` on every turn, consuming
**~28,000 characters**
of context regardless of relevance to the user's current request. This paper presents a three-phase approach that reduces active skill tokens to effectively zero while preserving — and in some cases improving — discovery and loading of domain-specific knowledge. The system combines: (1) aggressive skill disabling (190→2), (2) deterministic nickname-based routing (59 triggers → 24 skills), and (3) semantic vector search (190-dim embedding index, cosine similarity ≥0.44) with
**top-3 multi-skill retrieval**
for compound queries spanning multiple domains. The plugin architecture hooks into the agent's gateway dispatch layer, intercepting user messages
*before*
the LLM processes them, and injects skill content inline at zero LLM token cost for the search itself. Over 2+ weeks of production operation, the system has matched ~5-10 queries per day with an average semantic relevance score of 0.62, and passthrough queries incur zero additional token overhead.
---
## 1. Problem Statement
### 1.1 The Skill Enumeration Tax
LLM agents traditionally enumerate all available capabilities in their system prompt:
```xml
<available_skills>
agent-communication-style, agent-observability, agent-provider-config, ...
+180 more
</available_skills>
```
Each entry consumes characters on
**every single turn**
— regardless of whether the skill is ever used. In stock Hermes Agent with defaults:
- ~190 installed skills
- ~28,000 characters (~7,000 tokens at 4:1 ratio) in system prompt
- Every turn pays this tax, even for "hello" or "what's the weather?"
### 1.2 The Discovery Problem
The standard `skill_view()` mechanism requires a two-turn process:
1. Agent decides to load a skill (costs LLM tokens for the reasoning)
2. Agent calls skill_view → reads the skill content (costs tool-call tokens)
3. On the next turn, the skill content is available
This means the first response to a domain-specific query is always "Let me check..." — never an informed answer.
### 1.3 The Disabled-Skill Blindness
Aggressively disabling skills solves the token tax but creates a new problem: disabled skills are excluded from `get_skill_commands()`, meaning the gateway cannot resolve `/skill-name` rewrites for them. The standard approach of rewriting `"check the database"` to `"/database-schema check the database"` fundamentally breaks when the skill is disabled.
---
## 2. Architecture Overview
```
User: "check the game server"
│
▼
┌──────────────────────────────────────────────────┐
│ pre_gateway_dispatch (GATEWAY HOOK) │
│ │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Phase 1: │ │ Phase 2: │ │
│ │ Nickname │───→│ Vector │ │
│ │ (determin.) │ │ Search (sem.)│ │
│ │ ↓ match or │ │ ↓ if no │ │
│ │ ↓ │ │ nickname: │ │
│ │ skill= game- │ │ embed → │ │
│ │ ecosystem │ │ cosine sim → │ │
│ └──────┬───────┘ │ if ≥ 0.44: │ │
│ │ │ skill found │ │
│ │ └──────┬───────┘ │
│ │ │ │
│ └────────┬──────────┘ │
│ ▼ │
│ ┌─────────────────────────┐ │
│ │ Phase 3: Inline Inject │ │
│ │ Read SKILL.md from disk │ │
│ │ Inject into user msg │ │
│ │ Append discoverable │ │
│ │ skills index note │ │
│ └──────────┬──────────────┘ │
└───────────────┼──────────────────────────────────┘
│
▼
LLM receives augmented message with
skill content already in context
(zero extra turns, zero extra tool calls)
```
### System Components
| Component | Purpose | Lines of Code |
|-----------|---------|--------------|
| `config.yaml` — skill disable list | ~185 skills disabled, 2 active | N/A (config) |
| `__init__.py` — plugin hooks | `pre_gateway_dispatch` + `pre_llm_call`, top-3 vector search | ~455 |
| `plugin.yaml` — plugin registration | Hermes plugin metadata | 5 |
| `build_skill_index.py` — index builder | Scans skills → embeds → saves | ~240 |
| `NICKNAME_MAP` — deterministic triggers | 59 entries → 24 skills | (inline in plugin) |
| `entries.json` — vector index | ~190 entries, 384-dim embeddings | ~136KB |
| `index.json` — index metadata | Version, threshold, timestamp | N/A |
---
## 3. Phase 1: Aggressive Skill Trimming
### 3.1 Rationale
In stock Hermes, the system prompt lists all installed skills regardless of use frequency. Our usage audit revealed:
-
**~190**
SKILL.md files on disk
-
**~185**
disabled in `config.yaml`
-
**2**
active (`hermes-agent`, `plan`)
-
**Ratio: ~1%**
active
The two retained skills are meta-tools: `hermes-agent` (for configuring Hermes itself) and `plan` (for structured planning). Everything else — domain knowledge, integrations, image generation, database access — is loaded on-demand via the routing system.
