SKILLEMALL.ai
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Market intelligence API for AI agents. Macro regime detection, risk scoring, trading signals (IGNITION/ACCUMULATION/DISTRIBUTION/CAPITULATION), whale alerts, funding arbitrage, orderbook analytics, 29 technical indicators, RSI screening (606 coins), breaking news with FinBERT sentiment, economic calendar, treasury tracking, and Polymarket odds. 33 endpoints, 1181 assets, 250+ sources. Use when an agent needs macro regime context, risk assessment, position sizing guidance, market structure data, whale activity monitoring, or news sentiment. API-only — no local execution, no filesystem access, no secrets in prompt.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 1 523 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Market intelligence API for AI agents.

As a process F 33/100 · Will not run — References files that are not bundled: references/endpoints.md

IntegrationTelegramAI and agentstype 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
B
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
73
Run on models
none yet
Process rating
F
33/100
Will not run
References files that are not bundled: references/endpoints.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token endpoints.md:740
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "wallet_address": "34xp…seo",
    quoted

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/endpoints.md

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: references/endpoints.md
  • 0Tools and files. 1 referenced file(s) missing: references/endpoints.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 85Steps. 9 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1523 tokens
  • 100Running it twice. No mutating operations
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 620: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 9 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.