Check security audit findings against code evidence and trace how untrusted input moves through a system. Guide a second review, document verification gaps, and assess draft reports against a minimum quality score before delivery.
Enforce output quality, evidence verification, and quality gates across security audits. Use this skill when you perform SAST, SCA, threat modeling, or security code reviews. Use this skill to verify code locations and taint traces for all findings. Use this skill to prevent the suppression of valid security findings. Use this skill to evaluate reports against quality thresholds (score ≥ 8/10) before delivery.
Review the instructions the agent will receive before you install.
Enforce output quality, intellectual honesty, and continuous improvement across security audits and code reviews.
This skill provides 7 modular capabilities in the references/ directory. Load each file when you reach its execution phase:
| Component | Reference File | Purpose |
|---|---|---|
| Clarification Protocol | references/clarification-protocol.md | Ask a maximum of 2 targeted questions when scope is not clear |
| Anti-Rationalization Guard | references/anti-rationalization-guard.md | Prohibited rationalizations and mandatory responses for findings |
| Self-Critique Loop | references/self-critique-loop.md | Mandatory second pass to verify evidence and coverage |
| Retry Protocol | references/retry-protocol.md | Tool failure and empty search handling: retry once, then document |
| Non-Negotiable Behaviors | references/non-negotiable-behaviors.md | Mandatory rules: do not fabricate, cite evidence, report gaps |
| Self-Reflection Quality Gate | references/self-reflection-quality-gate.md | Scoring rubric (1–10 scale) with a minimum threshold of 8 |
| Self-Learning System | references/self-learning-system.md | Templates and rules for lessons and security memories |
Load reference files as you reach each phase:
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Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.
Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.
Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.
Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.