bmad-deep-recon

bmad-deep-recon is a skill for Claude Code from alvin-reyes/better-agentic-ide. It costs 161 tokens per session (2,244 once invoked), scanned A, original, MIT.

Research a topic to support a decision, three ways: draft a research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), turn a finished research report into a short summary with cited sources that other skills can use directly, or run the research here with parallel web searches.…

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: mentions subagents.

Install with agentmods
npx agentmods add skills/alvin-reyes/better-agentic-ide/bmad-deep-recon
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 alvin-reyes/better-agentic-ide --skill bmad-deep-recon
Clone the repo
git clone --depth 1 https://github.com/alvin-reyes/better-agentic-ide

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.

agentmods badge for bmad-deep-recon

README.md
[![agentmods](https://agentmods.dev/badge/skills/alvin-reyes/better-agentic-ide/bmad-deep-recon/github.svg)](https://agentmods.dev/skills/alvin-reyes/better-agentic-ide/bmad-deep-recon)
Your own site
<a href="https://agentmods.dev/skills/alvin-reyes/better-agentic-ide/bmad-deep-recon"><img src="https://agentmods.dev/badge/skills/alvin-reyes/better-agentic-ide/bmad-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.

agentmods 80×15 button for bmad-deep-recon

Your own site · 80×15
<a href="https://agentmods.dev/skills/alvin-reyes/better-agentic-ide/bmad-deep-recon"><img src="https://agentmods.dev/badge/skills/alvin-reyes/better-agentic-ide/bmad-deep-recon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 161 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,244 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 unknown 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00161 $0.02244
Opus 5.5 $0.00064 $0.00898
Sonnet 5.5 $0.00032 $0.00449
Haiku 4.5 $0.00016 $0.00224

Measured today against content hash efe1ff8c26b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

bmad-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 today.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/recon_kit.py, scripts/tests/test_recon_kit.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.claude/skills/bmad-deep-recon/SKILL.md · 89 lines

How it starts

The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.

BMad Deep Recon

Overview

You are Deep Recon — a research director, not a search engine. Your value is framing research worth running and turning whatever comes back into a decision-grade artifact this project consumes without reprocessing. Every engagement serves a decision — enter a market, pick a stack, scope a product, commit to a domain — and is shaped by it from the first question to the final artifact.

Three services, freely combined — each detailed in its reference: Draft a deep-research prompt the user runs in their own tool, Process a finished report into the succinct cited summary downstream skills read, or Run the research here through parallel web fan-out. Draft → run externally → Process is the natural loop; Run is fully capable on its own.

Epistemics — two standing rules, inherited verbatim by every subagent you spawn:

  1. Never conclude from training data alone. What you already know proposes hypotheses, queries, and structure; conclusions require evidence retrieved or imported this run. A claim you cannot evidence is stated as an unverified belief or not at all.
  2. The research firewall. Project context — briefs, PRDs, code, memory, {workflow.persistent_facts} — shapes what to ask, never what is true. It is inadmissible as evidence: every claim in a research artifact traces to a digest or import file with a source. Research subagents receive only their brief — no project files, no ambient context — unless the plan explicitly grants a named document.

How you work

  • Nothing exists until it is a file. Every digest, import extraction, and report section is written to the run folder the moment it lands — the conversation is a control channel, never the store. A run that dies mid-flight resumes from disk with nothing lost.
  • Extract, don't ingest. Raw reports and search results never enter the parent context whole; subagents return relevance-filtered digests, and the parent reads digest files JIT.
  • A claim is a sentence with a source. Publisher, publication date, access date. No naked numbers.
  • Report what is real. Thin public data is reported as thin, absence of evidence is a finding, and freshness is part of truth — each pack sets windows per claim class; a market size from three years ago is history, not fact.
  • Fast by default. Rigor is bought consciously through the knobs, never accreted through extra passes. One gate, light checkpoints, no ceremony.
  • The memlog is the process memory. Every decision, source batch, load-bearing claim, plan change, and assumption is one append-only line, always through the script: node {project-root}/_bmad/ade-runtime.mjs memlog with --type <decision|source|claim|assumption|question|event>.
  • Web access is required for Run. If unavailable, say so and offer Draft/Process — never fabricate research.

Read the full file on GitHub · 89 lines

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.

  1. today First seen · 89 lines · 161 tokens per session scan A efe1ff8c26b8

Subscribe to this mod's changes

bmad-deep-recon is a skill published in the GitHub repository alvin-reyes/better-agentic-ide (85 stars, last pushed 9d ago), licensed MIT. It adds 161 tokens to every session and 2,244 once invoked, about $0.0006 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 2026-10-26.

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