SKILLEMALL.ai

BF module-analyzer-generate-doc

Java/Maven single-module deep documentation generator. Generates L3(file-level) to L2(module-level) business logic docs for specified module. Supports multi-subagent parallel processing, context compression, checkpoint resume, and auto-retry.

ClawHub Agent Skills author: endcy v1.0.5 MIT-0 12 files body ≈ 3 357 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process F 40/100 · Will not run — References files that are not bundled: references/context-compression.md, references/task-monitoring.md, references/retry-mechanism.md

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
99
Quality 40%
64
Run on models
none yet
Process rating
F
40/100
Will not run
References files that are not bundled: references/context-compression.md, references/task-monitoring.md, references/retry-mechanism.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. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. 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-execpolicy-bypass package.json:19
    Runs PowerShell with execution policy bypassed (quoted — discussed, not commanded)
    "generate": "powershell -ExecutionPolicy Bypass -File scripts/gene…ps1",
    quoted

Files scanned: 12. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: references/context-compression.md
  • warning missing-ref reference to a missing file: references/task-monitoring.md
  • warning missing-ref reference to a missing file: references/retry-mechanism.md
  • warning missing-ref reference to a missing file: references/secondary-scan.md

Process rating: all ten parameters 40/100

Will not run. References files that are not bundled: references/context-compression.md, references/task-monitoring.md, references/retry-mechanism.md
  • 0Tools and files. 4 referenced file(s) missing: references/context-compression.md, references/task-monitoring.md, references/retry-mechanism.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 63 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3357 tokens
  • 100Running it twice. No mutating operations
  • low 22 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 242: enough signal without eating the budget
  • +4Structure: 45 headings
  • +3Step-by-step instructions: 63 items
  • +4Has examples (21 code blocks)
  • +4Reference files are cited in the instructions (2 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: clean
This skill is a disclosed Java module documentation generator that reads project source and writes local documentation files, with no evidence of exfiltration or destructive behavior.
LLM: benign (high) · VirusTotal: · 16 Jul 2026