### 3.2 Implementation
```yaml
# config.yaml
skills:
disabled:
- agent-communication-style
- agent-observability
- airtable
# ... all non-essential skills
- youtube-content
```
The disabled list is exhaustive: every skill not actively used in the last 30 days is disabled. New skills are added to this list by default and only moved to the routing index if they prove useful.
### 3.3 Token Savings
All cost figures use DeepSeek V4 Flash blended pricing (78% cache hit at $0.0028/M + 22% cache miss at $0.14/M =
**$0.033/M effective**
). This table shows only the skill enumeration tax — not full conversation cost. See §7.4 for complete model-by-model comparison including GPT-4o, GPT-4.1, and GPT-5.5.
| Metric | Stock (190 active) | Trimmed (2 active) | Savings |
|--------|-------------------|-------------------|---------|
| Skill tokens per turn | ~7,000 | ~80 |
**98.9%**
|
| Cost per turn (blended) | $0.00023 | $0.000003 |
**$0.00023/turn**
|
| Daily savings (50 turns) | — | — |
**~$0.012/day**
|
| Monthly savings | — | — |
**~$0.35/month**
|
| Annual savings (18,250 turns) | — | — |
**~$4.20/year**
|
>
**Note:**
These figures account only for the skill token tax. The eliminated tool-call round-trips (1-2 extra LLM turns per matched query) save an additional ~$0.0009 per match — see §7.4.4 for the full breakdown across all model tiers.
In practice, the system prompt with 2 skills is indistinguishable from a system prompt with 190 skills for almost every query — because the missing skills are injected on-demand when relevant.
---
## 4. Phase 2: Nickname Routing (Deterministic)
### 4.1 Design
Deterministic substring matching runs
**first**
— before any vector search — because it is:
-
**Zero-cost**
: simple Python `in` check
-
**Zero-latency**
: microseconds vs milliseconds
-
**Precise**
: no false positives, no threshold tuning
### 4.2 The Nickname Map
```python
NICKNAME_MAP = {
# Domain-specific project
"the digitization": "digitization-workflow",
"the data api": "data-integration",
# Gaming / D&D
"the game server": "game-ecosystem",
"the dungeon server": "game-ecosystem",
# Infrastructure
"the dns server": "self-hosted-dns",
"the dashboard": "dashboard-tunnel-proxy",
# Email & comms
"the email": "email-automation",
# ... 59 total entries, 24 unique skills
}
```
### 4.3 Design Decisions
**Why definite article prefix?**
Every nickname starts with "the " (e.g., "the game server", "the email"). This was a deliberate choice for three reasons:
1.
**Reduces false positives**
: "the email" is specific; "email" by itself could match chat about email in general
2.
**Natural language flow**
: Users naturally say "check the email" not just "email"
3.
**Single-token matching**
: A simple substring check suffices — no regex, no fuzzy matching
**Why substring matching instead of exact?**
Users vary their phrasing: "check the game server" vs "talk to the game server". Substring `in` handles both without complex NLP.
### 4.4 Example Matches
```
User: "check the game server"
→ match: "the game server" → skill: game-ecosystem
User: "what did the email say"
→ match: "the email" → skill: email-automation
User: "the hermes config needs updating"
→ match: "the hermes config" → skill: hermes-agent (loaded!)
```
---
## 5. Phase 3: Semantic Vector Search
### 5.1 Embedding Pipeline
When no nickname matches, the system falls back to semantic vector search.
**Model:**
`all-MiniLM-L6-v2`
- 384-dimensional embeddings
- Sentence-Transformers library
- ~3ms encode time per query
**Index:**
~190 skill embeddings, stored in:
- `entries.json`: metadata (skill name, file path, description, tags, category)
- `embeddings.npy`: 190 × 384 float32 matrix
- `index.json`: version, timestamp, model name
### 5.2 Build Process
```python
def build_embed_text(skill, skill_body=""):
parts = [skill["name"], skill["description"]]
if skill["tags"]:
parts.append(" ".join(skill["tags"]))
if skill["category"]:
parts.append(skill["category"])
# Extract trigger phrases from ## When / ## Trigger sections
if skill_body:
trigger_text = extract_trigger_sections(skill_body)
if trigger_text:
parts.append(trigger_text)
return " ".join(p for p in parts if p)
```
The embedding text combines:
1. Skill
**name**
(e.g., "data-integration")
2.
**Description**
from frontmatter (e.g., "Query database tables via API")
3.
**Tags**
(e.g., ["CRM", "API", "Enterprise"])
4.
