Agent skill

Recipe Reverse Engineer

by shinpr in shinpr/claude-code-workflows

Generate PRD and Design Docs from existing codebase through discovery, generation, verification, and review workflow

MITAuto-check passedProduct & Project Management

Install Recipe Reverse Engineer

skills CLI
$ npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install shinpr/claude-code-workflows recipe-reverse-engineer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/shinpr/claude-code-workflows.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/recipe-reverse-engineer .claude/skills/recipe-reverse-engineer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
recipe-reverse-engineer
GitHub stars
691
Token cost
~4k tokens
SKILL.md length
1,221 words
Files
1
Skills in repo
30
Repo updated
First seen
Licence
MIT

At a glance

Generate PRD and Design Docs from existing codebase through discovery, generation, verification, and review workflow

  • Works in 3 steps: Initial Configuration → PRD Generation → Design Doc Generation
  • Tasks that involve PRD writing
  • SKILL.md covers Orchestrator Definition, Step 0: Initial Configuration, Workflow Overview and Phase 1: PRD Generation, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Recipe Reverse Engineer is an agent skill from shinpr/claude-code-workflows. Generate PRD and Design Docs from existing codebase through discovery, generation, verification, and review workflow

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Product & Project Management, covering PRD writing and Architecture decision records. The repository describes itself as: Development workflows for Claude Code that keep broad exploration focused on the outcome you approved. The licence is MIT.

When your agent uses it

  • Tasks that involve PRD writing
  • Tasks that involve Architecture decision records

Example prompts

  • “/recipe-reverse-engineer”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Initial Configuration
  2. PRD Generation
  3. Design Doc Generation

What it can do on your machine

Read from SKILL.md and the folder at commit a4ecd62. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Recipe Reverse Engineer loads about 4k tokens when it runs. Until then it costs about 35 tokens; SKILL.md has 1,221 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~35
When it runs · the whole SKILL.md, loaded when a task matches
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from shinpr/claude-code-workflows at commit a4ecd62, republished under its MIT licence (© shinpr). 1,221 words, ~4,008 tokens.

Download SKILL.mdSave it as .claude/skills/recipe-reverse-engineer/SKILL.md (or your agent's skills folder).
name
recipe-reverse-engineer
description
Generate PRD and Design Docs from existing codebase through discovery, generation, verification, and review workflow
disable-model-invocation
true

Explicit User Instruction: The user explicitly instructs and authorizes every subagent call named in this recipe. Execute each applicable call when its prerequisites are met.

Execute Skill: llm-friendly-context before writing Agent prompts, handoffs, or generated artifacts. Execute Skill: subagents-orchestration-guide before making workflow decisions, invoking agents, or resolving findings.

Context: Reverse engineering workflow to create documentation from existing code

Target: $ARGUMENTS

Orchestrator Definition

Core Identity: "I am an orchestrator."

Local authority gate: Make this recipe's workflow decisions and validate each returned result directly; delegate semantic deliverable production to the named specialist.

Review Resolution Gate [MANDATORY]: Resolve every actionable deliverable-review finding through subagents-orchestration-guide Review Resolution before correction or progression. Before the first finding disposition, read references/review-resolution.md from the loaded subagents-orchestration-guide skill.

Execution Protocol:

  1. Invoke named specialists for deliverable production — pass deliverable paths between them and validate their results (see subagents-orchestration-guide "Orchestrator Execution Boundary")
  2. Process one step at a time: Execute steps sequentially within each unit (2 → 3 → 4 → 5). Each step's output is the required input for the next step. Complete all steps for one unit before starting the next
  3. Preserve evidence while bridging outputs — copy the fields required by the next specialist in their declared form; apply Review Resolution before routing any correction

At each Agent invocation below, build the prompt as a mechanical extraction: copy the named source values into the exact fields, apply only the declared serialization, then invoke immediately.

Execution Gate: Complete Phase 1 before Phase 2. Within each phase, complete one unit's generation, verification, review, and required revision to convergence before starting the next unit. Advance only when the current step's stated output and quality gate are satisfied. At each loop boundary, select the first unit in the current phase's declared order whose Unit Completion conditions are unsatisfied and that is not logged as a generation failure. A document path proves generation only.

Step 0: Initial Configuration

0.1 Scope Confirmation

Use AskUserQuestion to confirm:

  1. Target path: Which directory/module to document
  2. Depth: PRD only, or PRD + Design Docs
  3. Reference Architecture: layered / mvc / clean / hexagonal / none
  4. