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CD secrets-scan

Detect hardcoded secrets (API keys, tokens, passwords) in text or code

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 7 files body ≈ 193 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Detect hardcoded secrets (API keys, tokens, passwords) in text or code

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches

IntegrationInfrastructureAI 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
C
71/100
safety, quality, tests
Safety 60%
74
Quality 40%
66
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Secrets in code
If you install

The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. 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 · 5

  • high Secrets in code secret-private-key README.md:56
    Private key material (key header without key body)
    | `private_key` | -----BEGIN PRIVATE KEY----- …
    header only
Medium and low: 4
  • medium Secrets in code secret-labelled-token README.md:67
    Labelled token / key literal (vendor format unknown — verify it is not a live credential)
    apiKey: "sk-p…789",
  • low Exfiltration read-dotenv pipeline-cli.yaml:8
    Reads a .env file (code comment)
    #   cat .env.example | expanso-edge run pipeline-cli.yaml
    comment
  • low Secrets in code secret-password-literal README.md:67
    Hard-coded password / key literal (may be an example)
    apiKey: "sk-p…789",
  • low Secrets in code secret-password-literal README.md:69
    Hard-coded password / key literal (may be an example)
    password: "supe…123!"

Files scanned: 5. 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")

Process rating: all ten parameters 46/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (secrets-scan) differs from the folder (expanso-secrets-scan)
  • 50Steps. 2 steps
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Execution cost. Instruction body is 193 tokens

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)
  • +3Description length 70: 120–800 characters recommended
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 7 headings
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.