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 skills add CodedRichy/food-chain-ideation --skill food-chain-codegit clone --depth 1 https://github.com/CodedRichy/food-chain-ideationWrote 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/codedrichy/food-chain-ideation/food-chain-code)<a href="https://agentmods.dev/skills/codedrichy/food-chain-ideation/food-chain-code"><img src="https://agentmods.dev/badge/skills/codedrichy/food-chain-ideation/food-chain-code.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 5.1 | $0.00075 | $0.03219 |
| Opus 5 | $0.00037 | $0.01610 |
| Sonnet 5 | $0.00015 | $0.00644 |
| Haiku 4.5 | $0.00007 | $0.00322 |
Grade A, and why
food-chain-code 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 7d 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 — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Food Chain Code
Adversarial architecture stress-tester. Each agent attacks a technical decision from a distinct failure vector under strict role-lock. Weak arguments are eliminated each round. Survivors absorb and evolve. The architecture patches itself. One apex predator remains — and the design that survived it is the design worth building.
What This Is
The only adversarial skill built for software architecture decisions, not product validation. It produces structural insights a single-pass review cannot — because each attacker operates in isolation, the elimination mechanic forces genuine technical pressure, and the architecture is stress-tested against its evolved version in every subsequent round. Use it before writing code, not after.
What This Is Not
- A code review tool. This attacks architecture decisions, not implementations.
- A brainstorming tool. The architecture must already be proposed. Use a design skill first if not.
- A product validation tool. For product ideas, use
food-chain-ideation. - A benchmarking tool. It does not measure performance — it predicts structural failure.
- A linter or static analysis replacement. Those catch bugs. This catches regret.
Prerequisites
Read references/code-animal-library.md before designing any ecosystem.
Use the Quick Selection Guide at the top of that file to identify candidate animals fast.
Never invent behavioral traits from scratch. Never select animals whose failure vectors overlap.
Check the Anti-Pattern Combinations section before finalizing the ecosystem.
Pre-Flight Checks
Run these before starting the battle. If any fail, fix them before proceeding.
- Input sharp enough? The architecture proposal must contain three things: a defined stack (languages, frameworks, infrastructure), a stated scale target (users, requests/sec, data volume), and a team size (who maintains this). If any are missing, ask one mandatory question before proceeding. Not optional. Not skippable.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 340 lines · 75 tokens per session scan A fe344edb8a7b
food-chain-code is a skill published in the GitHub repository CodedRichy/food-chain-ideation (3 stars, last pushed 2mo ago), licensed MIT. It adds 75 tokens to every session and 3,219 once invoked, about $0.0004 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
review-work
Post-implementation gate review: run manual QA on the real surface yourself, then launch ONE gate reviewer (never a panel) to audit goal, constraints, code quality, security, missed context, and QA evidence. Use before a PR handoff or when the user explicitly asks to review completed work.
one-way-door
Flags irreversible decisions before commit. Use for data models, infra, auth boundaries, API contracts, event schemas, CI/CD.
critical-code-reviewer
Rigorously review code or pull requests for correctness, security, accessibility, maintainability, tests, and edge cases. Use when users request a critical code review, want a guided walkthrough of findings, need implementer-facing feedback, or want to prepare, create, or submit a GitHub pull request review.
map-codebase
Deep architecture report that fans out parallel inspections across different aspects of the codebase (structure, tech stack, APIs, patterns, data flow, dependencies, testing) and synthesizes findings into a comprehensive document at .turbo/codebase-map.md and .turbo/codebase-map.html. Use when the user asks to "map…
reply-to-pr-threads
Draft, confirm, and post replies to GitHub PR review threads. Handles per-category reply formatting, re-fetches thread resolution state so auto-resolved threads are skipped, and posts via GraphQL. Use when the user asks to "reply to PR threads", "post PR thread replies", or "draft PR reply messages".
answer-reviewer-questions
For each reviewer question on a PR, recall implementation reasoning and compose a raw answer. Use when the user asks to "answer reviewer questions", "draft answers to PR questions", or "explain reviewer questions".