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BB project_analyzer

Analyze any project directory and produce a detailed report covering what the project does, its tech stack, folder structure, entry points, how to run it, and where to start reading.

ClawHub Agent Skills author: Gavriel Donovan v1.0.0 MIT-0 4 files body ≈ 614 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 67/100 · Nearly there — weak spots: inputs and preconditions, consistency

AnalyzerData and analyticsInfrastructuretype 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%
100
Quality 40%
69
Run on models
none yet
Process rating
B
67/100
Nearly there
Inputs and preconditions w 11
30
Consistency w 8
40
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 67/100

  • 30Inputs and preconditions. Does not say what the process needs to start
  • 40Consistency. Frontmatter name (project_analyzer) differs from the folder (pal)
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 60Failures and branches. 2 branches
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 23 steps
  • 100Execution cost. Instruction body is 614 tokens
  • 100Running it twice. No mutating operations
  • 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 182: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 23 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: clean
This appears to be a project-understanding skill that reads local code as part of its stated purpose, with some privacy cautions but no evidence of hidden, destructive, persistent, or exfiltrating behavior.
LLM: benign (medium) · VirusTotal: · 29 May 2026