SuperAPI AI Directory
Partner with us中文AI newsFavorites
Skillssecurity

security

Security & auditing

View repository

Updated 10-15 21:35

About this skill

Codex compatibility note:

  • Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
  • Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
  • User-question prompts mean to ask the user directly in Codex.
  • Ignore Claude-specific mode-switch instructions when they appear.
  • Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
  • Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
  • Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
  • For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
  • If a required step/tool cannot run in this environment, stop and ask the user before adapting.

Codex Project-Reference Loading (No Hooks)

Codex does not receive Claude hook-based doc injection. When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.

Always read:

  • docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
  • docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
  • docs/project-reference/lessons.md (always-on guardrails and anti-patterns)

Situation-based docs:

  • Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md, project-structure-reference.md
  • Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
  • Spec/test-case planning or TC mapping: feature-docs-reference.md
  • Integration test implementation/review: integration-test-reference.md
  • E2E test implementation/review: e2e-test-reference.md
  • Code review/audit work: code-review-rules.md plus domain docs above based on changed files

Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.

[BLOCKING] Execute skill steps in declared order. NEVER skip, reorder, or merge steps without explicit user approval. [BLOCKING] Before each step or sub-skill call, update task tracking: set in_progress when step starts, set completed when step ends. [BLOCKING] Every completed/skipped step MUST include brief evidence or explicit skip reason. [BLOCKING] If Task tools are unavailable, create and maintain an equivalent step-by-step plan tracker with the same status transitions.

Quick Summary

Goal: Perform security review against OWASP Top 10 and project authorization patterns.

Workflow:

  1. Scope — Identify security-sensitive code areas
  2. Audit — Review against OWASP categories and platform security patterns
  3. Report — Document findings with severity and remediation

Key Rules:

  • Analysis Mindset: systematic review, not guesswork
  • Check both backend and frontend attack surfaces
  • Use project authorization attributes and entity-level access expressions (see docs/project-reference/backend-patterns-reference.md)

$ARGUMENTS

Analysis Mindset (NON-NEGOTIABLE)

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

  • Verify security by reading the actual implementations — never assume code is secure at face value
  • Every vulnerability finding must include file:line evidence
  • If you cannot prove a vulnerability with a code trace, state "potential risk, not confirmed"
  • Question assumptions: "Is this actually exploitable?" → trace the input path to confirm
  • Challenge completeness: "Are there other attack vectors?" → check all input boundaries
  • No "looks secure" without proof — state what you verified and how

Activate arch-security-review skill and follow its workflow.

CRITICAL: Present your security findings. Wait for explicit user approval before implementing fixes.

Sub-Agent Type Override

MANDATORY: Security reviews spawn security-auditor sub-agent for Round 2, NOT code-reviewer. Rationale: security-auditor has dedicated OWASP protocols, auth flow analysis, injection risk tracing, dependency CVE checking, and microservices boundary security context that code-reviewer lacks.

Recursive Quality Loop

  1. Round 1: Main agent (or arch-security-review skill) runs analysis → draft findings report
  2. Round 2: Spawn fresh security-auditor sub-agent (agent_type: "security-auditor") — ZERO memory of Round 1. Include in prompt: OWASP Top 10 checklist, auth flows, injection risks, dependency CVEs, microservices boundary security.
  3. Issues found → fix → Round 3 with NEW fresh security-auditor sub-agent
  4. Max 3 rounds → escalate to user via a direct user question
  5. Clean Round 1 ENDS the review. When issues are found, fix and spawn a fresh sub-agent for Round 2 — main agent rationalizes own work, fresh eyes catch what was dismissed.

Run python .claude/scripts/code_graph query callers_of <function> --json to trace all entry points into sensitive functions.

Graph Intelligence (RECOMMENDED if graph.db exists)

If .code-graph/graph.db exists, enhance analysis with structural queries:

  • Trace data flow to sensitive functions: python .claude/scripts/code_graph query callers_of <function> --json
  • What does this function call? python .claude/scripts/code_graph query callees_of <function> --json
  • Batch analysis: python .claude/scripts/code_graph batch-query file1 file2 --json

See <!-- SYNC:graph-assisted-investigation --> block above for graph query patterns.

Graph-Trace for Data Flow Analysis

When graph DB is available, use trace to analyze data flow paths for security review:

  • python .claude/scripts/code_graph trace <entry-point> --direction downstream --json — trace data flow from input to all consumers (find where untrusted data travels)
  • python .claude/scripts/code_graph trace <sensitive-file> --direction upstream --json — find all entry points that reach sensitive code
  • Trace reveals cross-service MESSAGE_BUS flows where data crosses trust boundaries

Workflow Recommendation

MANDATORY IMPORTANT MUST ATTENTION — NO EXCEPTIONS: If you are NOT already in a workflow, you MUST ATTENTION use a direct user question to ask the user. Do NOT judge task complexity or decide this is "simple enough to skip" — the user decides whether to use a workflow, not you:

  1. Activate quality-audit workflow (Recommended) — security → sre-review → test
  2. Execute $security directly — run this skill standalone

Phase 1: Why-Review Self-Validation Gate (MANDATORY when findings exist)

Purpose: Adversarial validation of own findings BEFORE handoff. Catches over-flagged Highs, false positives, and severity inflation at the source rather than letting them propagate downstream.

