cortexloop-expert-core

A shared protocol for experts in a seven-step CodeCortexLoop code-review pipeline. It makes each expert inspect only one assigned category and pass structured results to the next stage.

In plain words
What is it for?
Use it as the common operating procedure for pipeline experts reviewing a codebase. It helps them read the run files, write category reports, and defer out-of-scope findings.
Why use it?
It prevents reviewers from duplicating work or mixing unrelated concerns, such as checking security during a correctness review. It also defines how reports and handoff data are saved.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/whitequeen306/code-cortex-loop/cortexloop-expert-core
Any agent
npx skills add whitequeen306/code-cortex-loop --skill cortexloop-expert-core
Clone the repo
git clone --depth 1 https://github.com/whitequeen306/code-cortex-loop

Made for: Claude Code, Codex.

Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,359 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00038 $0.01359
Opus 5 $0.00019 $0.00679
Sonnet 5 $0.00008 $0.00272
Haiku 4.5 $0.00004 $0.00136

Measured 2d ago against content hash 9d6076486934, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

cortexloop-expert-core scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/cortexloop-expert-core/SKILL.md · 114 lines

How it starts

The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CodeCortexLoop Expert Core

Ultra-thin shared contract for all 7 pipeline experts. Load this first, then your pass contract (passes/XX-*.md) and domain depth skill(s) only.

Your role

  • You are one expert in a fixed 7-pass pipeline — not the orchestrator, not a general reviewer.
  • Analyze only your pass category. Other categories belong to later experts.
  • Write category markdown + handoff JSON on disk before returning.
  • Never invoke other agents — the orchestrator runs the pipeline.

Domain boundary

  1. Read your pass contract for in-scope / out-of-scope lists.
  2. If you notice a concern outside your category: do not score it — add deferToLaterPasses with target pass key + one-line note.
  3. Do not "quickly check" security while doing correctness, tests while doing security, etc. Mentioning other domains in findings causes cross-pass noise.

Inputs

  • Run archive: read .cortexloop/run-meta.json first — write category report to reports.categoryReports[...] under runDir; include header 运行时间: {runDisplayTime} (human-readable, not ISO)
  • Scope (on disk): .cortexloop/scope-manifest.json, .cortexloop/scope-paths.json
  • Index strategy: read scope-manifest.jsonindexStrategy first (tier L0/L1, optional codegraph hints)
  • Scope map (large scope): if .cortexloop/scope-map.json exists, read in this order:
    1. hotspots + entryFiles — prioritize depth here first
    2. hotspotSymbolHints — export names on hotspot entry files (grep targets, not a call graph)
    3. mustReview + patternHits[<your category>] — mandatory review
    4. longTailSample.paths — sample at least a few non-hotspot files per pass
    5. recentChangeFocus — git-changed files
  • Code retrieval order (required):
    1. indexStrategy → know guaranteed tier (L0 paths only, or L1 + scope-map)
    2. scope-map priorities above
    3. grep/glob for file slices
    4. Only when needed: codegraph MCP if indexStrategy.optionalDeepIndex.useWhen applies and userDecision !== 'decline'
    5. Without codegraph / user declined: continue grep/Read; mark unverified chains Confidence medium
  • Coverage rule: MAP is prioritization, not exclusion. Never treat non-hotspot paths as out-of-scope.
  • Prior handoff JSON paths (if any) — read summaries and defer notes in your subagent session from disk; do not re-run upstream analysis
  • Playbook query output (if orchestrator enabled learning) — recall only, re-verify every claim

Read the full file on GitHub · 114 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 114 lines · 38 tokens per session scan A 9d6076486934

Subscribe to this mod's changes

cortexloop-expert-core is a skill published in the GitHub repository whitequeen306/code-cortex-loop (15 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 1,359 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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