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 cmdr-chara/codex-toolkit --skill repository-intelligencegit clone --depth 1 https://github.com/cmdr-chara/codex-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/skills/cmdr-chara/codex-toolkit/repository-intelligence)<a href="https://agentmods.dev/skills/cmdr-chara/codex-toolkit/repository-intelligence"><img src="https://agentmods.dev/badge/skills/cmdr-chara/codex-toolkit/repository-intelligence/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cmdr-chara/codex-toolkit/repository-intelligence"><img src="https://agentmods.dev/badge/skills/cmdr-chara/codex-toolkit/repository-intelligence.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00036 | $0.00557 |
| Opus 5.5 | $0.00014 | $0.00223 |
| Sonnet 5.5 | $0.00007 | $0.00111 |
| Haiku 4.5 | $0.00004 | $0.00056 |
Grade A, and why
repository-intelligence 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 24d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository Intelligence
Build an evidence-backed map of the repository without turning discovery into implementation.
Trigger boundary
Use this skill for architecture/ownership mapping, dependency boundaries, blast radius, hotspots, change impact, or likely edit conflicts.
Do not trigger to delegate work, implement the change, diagnose a specific runtime failure, or approve a release.
Required inputs
Resolve repository root, task/change hypothesis, candidate/base ref when relevant, working-tree state, generated/vendor boundaries, and the decision this map must support.
For a breadth-first signal scan:
python skills/repository-intelligence/scripts/repo_signal_scan.py . --format markdown
Treat scanner output as evidence leads, not ownership truth.
Safety baseline
- Preserve uncommitted user work and remain read-only.
- Do not edit generated/vendor content during mapping.
- Distinguish facts from inference and state confidence.
- Prefer repository-native manifests, imports, registrations, schemas, tests, and build/deploy configuration over naming guesses.
Workflow
- Frame the decision. State what must be mapped and what would change the next action.
- Establish state. Record branch/ref, worktree changes, languages/workspaces, generated boundaries, and major entry points.
- Map components and dependencies. Trace imports/calls, data/contracts, routes/events, build/runtime links, persistence, and operational boundaries.
- Determine ownership. Use code/config/history/docs evidence; mark ambiguous ownership rather than inventing it.
- Map impact. Identify direct edits, consumers, compatibility surfaces, tests, deployment units, migrations, and generated outputs.
- Locate hotspots/conflicts. Highlight high-coupling/stateful areas and likely overlapping write scopes.
- Report. Use
references/evidence-model.mdandreferences/output-schema.mdfor the evidence/confidence contract.
Handoffs and interaction boundaries
What ships with it
4 files 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.
- 24d ago Changed · -137 lines · -44 tokens per session 4041a6321ee8
- 1mo ago First seen · 193 lines · 80 tokens per session scan A a648c82a6c30
repository-intelligence is a skill published in the GitHub repository cmdr-chara/codex-toolkit (2 stars, last pushed 11d ago), licensed MIT. It adds 36 tokens to every session and 557 once invoked, about $0.0001 per session on Opus 5.5. 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.
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