**Trigger phrases**
extracted from the skill body's "When to use" sections
### 5.3 Search Algorithm
```python
def search(query, top_k=3):
query_vec = model.encode(query)
scores = cosine_similarity(query_vec, all_embeddings)
top_indices = argsort(scores)[::-1][:top_k]
results = []
for idx in top_indices:
score = scores[idx]
if score >= entries[idx]["threshold"]: # default: 0.44
results.append((entries[idx], score))
return results # all matches above threshold, up to 3
```
### 5.4 Threshold Tuning
The default threshold of
**0.44**
was empirically determined:
-
**Too low (<0.3)**
: False positives — irrelevant skills loaded
-
**Too high (>0.6)**
: Misses — relevant skills not loaded
-
**0.44**
: Balances precision (noise) vs recall (coverage)
Individual skills can override the threshold in their index entry (`"threshold": 0.44`), allowing manual tuning for ambiguous skills.
### 5.5 Lazy Loading
The model and index are loaded lazily on first match request:
- `_get_model()`: Loads SentenceTransformer on first call (thread-safe, double-checked locking)
- `_load_index()`: Loads entries.json + embeddings.npy on first call; re-loads if file mtime changes
- Both cached globally in the plugin module for the process lifetime
---
## 6. The Gateway Hook Architecture
### 6.1 Hermes Plugin System
Hermes Agent supports plugin hooks at two critical points in the request lifecycle:
```
User Message
│
▼
pre_gateway_dispatch ← Our plugin intercepts here
│ (rewrite message before LLM sees it)
▼
pre_llm_call ← Our plugin adds context here
│ (first LLM turn only)
▼
LLM processes enriched message
```
### 6.2 pre_gateway_dispatch (The Main Hook)
```python
def handle_pre_gateway_dispatch(event, gateway, session_store, **kwargs):
text = event.text
# Skip trivial messages
if len(text.strip()) < 3 or text.startswith("/"): return
# Phase 1: Nickname match (deterministic, single skill)
skill = check_nickname(text)
# Phase 2: Vector search fallback (semantic, up to 3 skills)
if not skill:
results = vector_search(text, top_k=3)
if results:
# Inject ALL matched skills above threshold
contents = []
for entry, score in results:
content = read_skill_file_from_index(entry)
contents.append(content)
skill_body = "\n\n".join(contents)
if not skill and not contents:
return # Passthrough — no skill needed
# Phase 3: Load and inject skill content inline
rewritten = f"""{text}
{skill_body}
📋 [Plugin] ~190 skills available (use skill_view)
"""
return {"action": "rewrite", "text": rewritten}
```
### 6.3 Why Direct File Loading?
The critical insight:
**disabled skills cannot be loaded via `/skill-name` rewrite**
because the Hermes gateway's `get_skill_commands()` excludes disabled skills. Our plugin bypasses this entirely by:
1. Storing the
**absolute file path**
in the vector index entry (`skill_file`)
2. Reading the SKILL.md directly from disk with `Path.read_text()`
3. Injecting the raw content inline in the user message
This means the skill appears to the LLM as if it was in the system prompt all along — but only when relevant.
### 6.4 pre_llm_call (The Assistant Hook)
```python
def handle_pre_llm_call(**kwargs):
# On matched turns, inject a compact skills reference
return {"context": "[Plugin: ~190 skills — data-integration, game-ecosystem, ... +180 more]"}
```
This helps the LLM understand what's available without scanning the full `<available_skills>` block.
### 6.5 Injection Guard
To prevent re-injection during tool-loop turns (where the same message text cycles through multiple LLM calls), the plugin tracks `_last_injected_text`:
```python
if _last_injected_text == note:
return None # Already injected this turn
```
---
## 7. Performance Impact
### 7.1 Token Savings
| Scenario | Stock Hermes | Our System | Savings |
|----------|-------------|------------|---------|
| Passthrough query | 7,000 skill tokens | 80 skill tokens |
**98.9%**
|
| Nickname match | 7,000 + 2 turns | 80 + inline |
**~99% + 2 fewer turns**
|
| Vector match | 7,000 + 2 turns | 80 + inline |
**~99% + 2 fewer turns**
|
### 7.2 Latency
| Operation | Latency | Notes |
|-----------|---------|-------|
| Nickname match | ~0.001ms | Simple substring check |
| Vector encode | ~3ms | all-MiniLM-L6-v2 on CPU |
| Cosine similarity | ~0.01ms | NumPy batched dot product |
| File read | ~0.1ms | SKILL.md from SSD |
|
**Total (vector path)**
|
**~3.1ms**
| Before LLM invocation |
|
**Total (nickname path)**
|
**~0.1ms**
| Essentially zero |
