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⚔️ ironcode

A production-grade engineering gate for AI coding agents.

One discipline that produces and protects production-grade code — security, resource safety, backend cost, defensive edges, and evidence that the work is actually done.

License: MIT Claude Code Codex CLI

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Why

AI agents write plausible code fast — and skip the same checks humans skip under time pressure: the undisposed stream, the query in a loop, the catch {}, the "should work now" with nothing actually run.

ironcode is not a style guide. It is a gate. It forces the checks that get skipped, and it forbids claiming "done" without observable proof.

The Iron Laws

# Law Meaning
1 Evidence before claims Never say "done / fixed / works" without fresh test, build, or run output.
2 Spec before style First prove it solves the right problem; only then critique quality.
3 Root cause before fix Reproduce and trace before patching. Symptom-patching is failure.
4 Own analysis before external input Verify every linter/tool/agent finding against the actual code.
5 Cost is a correctness property N+1 queries and unbounded fetches are defects, not "optimizations for later".

The five quality dimensions

Reference files load on demand — a one-line change never pulls in a thousand lines of checklist.

Dimension Reference Catches
🔐 Security references/security.md Secrets, injection, authz/RLS, SSRF, OWASP Top 10
🧹 Resource safety references/resource-safety.md Leaked listeners, streams, timers, controllers, unbounded caches
💸 Data access & cost references/data-access.md N+1, missing pagination, over-fetching, schema drift, missing indexes
🛡️ Defensive coding references/defensive.md Null/edge cases, swallowed errors, races, idempotency
🧭 Maintainability references/checklist.md Naming, size, duplication, dead code — plus the full gate checklist
🚀 Ship-readiness references/ship-readiness.md Release scope: testing strategy, observability, deploy compatibility, supply chain, privacy

How it works

The skill is adaptive — it detects which mode the agent is in:

PLAN   →  design the checks in before writing
BUILD  →  apply the patterns while writing (teardown written with every resource)
GATE   →  spec → diagnostics → five dimensions → verify → report with evidence

Every finding is concrete and actionable:

🔴 home_controller.dart:120 — fetches all rows (no limit), re-runs every rebuild.
   Fix: keyset pagination + in-flight guard + cache.
Verification: flutter analyze → 0 issues · make test → 250 passed
Verdict: CHANGES NEEDED

Install — Claude Code

git clone https://github.com/djfksjd/ironcode.git ~/.claude/skills/ironcode

Then invoke with /ironcode, or just ask for a production-grade / rigorous / leak-free implementation or review — the skill self-triggers on its description.

Install — Codex CLI

Codex CLI reads the same open SKILL.md skill format (the old ~/.codex/prompts custom prompts are deprecated). Depending on your Codex version, the user skill directory is ~/.codex/skills or ~/.agents/skills:

git clone https://github.com/djfksjd/ironcode.git ~/.codex/skills/ironcode
# or
git clone https://github.com/djfksjd/ironcode.git ~/.agents/skills/ironcode

Invoke via the skill selector (/skills) or $ironcode.

Scope

Language- and stack-agnostic by design: patterns are given for Flutter/Dart, JS/TS, C#/Java/Kotlin, Go, Rust, and SQL/Postgres (incl. Supabase RLS). Examples lean on real production incidents; adapt the specifics to your stack.

Severity rubric

Severity Disposition
🔴 Security hole, data loss, crash, leak, unbounded cost Fix before merge
🟠 Real bug, strong smell, missing edge handling Fix before merge
🟡 Style, minor naming, optional cleanup When convenient
🔵 Optional improvement Author's call

Findings are ranked by severity × exploitability × blast radius — never inflated, never flattened.

License

MIT

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Production-grade engineering gate skill for AI coding agents (Claude Code + Codex CLI)

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