Borrowing it
Nothing to install: this file belongs to 2233admin/design-pipeline. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/2233admin/design-pipeline/main/.agents/skills/bmad-deep-recon/SKILL.mdgit clone --depth 1 https://github.com/2233admin/design-pipelineWrote 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/2233admin/design-pipeline/bmad-deep-recon)<a href="https://agentmods.dev/skills/2233admin/design-pipeline/bmad-deep-recon"><img src="https://agentmods.dev/badge/skills/2233admin/design-pipeline/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.
<a href="https://agentmods.dev/skills/2233admin/design-pipeline/bmad-deep-recon"><img src="https://agentmods.dev/badge/skills/2233admin/design-pipeline/bmad-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 | $0.00167 | $0.02080 |
| Opus 5 | $0.00084 | $0.01040 |
| Sonnet 5 | $0.00033 | $0.00416 |
| Haiku 4.5 | $0.00017 | $0.00208 |
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 10d 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
92% identical to bmad-deep-recon — 10 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 — 83 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:
- 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.
- 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:
uv run {project-root}/_bmad/scripts/memlog.pywith--type <decision|source|claim|assumption|question|event>. - Web access is required for Run. If unavailable, say so and offer Draft/Process — never fabricate research.
What ships with it
19 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.
- assets/research.template.md 460 B
- customize.toml 9.3 KB
- references/draft.md 1.9 KB
- references/finalize.md 1.8 KB
- references/html-briefing.md 2.4 KB
- references/lifecycle.md 1.1 KB
- references/process.md 1.9 KB
- references/run.md 9.2 KB
- references/selection.md 2.3 KB
- references/synthesis.md 2.7 KB
- references/verification.md 4.1 KB
- scripts/recon_kit.py 12 KB runs code
- scripts/tests/test_recon_kit.py 4.8 KB runs code
- types/academic-lit.md 1.5 KB
- types/competitive.md 1.5 KB
- types/domain.md 1.7 KB
- types/market.md 1.3 KB
- types/technical.md 1.6 KB
- types/user-voice.md 1.7 KB
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.
- 10d ago First seen · 83 lines · 167 tokens per session scan A dce5f7db0309
bmad-deep-recon is a skill published in the GitHub repository 2233admin/design-pipeline (9 stars, last pushed 7d ago), licensed MIT. It adds 167 tokens to every session and 2,080 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to bmad-deep-recon, differing in 10 lines, and is treated as a copy.
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