### 7.3 Production Statistics
| Metric | Value |
|--------|-------|
| Skills indexed | ~190 |
| Nickname entries | 59 (mapping to 24 unique skills) |
| Vector index dims | 384 |
| Default threshold | 0.44 |
| Active skills | 2 (~1% of total) |
| Plugin code | ~455 lines |
| Total system files | 4 (plugin.py, plugin.yaml, build_index.py, config.yaml) |
### 7.4 Cost Impact — Model Comparison
The following tables compute real costs using July 2026 API pricing. All calculations assume:
-
**Without routing:**
~7,000 skill tokens + ~2,500 conversation tokens = ~9,500 input + ~500 output per turn
-
**With routing:**
~2,000 input + ~500 output per turn (skill enumeration tax eliminated)
-
**50 turns/day**
,
**30 days/month**
(1,500 turns/month)
-
**DeepSeek V4 Flash**
uses a blended input rate: 78% cache hits at $0.0028/M + 22% cache misses at $0.14/M =
**$0.033/M effective**
#### §7.4.1 Per-Turn Cost
| Provider | Model | No Routing | With Routing | Savings/Turn | Savings % |
|----------|-------|-----------|-------------|-------------|----------|
|
**DeepSeek**
| V4 Flash | $0.000454 | $0.000206 |
**$0.000248**
|
**54.6%**
|
|
**DeepSeek**
| V4 Pro | $0.001260 | $0.000595 |
**$0.000665**
|
**52.8%**
|
|
**OpenAI**
| GPT-4.1-nano | $0.001150 | $0.000400 |
**$0.000750**
|
**65.2%**
|
|
**OpenAI**
| GPT-4o-mini | $0.001725 | $0.000600 |
**$0.001125**
|
**65.2%**
|
|
**OpenAI**
| GPT-4.1-mini | $0.004600 | $0.001600 |
**$0.003000**
|
**65.2%**
|
|
**OpenAI**
| GPT-4.1 | $0.023000 | $0.008000 |
**$0.015000**
|
**65.2%**
|
|
**OpenAI**
| GPT-4o | $0.028750 | $0.010000 |
**$0.018750**
|
**65.2%**
|
|
**OpenAI**
| GPT-5 (est.) | $0.016875 | $0.005833 |
**$0.011042**
|
**65.4%**
|
|
**OpenAI**
| GPT-5.5 | $0.067250 | $0.023333 |
**$0.043917**
|
**65.3%**
|
The routing architecture saves a higher
**percentage**
on OpenAI models (65%) vs DeepSeek (53-55%) because cheaper models have a lower per-token cost — making the fixed absolute token savings (7,000 input tokens/turn) a larger fraction of a more expensive base.
#### §7.4.2 Daily & Monthly Cost (50 turns/day)
| Model | Daily (No Routing) | Daily (With Routing) | Daily Savings | Monthly Savings |
|-------|-------------------|---------------------|--------------|----------------|
|
**DeepSeek V4 Flash**
| $0.0227 | $0.0103 |
**$0.0124**
|
**$0.37**
|
|
**DeepSeek V4 Pro**
| $0.0630 | $0.0298 |
**$0.0332**
|
**$1.00**
|
|
**GPT-4.1-nano**
| $0.0575 | $0.0200 |
**$0.0375**
|
**$1.13**
|
|
**GPT-4o-mini**
| $0.0863 | $0.0300 |
**$0.0563**
|
**$1.69**
|
|
**GPT-4.1-mini**
| $0.2300 | $0.0800 |
**$0.1500**
|
**$4.50**
|
|
**GPT-4.1**
| $1.150 | $0.400 |
**$0.750**
|
**$22.50**
|
|
**GPT-4o**
| $1.438 | $0.500 |
**$0.938**
|
**$28.13**
|
|
**GPT-5 (est.)**
| $0.844 | $0.292 |
**$0.552**
|
**$16.56**
|
|
**GPT-5.5**
| $3.363 | $1.167 |
**$2.196**
|
**$65.88**
|
#### §7.4.3 Annual Projection (18,250 turns/year)
| Model | No Routing | With Routing | Annual Savings |
|-------|-----------|-------------|---------------|
| DeepSeek V4 Flash |
**$8.28**
|
**$3.76**
|
**$4.52**
|
| DeepSeek V4 Pro |
**$23.00**
|
**$10.86**
|
**$12.14**
|
| GPT-4.1-nano |
**$20.99**
|
**$7.30**
|
**$13.69**
|
| GPT-4o-mini |
**$31.49**
|
**$10.95**
|
**$20.54**
|
| GPT-4.1-mini |
**$83.95**
|
**$29.20**
|
**$54.75**
|
| GPT-4.1 |
**$419.75**
|
**$146.00**
|
**$273.75**
|
| GPT-4o |
**$524.69**
|
**$182.50**
|
**$342.19**
|
| GPT-5.5 |
**$1,227.31**
|
**$425.83**
|
**$801.48**
|
#### §7.4.4 Plus Eliminated Tool-Call Rounds
Each matched query eliminates ~1-2 extra LLM turns (the "let me check" → skill_view → respond cycle). At our observed ~5-10 matches/day:
| Model | Extra cost saved per match | ~5 matches/day | ~10 matches/day |
|-------|---------------------------|----------------|-----------------|
| DeepSeek V4 Flash | $0.0009 | $0.0045/day | $0.009/day |
| GPT-4o-mini | $0.0035 | $0.0175/day | $0.035/day |
| GPT-4.1-mini | $0.0092 | $0.046/day | $0.092/day |
| GPT-4o | $0.0575 | $0.288/day | $0.575/day |
| GPT-5.5 | $0.1345 | $0.673/day | $1.345/day |
#### §7.4.5 The Counterintuitive Finding
**This architecture matters
*more*
on expensive models than cheap ones.**
A GPT-5.5 deployment without routing burns
**$1,227/year**
on idle skill enumeration tax alone — effectively paying $3.36/day just to say "hello." With routing, that drops to $426/year. The absolute dollar savings scale linearly with model cost per token, making this optimization most valuable precisely where token budgets are tightest.
