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BF skill-pilot

Adaptive skill scheduling engine with environment-aware routing, user preferences, and self-learning optimization. Use when: (1) auto-select best skill for task, (2) need environment-aware configuration, (3) want to learn from execution history, (4) need observability and performance insights.

ClawHub Agent Skills author: Mr-ChenXY v0.4.6 MIT-0 38 files body ≈ 2 246 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 35/100 · Will not run — References files that are not bundled: references/environment-probe.md, references/strategy-guide.md

ProcedureInfrastructuretype 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
88/100
safety, quality, tests
Safety 60%
99
Quality 40%
71
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: references/environment-probe.md, references/strategy-guide.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 Dangerous commands cmd-eval-dynamic scripts/test.py:28
    Dynamic code execution from decoded/untrusted input (test fixture / example file)
    os.system(f"python3 {skill_dir}/scripts/validate.py {skill_dir}")
    fixture

Files scanned: 38. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/environment-probe.md
  • warning missing-ref reference to a missing file: references/strategy-guide.md
  • note frontmatter-key unknown frontmatter key "required_env"
  • note frontmatter-key unknown frontmatter key "optional_env"
  • note frontmatter-key unknown frontmatter key "security_notes"

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: references/environment-probe.md, references/strategy-guide.md
  • 0Tools and files. 2 referenced file(s) missing: references/environment-probe.md, references/strategy-guide.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
  • 100Steps. 53 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2246 tokens
  • 100Running it twice. No mutating operations
  • 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

  • +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
  • -226 emoji in the instructions: noise for the model
  • -310 of 14 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 297: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 53 items
  • +4Has examples (21 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
SkillPilot is a coherent skill scheduler, but it needs review because it can run other installed skills with inherited environment variables and persist sensitive request history locally.
LLM: suspicious (high) · VirusTotal: · 29 May 2026