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 CrypticSwarm/Swarmforge --skill agents-md-reconnaissancegit clone --depth 1 https://github.com/CrypticSwarm/SwarmforgeWrote 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/crypticswarm/swarmforge/agents-md-reconnaissance)<a href="https://agentmods.dev/skills/crypticswarm/swarmforge/agents-md-reconnaissance"><img src="https://agentmods.dev/badge/skills/crypticswarm/swarmforge/agents-md-reconnaissance.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 | $0.00062 | $0.01493 |
| Opus 5 | $0.00031 | $0.00746 |
| Sonnet 5 | $0.00012 | $0.00299 |
| Haiku 4.5 | $0.00006 | $0.00149 |
Grade A, and why
agents-md-reconnaissance 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 6d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What This Skill Does
Runs a structured, multi-phase reconnaissance to produce (or refine) a
directory-specific AGENTS.md that gives future agent sessions the context
they need to work effectively in that part of the codebase.
Workflow Overview
Four phases, executed in order:
- Explore -- Build an initial AGENTS.md from codebase exploration
- Stress-Test -- Spawn subagents with real tasks derived from git history to find gaps
- Refine -- Incorporate subagent findings into the AGENTS.md
- Review -- Run a review agent to catch inconsistencies and trim noise
Phase 1: Explore
Goal: Produce a first-draft AGENTS.md for the target directory.
- Read the target directory listing to understand the file landscape.
- Identify the major subsystems by reading key entry points, config files, and index/barrel files.
- Use the Task tool with explore agents to investigate:
- Architecture and data flow (how does a request/operation flow through the code?)
- Key abstractions and class hierarchies
- Naming conventions and domain terminology
- Configuration patterns and registration mechanisms
- Common procedures (how are new features of each type typically added?)
- Write the initial AGENTS.md at the target path. Structure it as:
- Architecture Overview -- One-paragraph orientation
- Naming Conventions -- Place near the top so terms are defined before use
- Key Files -- Curated list with one-line descriptions (not exhaustive)
- Subsystem sections -- One section per major subsystem, covering concepts an agent needs to make correct decisions (not implementation minutiae)
- Common Procedures -- Step-by-step checklists for recurring task types
Writing guidelines:
- Optimize for agent decision-making, not human onboarding.
- Avoid line-number references (they drift on any edit).
- Define conventions once, in one place, and reference from elsewhere.
- Prefer high-level summaries over implementation details an agent would verify in code anyway.
- Keep under 500 lines. If longer, trim low-signal content.
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.
- 6d ago First seen · 146 lines · 62 tokens per session scan A f7e18ac9897f
agents-md-reconnaissance is a skill published in the GitHub repository CrypticSwarm/Swarmforge (2 stars, last pushed 7d ago), licensed MIT. It adds 62 tokens to every session and 1,493 once invoked, about $0.0003 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.
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