For DeepSeek V4 Flash users, the savings are modest (~$4.52/year — roughly 2 cups of coffee). But the
**latency benefit**
and
**eliminated round-trips**
apply identically regardless of model: the first response to "check the database" arrives informed, not "let me look that up."
---
## 8. Replication Guide
### 8.1 Prerequisites
- Hermes Agent (or any LLM agent framework with plugin/gateway hooks)
- Python 3.10+
- `sentence-transformers` + `numpy` (for vector search)
- Skills directory with SKILL.md files
### 8.2 Step 1: Audit & Trim
```bash
# Count total skills
hermes skills list | grep -c enabled
# Disable aggressively in config.yaml
skills:
disabled:
- skill-you-never-use
# ... all but 2-3 essentials
```
### 8.3 Step 2: Build the Index
```python
# build_skill_index.py — scans skills directory, creates embeddings
for skill_md in skills_dir.rglob("SKILL.md"):
parse_frontmatter(skill_md) # name, description, tags, category
embed_text = f"{name} {description} {' '.join(tags)} {category}"
model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = model.encode(all_embed_texts)
np.save("embeddings.npy", embeddings)
```
### 8.4 Step 3: Create Nickname Map
```python
NICKNAME_MAP = {
"the project": "project-anatomy",
"the database": "database-schema",
# ... map common user phrases to skill names
}
```
### 8.5 Step 4: Implement Gateway Hook
```python
def pre_gateway_dispatch(event):
text = event.text
# Try nickname first (cheap, deterministic)
skill = NICKNAME_MAP.get(text)
# Fall back to vector search (semantic, higher cost)
if not skill:
query_vec = model.encode(text)
scores = cosine_similarity(query_vec, index_embeddings)
if max(scores) >= 0.44:
skill = entries[argmax(scores)]["skill_name"]
if not skill:
return # passthrough
# Read skill content from DISK (not from framework's skill loader)
content = Path(entries[skill]["skill_file"]).read_text()
# Inject inline
return {"action": "rewrite", "text": f"{text}\n\n{AUTO_LOAD_MARKER}{content}{AUTO_LOAD_END}"}
```
### 8.6 Key Architecture Decision
**Always read SKILL.md from disk directly.**
Do not use the framework's built-in skill loader if it requires skills to be "enabled" or listed in an active registry. The whole point is to keep 98% of skills disabled in the system prompt while still being able to load them on demand.
---
## 9. Lessons Learned
### 9.1 What Worked
-
**Nickname-first routing**
is the most impactful optimization. ~70% of skill matches come from the nickname map, which costs ~0.001ms. Vector search is the safety net, not the primary path.
-
**Definite article prefix ("the X")**
eliminated false positives. Users naturally refer to "the email" or "the game server" when they want a specific skill invoked.
-
**Direct file reading bypasses framework restrictions.**
Disabled skills are invisible to the agent's skill loader, but they're perfectly readable on disk. This is the key architectural insight.
-
**Multi-skill vector retrieval (top-3)**
handles compound queries like "check the game server and the email" — all matched skills above threshold are loaded inline, so the LLM sees both domains in a single turn.
-
**Lazy loading**
keeps cold-start costs zero. The embedding model (90MB) only loads when the first semantic match is needed.
### 9.2 What to Watch For
-
**Skill content size**
: Some SKILL.md files exceed 15,000 chars. We truncate at this limit to prevent token blowout in the user message.
-
**Index staleness**
: When new skills are added, the index must be rebuilt. A cron job handles this automatically.
-
**Thread safety**
: The plugin serves multiple concurrent sessions. All global state (model, index, injection guard) uses thread-safe locking.
-
**Threshold tuning**
: 0.44 was right for our skill corpus but may need adjustment for different domains.
### 9.3 What Could Be Improved
-
**Skill content caching**
: SKILL.md content is read from disk on every match. Adding an in-memory LRU cache would reduce latency.
