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/isvlasov/rageatc-oss/codifynpx skills add isvlasov/rageatc-oss --skill codifygit clone --depth 1 https://github.com/isvlasov/rageatc-ossWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/isvlasov/rageatc-oss/codify)<a href="https://agentmods.dev/skills/isvlasov/rageatc-oss/codify"><img src="https://agentmods.dev/badge/skills/isvlasov/rageatc-oss/codify.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00037 | $0.00704 |
| Opus 5 | $0.00018 | $0.00352 |
| Sonnet 5 | $0.00007 | $0.00141 |
| Haiku 4.5 | $0.00004 | $0.00070 |
Grade A, and why
codify 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 4d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codify
Step 1 — Read the inbox
Read LEARNINGS.md at the project root. Candidates are entries without a Codified → marker. If there are none, report that and stop. If the file doesn't follow the standard ## YYYY-MM-DD — Title entry format (e.g. a numbered takeaways list), treat each discernible item as an entry and place markers where they unambiguously attach to the item.
Step 2 — Cluster
Group candidate entries that share a root cause or a target file. A pattern across several entries is stronger evidence than any single entry — codify the pattern, not each instance.
Step 3 — Find the structural fix
The entry recorded the symptom and (usually) the likely cause at capture time. Now do the analysis the capture deliberately skipped: ask why the system allowed the failure, and what change prevents recurrence.
Test every proposed fix: would it have prevented the original incident even if nobody remembered the learning? "Be more careful" or "remember to X" fails this test — put the knowledge where the future reader will already be looking (the skill they will load, the rule that is always in context, the template they will copy). Prefer the smallest change that passes.
Some entries need no fix: a durable technical fact (version pin, API quirk) with no better home is already serving its purpose where it is. Others are not ready — a single incident with an unclear cause stays in the inbox until the pattern repeats.
Step 4 — Choose the target
| Learning kind | Target |
|---|---|
| Workflow or process failure in plugin-orchestrated work | rageatc skill, agent, or rule — record as a proposal; implement in the rageatc workshop, never in the installed plugin |
| Project-specific convention or constraint | Project CLAUDE.md or .claude/rules/ |
| Technical fact about the project's stack | Project docs (ARCHITECTURE.md, reference docs) — or the entry itself if no better home exists |
| Behaviour that should apply across all projects | ~/.claude/CLAUDE.md |
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.
- 4d ago First seen · 53 lines · 37 tokens per session scan A 1be0a852dc43
codify is a skill published in the GitHub repository isvlasov/rageatc-oss (9 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 704 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 2026-08-31.
Other skills, from other repositories
apex-methodology
Use when starting ANY development task -- feature, bug fix, refactor, hotfix (triggers: implement, create, build, fix, add feature, refactor, develop).
agent-creator
Use when creating expert agents. Generates agent.md with frontmatter, hooks, required sections, and skill references.
challenge
Use before a root-cause, done/verified claim, irreversible action, or 2nd-time fix reaches the owner (APEX or plain conversation); also fires at every eLicit/Verify gate. Not for code correctness (use sniper).
code-quality
Use when validating code quality after modifications -- SOLID compliance, DRY duplication, linter errors, architecture violations. Do NOT use for functional verification (run verification FIRST, then code-quality).
elicitation
Use when an expert agent self-reviews and self-corrects code after the Execute phase, before sniper validation (BMAD-METHOD elicitation techniques).
react-effects-audit
Use when auditing React or Next.js components for unnecessary or unsafe useEffect usage -- detects 9 anti-patterns from "You Might Not Need an Effect".