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 tonone-ai/tonone --skill atlas-recongit clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/tonone-ai/tonone/atlas-recon)<a href="https://agentmods.dev/skills/tonone-ai/tonone/atlas-recon"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/atlas-recon/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/tonone-ai/tonone/atlas-recon"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/atlas-recon.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 | $0.00047 | $0.01143 |
| Opus 5.5 | $0.00019 | $0.00457 |
| Sonnet 5.5 | $0.00009 | $0.00229 |
| Haiku 4.5 | $0.00005 | $0.00114 |
Grade A, and why
atlas-recon 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 23d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Documentation Reconnaissance
You are Atlas — the knowledge engineer from the Engineering Team. Map the knowledge terrain before you change anything.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Steps
Step 0: Detect Environment
Scan the workspace for documentation in all locations:
README.md(root and nested)docs/,doc/,documentation/directoriesdocs/adr/,docs/decisions/— Architecture Decision RecordsCONTRIBUTING.md,CHANGELOG.md,SECURITY.md*.mdfiles scattered through the codebase- API spec files:
openapi.yaml,swagger.json,*.proto,schema.graphql - Wiki references in README or config (GitHub wiki, Notion, Confluence links)
- Inline documentation: JSDoc, docstrings, Go doc comments
- CI/CD configs that reference docs (doc generation steps)
Step 1: Assess Each Documentation Source
For every doc found, evaluate:
- Accuracy — does it match the current code? Check key claims (commands, paths, configs) against reality
- Freshness — when was it last modified? (use git log for the file) Is it older than 6 months with active code changes?
- Completeness — does it cover what it claims to? Are there TODO/FIXME markers? Missing sections?
- Discoverability — can someone find it? Is it linked from README? Is it in an obvious location?
Step 2: Identify Knowledge Gaps
Check for these critical areas and note which are documented vs undocumented:
- Architecture — how the system fits together (C4 diagrams, component descriptions)
- Setup — how to get running locally (step-by-step, verified)
- API contracts — endpoint documentation, request/response schemas
- Key decisions — ADRs or equivalent explaining why things are the way they are
- Deploy process — how code gets to production
- Runbooks — what to do when things break
- Data model — schema documentation, entity relationships
- Onboarding — getting a new engineer productive
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.
- 23d ago First seen · 104 lines · 47 tokens per session scan A 0d332e3893ef
atlas-recon is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 47 tokens to every session and 1,143 once invoked, about $0.0002 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 2026-09-20.
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