A deep-recon skill for coordinating extended research and brainstorming sessions with multiple agents. It gathers findings in rounds and produces a structured reconnaissance document.

Use it to explore a topic from several angles, compare findings between rounds, and create a structured recon document in interactive or autonomous mode.

It organizes broad exploration into a repeatable process instead of leaving ideas and discoveries scattered across a conversation.

deep-recon — Skill for Claude Code by kvarnelis

Install

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.

Any agent

npx skills add kvarnelis/deep-recon --skill deep-recon

Clone the repo

git clone --depth 1 https://github.com/kvarnelis/deep-recon

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/kvarnelis/deep-recon/deep-recon/github.svg)](https://agentmods.dev/skills/kvarnelis/deep-recon/deep-recon)

Your own site

<a href="https://agentmods.dev/skills/kvarnelis/deep-recon/deep-recon"><img src="https://agentmods.dev/badge/skills/kvarnelis/deep-recon/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/kvarnelis/deep-recon/deep-recon"><img src="https://agentmods.dev/badge/skills/kvarnelis/deep-recon/deep-recon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,696 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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 5.5	$0.00012	$0.01078
Sonnet 5.5	$0.00006	$0.00539
Haiku 4.5	$0.00003	$0.00270

Measured 2mo ago against content hash 89146bc451b5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

deep-recon — 100% identical, 0 lines differ

SKILL.md · 205 lines

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 --autonomous or "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?"
Intention: Explore (default) or Focus
- --focus or "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 a PDFs/ subdirectory within the output directory
- Default: Off

Read the full file on GitHub · 205 lines

Files

What ships with it

1 file 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.

templates/brainstorm-output.md 2.2 KB

Changes

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.

2mo ago First seen · 205 lines · 31 tokens per session scan A 89146bc451b5

Subscribe to this mod's changes

deep-recon is a skill published in the GitHub repository kvarnelis/deep-recon (44 stars, last pushed 8mo ago), licensed MIT. It adds 31 tokens to every session and 2,696 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 2026-08-30.

Research

deep-recon next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
deep-reconkvarnelis/deep-recon	Skill	44	8mo ago	MIT	A	31	Claude Code
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	103	Claude Code