Trigger: Any finding produced (Critical, High, Medium, OR Low). Skip ONLY when the report's verdict is unconditional PASS with literally zero findings.

Protocol:

  1. Read own finalized report from plans/reports/{skill}-{date}-{slug}.md
  2. Invoke $why-review skill with arg: validate findings in plans/reports/{skill}-{date}-{slug}.md — verify each finding has file:line proof, steel-man each rejected interpretation, and stress-test severity classifications
  3. Read why-review output from plans/reports/why-review-{date}.md
  4. If why-review demotes/removes any finding: UPDATE own finalized report with revised severities, remove false positives, and add a ## Why-Review Validation Notes section citing what changed and why
  5. If why-review confirms all findings: Append ## Why-Review Validation line to own report stating "All N findings re-validated against actual code; no severity changes."

Skip conditions (record explicit reason if skipping):

  • Verdict is unconditional PASS with zero findings → log "Skipped — no findings to validate"
  • Why-review skill itself is the active context (avoid recursion)

Why this exists: AI sub-agent reports inherit confirmation bias — the orchestrator absorbs severity claims as ground truth. The 2026-05-09 review incident produced 5 Highs; adversarial validation demoted 3 of them. Codify this as standard practice.

Next Steps

MANDATORY IMPORTANT MUST ATTENTION — NO EXCEPTIONS after completing this skill, you MUST ATTENTION use a direct user question to present these options. Do NOT skip because the task seems "simple" or "obvious" — the user decides:

  • "$sre-review (Recommended)" — Production readiness review
  • "$performance" — Analyze performance next
  • "Skip, continue manually" — user decides

[IMPORTANT] Use task tracking to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

  • docs/project-reference/domain-entities-reference.md — Domain entity catalog, relationships, cross-service sync (read when task involves business entities/models) (read directly when relevant; do not rely on hook-injected conversation text)

External Memory: For complex or lengthy work (research, analysis, scan, review), write intermediate findings and final results to a report file in plans/reports/ — prevents context loss and serves as deliverable.

Evidence Gate: MANDATORY IMPORTANT MUST ATTENTION — every claim, finding, and recommendation requires file:line proof or traced evidence with confidence percentage (>80% to act, <80% must verify first).

Sub-Agent Selection — Full routing contract: .claude/skills/shared/sub-agent-selection-guide.md Rule: Route specialized domains (architecture, security, performance, DB, E2E, integration-test, git) to the matching specialist agent (see guide above) — NEVER use code-reviewer for these. — why: code-reviewer lacks each domain's checklist, so specialized issues slip through.

Graph-Assisted Investigation — MANDATORY when .code-graph/graph.db exists.

HARD-GATE: MUST ATTENTION run at least ONE graph command on key files before concluding any investigation.

Pattern: Grep finds files → trace --direction both reveals full system flow → Grep verifies details

Task Minimum Graph Action
Investigation/Scout trace --direction both on 2-3 entry files
Fix/Debug callers_of on buggy function + tests_for
Feature/Enhancement connections on files to be modified
Code Review tests_for on changed functions
Blast Radius trace --direction downstream

CLI: python .claude/scripts/code_graph {command} --json. Use --node-mode file first (10-30x less noise), then --node-mode function for detail.

Incremental Result Persistence — MANDATORY for all sub-agents or heavy inline steps processing >3 files.

  1. Before starting: Create report file plans/reports/{skill}-{date}-{slug}.md
  2. After each file/section reviewed: Append findings to report immediately — never hold in memory
  3. Return to main agent: Summary only (per SYNC:subagent-return-contract) with Full report: path
  4. Main agent: Reads report file only when resolving specific blockers

Why: Context cutoff mid-execution loses ALL in-memory findings. Each disk write survives compaction. Partial results are better than no results.

Report naming: plans/reports/{skill-name}-{YYMMDD}-{HHmm}-{slug}.md

Sub-Agent Return Contract — When this skill spawns a sub-agent, the sub-agent MUST return ONLY this structure. Main agent reads only this summary — NEVER requests full sub-agent output inline.

## Sub-Agent Result: [skill-name]

Status: ✅ PASS | ⚠️ PARTIAL | ❌ FAIL
Confidence: [0-100]%

### Findings (Critical/High only — max 10 bullets)

- [severity] [file:line] [finding]

### Actions Taken

- [file changed] [what changed]

### Blockers (if any)

- [blocker description]

Full report: plans/reports/[skill-name]-[date]-[slug].md

Main agent reads Full report file ONLY when: (a) resolving a specific blocker, or (b) building a fix plan. Sub-agent writes full report incrementally (per SYNC:incremental-persistence) — not held in memory.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

Evidence-Based Reasoning — Speculation is FORBIDDEN. Every claim needs proof.

