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AC fomo-research

Smart money research via Fomo social graph. Track top traders, monitor live trades, build watchlists — all from your agent. Powered by fomo.family, built by cope.capital. Use when: (1) user asks about smart money, whale wallets, or top traders, (2) user wants to track specific Fomo handles or crypto traders, (3) user asks "what are the best traders buying", "who's profitable on fomo", (4) user wants real-time trade alerts or wallet monitoring, (5) user says "fomo research", "check fomo", "smart money", "wallet tracker". NOT for: executing trades, managing funds, or anything requiring private keys.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 4 files body ≈ 4 181 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Smart money research via Fomo social graph.

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
98
Quality 40%
94
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token references/api.md:24
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "wallet": "A5SE…ZSk",
      quoted
    • low Secrets in code secret-high-entropy-token references/api.md:27
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "token_mint": "DezX…ump",
      quoted

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "primaryEnv"
    • note frontmatter-key unknown frontmatter key "env"

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 7 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4181 tokens
    • 85Steps. 50 steps, 1 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 604: enough signal without eating the budget
    • +4Structure: 41 headings
    • +3Step-by-step instructions: 50 items
    • +4Has examples (21 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.