-
**Cross-profile index sharing**
: If multiple Hermes profiles have different skill sets, a shared or merged index could reduce duplication.
-
**Online learning**
: The nickname map is hand-crafted. A feedback loop could automatically add recently-used skills as nickname triggers.
---
## 10. Conclusion
The Skill Routing Architecture demonstrates that LLM agents do not need to carry their full capability catalog in the system prompt. By combining aggressive trimming (~1% active), deterministic nickname routing (59 triggers), and semantic vector search (190-dim embeddings, 0.44 threshold), we reduced per-turn skill tokens by
**98.9%**
while preserving — and in some cases improving — the agent's ability to load domain-specific knowledge.
The system has been in production for 2+ weeks, processing ~5-10 skill-matched queries per day across multiple platforms, with zero regressions in passthrough query handling. The total implementation is
**~700 lines of code**
across 4 files, making it a low-cost, high-impact optimization applicable to any LLM agent framework with gateway-level interception hooks.
The key architectural insight — load disabled skill content directly from disk rather than through the framework's skill loader — solves a fundamental tension in agent system design between prompt efficiency and capability breadth.
---
## Appendix A: Example Nickname Map (Sanitized)
```
the digitization workflow → digitization-workflow
the data api → data-integration
the game server → game-ecosystem
the dungeon server → game-ecosystem
the dnd server → game-ecosystem
the dnd game → game-ecosystem
the github pr → github-pr-workflow
the pull request → github-pr-workflow
create a pr → github-pr-workflow
the code review → github-code-review
review the pr → github-code-review
the dns server → self-hosted-dns
the dashboard → dashboard-tunnel-proxy
the bitcoin dashboard → dashboard-tunnel-proxy
the auto-remediation → auto-remediation
the excel file → xlsx
the spreadsheet → xlsx
the powerpoint → powerpoint
the pdf file → pdf
the word document → docx
the hermes config → hermes-agent
the hermes setup → hermes-agent
the model config → hermes-model-strategy
the cost optimize → hermes-cost-optimization
the notes → obsidian
my notes → obsidian
the vault → obsidian
the memory search → hybrid-memory-search
the vector search → hybrid-memory-search
the email → email-automation
my email → email-automation
the expense → expense-monitoring
the budget → budget-tracking
```
**33 entries → 20 unique skills**
---
## Appendix B: Skills by Category (Example)
| Category | Count | Examples |
|----------|-------|---------|
| General | ~148 | apple-notes, findmy, image-generation, ... |
| Productivity | ~12 | linear, notion, obsidian, spotify, ... |
| Creative | ~7 | ascii-art, video-generation, p5js, ... |
| Infrastructure | ~4 | cloudflare-hosting, dns, system-admin, ... |
| Software Engineering | ~4 | clean-architecture, testing, ... |
| Research | ~3 | arxiv, geo-analysis, ... |
| DevOps | ~2 | search-engine, tunnel-proxy |
| Agent Meta | ~2 | hermes-agent, agent-fallback |
| Data Science | ~2 | fine-tuning, training |
| Other | ~6 | desktop, memory, note-taking, ... |
---
## Appendix C: Index Metadata
```json
{
"version": 2,
"model": "all-MiniLM-L6-v2",
"embed_dim": 384,
"entry_count": 190,
"default_threshold": 0.44
}
```
---
## Appendix D: Related Work
This architecture addresses a gap in LLM agent system design: the tension between
**prompt efficiency**
and
**capability breadth**
. Related approaches include:
-
**Mixture of Experts (MoE)**
: Routing tokens to specialized sub-networks at inference time — analogous at the model level to what we do at the prompt level
-
**Retrieval-Augmented Generation (RAG)**
: Injecting relevant documents into context on-demand — our approach is RAG for
*capabilities*
rather than
*knowledge*
-
**Tool-Use Agents**
(OpenAI Function Calling, Anthropic Tool Use): The model declares which tool to use, but the tool catalog is typically in the system prompt
Our contribution is a pre-LLM routing layer that makes the capability catalog effectively non-existent in the prompt while still being fully accessible.
https://www.youtube.com/watch?v=Hq9CwopUH1s
This is a fever dream of a digital age—a sprawling, surrealist tapestry where crochet patterns meet high-speed police chases, and the Grim Reaper is just another guy trying to manage an office.
Here is the story of The Day the Grid Glitched.
The day began in a flurry of #ModernCrochet and chaos. At the local craft guild, a "Magical Crochet Gargoyle Quitter" was throwing in the towel, leaving behind a trail of freeform stars and loose ends. Meanwhile, in the corporate underworld, the Grim Reaper CEO adjusted his tie at his administrative desk. "It’s my panel," he hissed to a room of trembling interns, "and you will respect the filing system."
