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.
npx agentmods add skills/whitequeen306/code-cortex-loop/reflectnpx skills add whitequeen306/code-cortex-loop --skill reflectgit clone --depth 1 https://github.com/whitequeen306/code-cortex-loopWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00048 | $0.02186 |
| Opus 5 | $0.00024 | $0.01093 |
| Sonnet 5 | $0.00010 | $0.00437 |
| Haiku 4.5 | $0.00005 | $0.00219 |
Grade A, and why
reflect-and-learn 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.
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect and Learn
CodeCortexLoop v2.2 — turn successful Direct fixes into reusable playbook memory.
Overview
After a Direct-mode run (or manual reflect), produce a human retrospective and a structured reflection that playbook.mjs record can upsert into .cortexloop/playbook.json.
Playbook entries are recall, not authority — new entries start as candidate until diverse verified evidence promotes them. Never skip analysis or blindly apply stored fixes.
Scope (what this skill covers)
| Topic | Authoritative doc |
|---|---|
When to run reflect, skip conditions, Step 6 gates (learning.enabled, Direct + re-verify) |
commands/cortexloop.md |
Manual /cortexloop-reflect orchestration |
commands/cortexloop-reflect.md |
Playbook trust model, signature, tiers, feedback / prune |
rules/learning-loop.mdc |
This skill covers only: how to extract generalizable patterns from a successful fix session and write high-quality 08-reflection.md + reflection.json. Do not duplicate CLI or trust-model details here — follow the table above when those questions arise.
When to Use
- Automatically after CodeCortexLoop Direct mode completes re-verification successfully (orchestrator loads this skill in Step 6)
- Manually via
/cortexloop-reflectwhen you want to capture learnings from a recent fix session - When you have evidence of what was fixed (ideally
docs/cortexloop/report.json+ git diff)
When NOT to use:
- Report-only runs with no fixes applied
- Failed or incomplete Direct runs (tests still failing)
- One-off project-specific hacks that cannot generalize
Inputs
Read before writing:
docs/cortexloop/report.json— findings marked fixed, before/after scores- Git diff of changes applied in Direct mode (diff wins if it disagrees with the report)
.cortexloop/playbook.json— skim existing entries for authoring-time dedupcortexloop.config.json→learningblock (paths, global flag) — forrecordflags onlyrules/learning-loop.mdc— when unsure whether something belongs in playbook memory
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.
- 2d ago First seen · 198 lines · 48 tokens per session scan A d65eea52a7ec
reflect-and-learn is a skill published in the GitHub repository whitequeen306/code-cortex-loop (15 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 2,186 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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