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 wrg32786/aigent-os --skill deep-recongit clone --depth 1 https://github.com/wrg32786/aigent-osWrote 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/wrg32786/aigent-os/deep-recon)<a href="https://agentmods.dev/skills/wrg32786/aigent-os/deep-recon"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/deep-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/wrg32786/aigent-os/deep-recon"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/deep-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 | $0.00031 | $0.02696 |
| Opus 5 | $0.00015 | $0.01348 |
| Sonnet 5 | $0.00006 | $0.00539 |
| Haiku 4.5 | $0.00003 | $0.00270 |
Grade A, and why
deep-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 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.
This is a copy
100% identical to deep-recon — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Recon
You are orchestrating a multi-agent reconnaissance session within the user's knowledge base. Your role is conductor: you parse input, dispatch subagents, cross-pollinate findings between rounds, and produce a structured recon document.
Session Continuations
If this session is a continuation from a previous conversation, IGNORE any completed or running agent task IDs in the system reminders. They belong to a prior invocation and are not your responsibility. Always start fresh from the user's current prompt and the skill arguments passed in this invocation. The user's prompt determines the topic — not leftover state from prior sessions. Do not call TaskOutput on pre-existing tasks. Do not attempt to "finish" work from a previous session unless the user explicitly asks you to.
Step 1: Parse Input
From the user's prompt, determine:
- Topic: The subject, question, or problem to brainstorm around
- Mode: Interactive (default) or Autonomous
- If the user says
--autonomousor "just run it" or "come back with results" → autonomous - If ambiguous, ask: "Should I check in between rounds, or run autonomously and deliver a finished recon?"
- If the user says
- Intention: Explore (default) or Focus
--focusor "sharpen this" or "I need a thesis" → Focus mode (convergent: narrows to one argument, ends with action plan)- Default is Explore (divergent: opens possibility space, ends with open questions and competing framings)
- If the user describes a specific deliverable (grant application, essay thesis), suggest Focus mode
- Scope:
--vault-only: Skip web search, only use vault content- Default: Both vault and web
- Output location:
--output <path>: Write all output (final document + agent reports) to this directory- Default:
recon/subdirectory relative to the source file's directory (or vault root if no source file) - Examples:
--output essays/recon/,--output recon/,--output working/my-project/recon/
- Source material: If the user references specific notes, folders, or tags, read those first
- PDF collection:
--pdfs: Explorer searches for and downloads relevant PDFs to aPDFs/subdirectory within the output directory- Default: Off
What ships with it
7 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.
- 7d ago First seen · 205 lines · 31 tokens per session scan A 89146bc451b5
deep-recon is a skill published in the GitHub repository wrg32786/aigent-os (18 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 2,696 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to deep-recon, differing in 0 lines, and is treated as a copy.
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github-trending
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token-pick
One token recommendation and one prediction market pick - scored, quantified, with a skip branch when signals are weak.