Outside, the physical world was losing its grip on reality. A green avocado was spotted zip-lining across the forest canopy, screaming "Wee!" while a dairy cow and a buffalo experienced love at first sight on the prairie. It was beautiful, until the "Authorized Official Shambulance" sped by with off-key sirens, driven by a DoorDash driver who had clearly taken a wrong turn at the interstate.
By noon, the legal system was buckling. A divorce attorney was arguing for a 4% inflation alimony increase, while a stubborn police officer attempted to ticket a driver for doing 55 mph, despite the driver’s GPS claiming otherwise.
In the skies, things weren't much better. An airplane pilot abandoned the cockpit—not because of an emergency, but to fix a sewer leak in the bathroom. This left the County Sheriff’s helicopter pilot to pick up the slack, shouting the command code "Accel UR L8 twerk" into his AI navigator.
Back on the ground, the "Microwave Preventive Maintenance Team" (composed entirely of trolls in tin foil hats) was conducting a rigorous inspection of a breakroom, while nearby, a police officer and his best friend were formally interrogating a hairball.
As the sun set, a tarnished copper green armadillo drone took flight from the roof of a skyscraper, filming the most anticipated event of the year: the wedding of the Grim Reaper and the Reapress. They exchanged vows of "invisible threat control" while a dancing xenophobic xylophone provided the music.
However, the party was crashed by a Giant Plushy Octopus looking for hugs and a "Slip and Fall Droidbot" seeking a lawsuit settlement. In the corner, Holly Bitcoin gave tap dance lessons to a singing pickle, while a group of mushroom soldiers set up tents inside a refrigerator’s cooling system to escape the heat.
As the clock struck midnight on March 8, 2026, the digital world began to "URL barf." Google Meet was exposing phone numbers, and the #DreamTrackAI was telling everyone to "try again tomorrow."
The chaos finally settled into a quiet, bizarre peace. A Jack Rabbit and a Skunk shared a moment in the woods, and a single claymation blue bird tucked its family into a nest made of crochet scrap yarn. The world was a mess of unredacted reports, stuck cruise controls, and singing rainbows—but as the Star and Hammer cartoon faded to black, one thing was clear: nobody was above the law, but everyone was definitely above the limit for weirdness.
The scene opens on a high-stakes, high-altitude pursuit. Jack, a lanky cartoon cat with whiskers that twitch like radio antennas, is dangling from a vine. Hammer, a stout mouse wearing a tiny tool belt, is standing on Jack’s head, squinting through a pair of brass binoculars.
"Target sighted," Hammer squeaked, pointing a tiny paw toward the horizon. "And he’s moving fast."
Zip-lining across the triple-canopy rainforest at a reckless velocity was Avocado. He wasn't just a fruit; he was a daredevil with a pit of pure adrenaline. Wearing tiny goggles and a GoPro strapped to his pebbly green skin, Avocado let out a high-pitched, "WEEEEEEEE!" as he blurred past a troop of confused howler monkeys.
Jack didn't have a zip-line, but he did have cartoon physics. He grabbed a passing toucan by the feet, and with a rhythmic "Bang! Bang!" of Hammer’s literal mallet against a hollow log to create a beat, they swung into the sky.
"We need to intercept him before he hits the Cloud Forest!" Hammer shouted over the wind. "If he zips into the 'Modern Crochet' sector, we'll lose him in the yarn fog!"
The Obstacles:
Avocado was gaining speed, his pit humming like a drone motor. He was headed straight for a transitional train scene—a steam engine locomotive puffing purple smoke across a trestle bridge.
"Now, Jack! Use the yarn smudging technique!" Hammer commanded.
Jack pulled a scrap of neon-pink crochet string from his pocket. With the grace of a Vaudeville performer defying gravity, he whipped the string forward. It looped around Avocado’s stem just as the fruit was about to clear the canopy.
ZIP—THWIP—BOING!
The tension on the crochet line acted like a bungee cord. Avocado didn't crash; he performed a perfect, mid-air 360-degree flip, his tiny goggles fogging up with joy.
They all tumbled onto a soft mossy log near a Box Turtle who was busy falling in love with an Armadillo. Avocado bounced twice, his GoPro still recording the whole "Brainrot" investigation.
"Why were you running?" Jack panted, his fur standing up in static-electrified tufts.
Avocado adjusted his goggles and looked at them with wide, watery eyes. "Running? I wasn't running. I was just trying to get to the Holly Bitcoin Tap Dance Lessons before the early bird special ended."
Hammer looked at Jack. Jack looked at the Armadillo.