  1. Cite file:line, grep results, or framework docs for EVERY claim
  2. Declare confidence: >80% act freely, 60-80% verify first, <60% DO NOT recommend
  3. Cross-service validation required for architectural changes
  4. "I don't have enough evidence" is valid and expected output

BLOCKED until: - [ ] Evidence file path (file:line) - [ ] Grep search performed - [ ] 3+ similar patterns found - [ ] Confidence level stated

Forbidden without proof: "obviously", "I think", "should be", "probably", "this is because" If incomplete → output: "Insufficient evidence. Verified: [...]. Not verified: [...]."

Source/test drift check. For coding, fix, debug, investigation, test, or review work: when source behavior changes, inspect affected unit/integration/E2E tests and decide from evidence whether tests should change to match intended behavior or the source change is an unintended bug to fix. Do not write tests for migration code; schema/data migrations are one-time execution paths, not core application logic.

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

IMPORTANT MUST ATTENTION cite file:line evidence for every claim. Confidence >80% to act, <60% = do NOT recommend.

IMPORTANT MUST ATTENTION run at least ONE graph command on key files when graph.db exists. Pattern: grep → trace → verify.

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

Prompt-Enhance Closing Anchors

IMPORTANT MUST ATTENTION follow declared step order for this skill; NEVER skip, reorder, or merge steps without explicit user approval IMPORTANT MUST ATTENTION for every step/sub-skill call: set in_progress before execution, set completed after execution IMPORTANT MUST ATTENTION every skipped step MUST include explicit reason; every completed step MUST include concise evidence IMPORTANT MUST ATTENTION if Task tools unavailable, maintain an equivalent step-by-step plan tracker with synchronized statuses

Closing Reminders

MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using task tracking BEFORE starting. MANDATORY IMPORTANT MUST ATTENTION validate decisions with user via a direct user question — never auto-decide. MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality. MANDATORY IMPORTANT MUST ATTENTION READ the following files before starting:

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using task tracking.

[IMPORTANT] Analyze how big the task is and break it into many small todo tasks systematically before starting — this is very important.

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/hooks/lib/prompt-injections.cjs + .claude/.ck.json

[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol — MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.

Generic portability boundary: Reusable skills and protocol text stay project-neutral; project-specific conventions are discovered from docs/project-config.json and docs/project-reference/. Apply shared AI-SDD from shared/sdd-artifact-contract.md. Read docs/project-config.json and docs/project-reference/docs-index-reference.md, then open the project reference docs named there. Any supported AI tool may execute when this shared context and local docs are available.

  1. DETECT: Match prompt against workflow catalog
  2. ANALYZE: Find best-match workflow AND evaluate if a custom step combination would fit better
  3. ASK (REQUIRED FORMAT): Use a direct user question with this structure unless the user explicitly invoked a workflow/skill and the local protocol treats explicit invocation as confirmation:
    • Question: "Which workflow do you want to activate?"
    • Option 1: "Activate [BestMatch Workflow] (Recommended)"
    • Option 2: "Activate custom workflow: [step1 → step2 → ...]" (include one-line rationale)
  4. ACTIVATE (if confirmed): Call $workflow-start <workflowId> for standard; sequence custom steps manually
  5. CREATE TASKS: task tracking for ALL workflow steps
  6. EXECUTE: Follow each step in sequence [CRITICAL-THINKING-MINDSET] Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination principle: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination. AI Attention principle (Primacy-Recency): Put the 3 most critical rules at both top and bottom of long prompts/protocols so instruction adherence survives long context windows. Goal-driven execution: Define success criteria first, loop until verified, and stop only when observable checks pass. Tests verify intent: Tests must protect business rules/invariants and fail when the protected intent breaks, not only mirror current behavior.

[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement — MANDATORY. Do not skip.

Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".

Extract lessons — ROOT CAUSE ONLY, not symptom fixes:

  1. Name the FAILURE MODE (reasoning/assumption failure), not symptom — "assumed API existed without reading source" not "used wrong enum value".
  2. Generality test: does this failure mode apply to ≥3 contexts/codebases? If not, abstract one level up.
  3. Write as a universal rule — strip project-specific names/paths/classes. Useful on any codebase.
  4. Consolidate: multiple mistakes sharing one failure mode → ONE lesson.
  5. Recurrence gate: "Would this recur in future session WITHOUT this reminder?" — No → skip $learn.
  6. Auto-fix gate: "Could $code-review/$code-simplifier/$security/$lint catch this?" — Yes → improve review skill instead.
  7. BOTH gates pass → ask user to run $learn. [TASK-PLANNING] [MANDATORY] BEFORE executing any workflow or skill step, create/update task tracking for all planned steps, then keep it synchronized as each step starts/completes.