Secrets-Exposure Review

SkillDev tools

Lets your agent analyze where secrets may be exposed across code, logs, and configs and produce an evidence-based review draft.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Secrets-Exposure Review skill

About this capability

Use this skill when you need evidence-bounded secrets-exposure-review analysis and validation preparation; triggers include 敏感信息暴露审查 and secrets-exposure-review.

What this skill tells your AI

The instructions your AI receives, as published by naodeng/awesome-qa-skills in skills/en/testing-types/secrets-exposure-review/SKILL.md and read by ahel’s review.

When to Use

  • Use this Skill when the work needs evidence-bounded analysis of secret locations, flows, log exposure, configuration boundaries, and rotation evidence.
  • Use it when input is incomplete but a reviewable draft with assumptions and gaps is still useful.
  • Use it when static security evidence must remain separate from planned validation and completed execution.

Output Format Options

  • Default to Markdown organized by security risk, evidence, and priority.
  • If the user asks for a table, CSV, JSON, or ticket format, preserve the same finding fields and evidence states.
  • Confirm the schema, enum values, and required fields before feeding the output to automation.

How to Use

  1. Read prompts/secrets-exposure-review.md and follow its input audit, coverage checklist, and output contract.
  2. Extract scope, environment, version, roles, data, dependencies, constraints, and available evidence.
  3. Model secret locations, flows, log exposure, configuration boundaries, and rotation evidence with reviewable scenarios, separating known facts, inferences, and candidate validation.
  4. Record impact, priority, owner role, close condition, and validation method for each item.
  5. With incomplete input, deliver a bounded draft; do not turn a risk assumption into a confirmed vulnerability or security pass.

Reference Files

  • Read prompts/secrets-exposure-review.md for every invocation.
  • Read evals/eval.yaml and matching evals/cases/ when evaluating the Skill.
  • Read references/, examples/, scripts/, or output-formats.md only when the directory exists and the task needs it.

Core Constraints

  • Analyze only secret locations, flows, log exposure, configuration boundaries, and rotation evidence; do not log in, call real APIs, or read credentials.
  • Do not invent vulnerabilities, exploit success, remediation completion, scan coverage, or security-pass claims.
  • Mark unsupported evidence as pending, blocked, or unassessed and provide an isolated validation method.
  • Leave risk acceptance, exception authorization, and release judgment to a Human.

Delivery Self-Check

  • Complete the known, missing, conflicting, stale, out_of_scope, and assumptions audit.
  • Cover the secrets exposure, triggers, expected concerns, and evidence state.
  • Separate facts, inferences, recommendations, gaps, and Human decisions.
  • Do not turn static findings or a dry-run into a real exploit, absence-of-vulnerability, or release-approval claim.

Common Pitfalls

  • Treating broad security review or API contract checking as a complete substitute for Secrets-Exposure Review.
  • Listing attack names without applicability, evidence, expected results, or close conditions.
  • Declaring a system secure with incomplete input, or reading real credentials for completeness.

Best Practices

  • Start with high-impact, hard-to-detect, permission-sensitive, or data-sensitive paths.
  • Use redacted material, least privilege, isolated environments, and reversible validation suggestions.
  • Make each security conclusion reviewable by another engineer from its source and boundary.

Signals

GitHub stars
217
Forks
31
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
secrets-exposure-review
Source
github.com/naodeng/awesome-qa-skills