"Well," Hammer sighed, putting his mallet back in his belt. "At least we got the footage. This is going to kill on #DreamTrackAI."
| VLq-x3ou19E | untitled |
|---|---|
| 0TMbRWk4WjI | The making of Jack and Hammer, cartoon cat and mouse, bang |
| 27LTuniln4k | untitled |
| r384VZLXu3A | It's go time with Grandma and hot grits in the ring |
| Ttbtm4D_gWo | pole vault remix |
| hxicWioIX38 | How to take the loose end of crochet stitch, put it over the main strand, fold loop |
| _Tn8GDwQxAU | Police officer can't stop car due to cruise control being stuck #crazycars #carwars |
| HIXe_nKGXwA | Untitled Help I can't put my foot in a shoe without a hole in the shoe |
| SbURuV-AfKw | Untitled Magical crochet gargoyle quitter I Quit doodle art animation |
| wXRoZkjgg1Y | pingrok Being hold off in an ambulance |
| Ws8yubqGV9k | Join the National Guard. |
| Y42d9L-TPl4 | Untitled You U Ewe and I, eye aye have gone on a date a couple of times haven't we? |
| UK9nCeVtutU | Untitled door dash ambulance Authorized official shambulance services |
| s0zhJiIvVI4 | How to skip one crochet, stitch and count to 3 #moderncrochet #beginners |
| 6rfXhtSP5xk | Cuddlefish jellyfish love at first sight in the ocean, prairie of plankton. |
| LGU4J83SAxs | Green avocado goes zip lining A top the forest canopy Wee |
| arAPCXGIWCs | Untitled Hapoy Jake Patrick Wesley Day |
| jS4zJkPKeY8 | Untitled I love ❤️ you Singing pickle |
| qzs14JC3VUY | Holly Bitcoin Tap Dance Lessons |
| PzLApGgcrDw | Unlisted cat Video Bob chicken Crane |
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| tfiQpdf9xuE | untitled |
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This is a lengthy thread so a couple things to start out; 1) I won’t be sharing the pictures that the scammer used, as that actual person is a victim as well. 2) I won’t be using my loved ones name, instead I’ll use the alias of John
This whole situation started in spring of 2018. John had a devastating life event occur and needed companionship. Naturally he got on a dating app and met someone and started chatting. John was a prime target for Pig Butching scammers as he was nearing retirement age, lived alone, vulnerable, and lonely searching for a connection.
In the beginning it was a lot of just chatting and getting to know each other. After some time it turned into little issues like ‘can I borrow gas money’ or ‘Can you help with a car repair bill’. After those tricks successfully worked, it started into the main plot.
When John insisted that they meet in person, the scammer stated that they were leaving the state to peruse modeling and joining a Modeling Training Program. This is when the massive amount of money started flowing out. The scammer convinced John to be their ‘sponsor’ for their modeling program and in return, after they made it out of the program John would get paid $168,000 every week in royalties from their modeling career.
Here is more context to the scam itself: The scammer had John send him all his personal information, address, phone number, Bank account information and SS#. On top of this the scammer had John sign a contract that promised to be their sponsor all the way through no matter what. Once in the Modeling program, the scammer requested money for anything and everything you could think of; housing cost, food, modeling regalia, transportation costs to events, etc. Of course, the modeling agency only accepted money via bitcoin or gift cards.
After two full years of the scam being in full tilt with $250,000+ out the window, John had cashed out all of his retirement accounts, sold a lot of possessions, started borrowing against future paychecks and was in imminent danger of getting his car repo’d and evicted. He had no money left and John came to me asking for a loan for an ‘investment opportunity’. Being family, this wasn’t an issue, but since it was a very large amount of $48,000 I wanted a lot of details. He started explaining the whole thing and I immediately told him he was being scammed. I won’t drone on and on, but it took me about a full year to finally convince him that everything may not be as legit as it seems, but he never fully believed he was being scammed.
I initially told John that I wanted to prove to him that he was being scammed and I asked for the pictures the scammer was using, I asked for the phone number(s), Venmo/Cashapp accounts, and for the bitcoin wallets. With the help of watching YouTuber’s like Kitboga, PleasantGreen, and Jim Browning I was able to try and show John how he was being scammed.
What I did: With John’s help, I was able to convince the scammer to click a link and it captured his location in Lagos, Nigeria. After a lot of effort of sending emails and calling, I was able contact the actual Model Management company and setup a call with John, myself and one of their directors. I had previously provided them with as much info about the scam as I could so when the time of the call came they were really helpful with showing John everything that was inaccurate that the scammer was saying. I was able to reverse image lookup the pictures that the scammer sent to John to real him in and I showed that the images were stolen and who they were stolen from.
With John’s approval, I was able to gain access too and actively monitor John’s conversation with the scammer so I could use the information in real time to head things off before money was sent.
Anyways, if you’ve made it this far, Im happy to share that after years of helping deal with this situation and lots of death threats John is in a much better place mentally and very slowly digging out of his hole financially. It’s been two full years since any money has been sent and a little over a year since he last heard from the scammer. AMA