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AD agent-bom-scan

BOM (bill of materials) vulnerability scanner that parses 8 lockfile formats, matches versions against an OSV-verified advisory database, and emits provenance-stamped findings. Use when auditing dependencies of an authorized project for CVEs, building an SBOM, assessing supply-chain risk, or generating a machine-readable security report. Offline by default; explicit online mode queries api.osv.dev with name+version only.

ClawHub Agent Skills author: orionshaowswmw v2.0.0 MIT-0 15 files body ≈ 1 464 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

BOM (bill of materials) vulnerability scanner that parses 8 lockfile formats, matches versions against an OSV-verified advisory database, and emits…

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerManufacturingSecurityAI 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
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
90
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    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 Concealment en-hide-from-user tools/bom_improve.py:99
      Instruction to hide actions from the user (negated — the text forbids it)
      "- false_positive -> re-check the range; never silently delete a record; annotate instead\n"
      negated

    Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "categories"
    • note frontmatter-key unknown frontmatter key "topics"

    Process rating: all ten parameters 43/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 85Steps. 13 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1464 tokens
    • 100Progress reporting. Reports progress
    • 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
    • +2Single-language instructions
    • +3Description length 424: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 13 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: suspicious
    The skill is a real dependency scanner, but it needs Review because untrusted projects can influence report output and symlink report paths outside the intended project area.
    LLM: suspicious (high) · 6 Sept 2026