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 instructions/bjcoombs/ai-native-toolkit/claude-mdgit clone --depth 1 https://github.com/bjcoombs/ai-native-toolkitWrote 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/instructions/bjcoombs/ai-native-toolkit/claude-md)<a href="https://agentmods.dev/instructions/bjcoombs/ai-native-toolkit/claude-md"><img src="https://agentmods.dev/badge/instructions/bjcoombs/ai-native-toolkit/claude-md.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 | $0.05597 | $0.05597 |
| Opus 5 | $0.02799 | $0.02799 |
| Sonnet 5 | $0.01119 | $0.01119 |
| Haiku 4.5 | $0.00560 | $0.00560 |
Grade A, and why
ai-native-toolkit CLAUDE.md 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
In-repo contract for agents editing ai-native-toolkit. Repo-specific rules only - global conventions (voice, workflow, worktrees) live in the user's ~/.claude/CLAUDE.md and aren't repeated here.
Scope of this repo
Source for the ai-native-toolkit Claude Code plugin. Portable skills (/assess, /huddle, /deslop, /ghsync, /skill-forge, /semantic-compress), portable framework commands (/6hats, /understand), personal workflow commands that are opt-in (/tm, /issues, /fix-pr, /fix-develop), and team-orchestration library skills (marathon, pr-review-merge, ab-equivalence) that are composed by the workflow commands and portable skills rather than invoked standalone.
The deliverable is markdown: agents, commands, skills. It also ships a Python deterministic core under skills/assess/scripts/ (plus lib/) and a standalone-skill build pipeline under scripts/. There is no application runtime, but there are pytest suites (skills/assess/, scripts/), a ruff + mypy lint gate, and a standalone-ZIP build step - all enforced in CI.
North star
Everything here serves one goal: make an AI contributor feel like an engineer who has been in the org eighteen months, not a brand-new hire. The difference is externalized context, not capability. An AI is structurally always the new hire - each session starts with an empty head and one narrow context window - so the codebase has to supply the tenure: a navigable map, load-bearing contracts made explicit, complexity made locally legible so the relevant slice fits one keyhole.
The safety half is non-negotiable: the goal is legibility you can trust, not omniscience you can't. An agent fluent about code nobody can verify is the dangerous failure, not the win. Answers must stay anchored to code a human can spot-check.
The other half is the same ethic pointed at the write side: when a contributor makes a mistake, ask "what made it possible, and what would make it impossible next time?" - not "who's to blame". Guardrails (linters, architecture tests, CI gates, coverage, review automation - Layers 3-7) aren't a leash on the AI; they protect the contributor from costly mistakes by design, the way an org protects a human engineer with RBAC and staged environments rather than hope. This is correctness by construction / poka-yoke: make the wrong action hard and the right action the path of least resistance.
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 · 224 lines · 5,597 tokens per session scan A 17c43191b9dd
ai-native-toolkit CLAUDE.md is an instructions file published in the GitHub repository bjcoombs/ai-native-toolkit (30 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 5,597 tokens to every session, about $0.0280 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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