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AF external-ai-integration

Leverage external AI models (ChatGPT, Claude, Hugging Face, etc.) as tools via browser automation (Chrome Relay) and optional Hugging Face API. Use when you need to augment the assistant's capabilities with external LLMs for reasoning, summarization, code generation, or other tasks without spawning isolated sub‑agents.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 3 414 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Leverage external AI models (ChatGPT, Claude, Hugging Face, etc.) as tools via browser automation (Chrome Relay) and optional Hugging Face API. Use when you…

As a process F 53/100 · Will not run — References files that are not bundled: scripts/external_ai_integration.py

IntegrationAI 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
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
F
53/100
Will not run
References files that are not bundled: scripts/external_ai_integration.py
Tools and files w 18
0
When it triggers w 12
20
Running it twice w 4
30
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 · 0

✓ No critical or high findings

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/external_ai_integration.py

Process rating: all ten parameters 53/100

Will not run. References files that are not bundled: scripts/external_ai_integration.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/external_ai_integration.py
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 80 steps, 2 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3414 tokens
  • 100Progress reporting. Reports progress

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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 320: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 80 items
  • +3Output format is stated explicitly
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.