design-pipeline: Skill for Claude Code

.agents/skills/bmad-deep-recon/SKILL.md

bmad-deep-recon is a skill for Claude Code, Codex from 2233admin/design-pipeline. It costs 167 tokens per session (2,080 once invoked), scanned A, a copy of bmad-deep-recon, MIT.

A research workflow for framing important questions, processing completed research, or running research to support a project decision.

In plain words
What is it for?
Use it when deciding on a market, technology stack, product scope, or other major direction that needs documented evidence.
Why use it?
It turns scattered research into a concise, cited summary that later workflows can use without repeating the work. Research findings are based on evidence gathered for the current task.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents; installed under .agents/ (shared by several agents).

This is 2233admin/design-pipeline's own configuration. It tells Claude Code and Codex how to work on design-pipeline itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything design-pipeline configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/2233admin/design-pipeline/main/.agents/skills/bmad-deep-recon/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/2233admin/design-pipeline

Made for: Claude Code, Codex.

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/2233admin/design-pipeline/bmad-deep-recon/github.svg)](https://agentmods.dev/skills/2233admin/design-pipeline/bmad-deep-recon)
Your own site
<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.

agentmods 80×15 button for bmad-deep-recon

Your own site · 80×15
<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>
Per session 167 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,080 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 92% copy Near-identical to another mod 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 $0.00167 $0.02080
Opus 5 $0.00084 $0.01040
Sonnet 5 $0.00033 $0.00416
Haiku 4.5 $0.00017 $0.00208

Measured 10d ago against content hash dce5f7db0309, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 10d ago.

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.

Origin

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.

.agents/skills/bmad-deep-recon/SKILL.md · 83 lines

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:

  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: uv run {project-root}/_bmad/scripts/memlog.py 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 · 83 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. 10d ago First seen · 83 lines · 167 tokens per session scan A dce5f7db0309

Subscribe to this mod's changes

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.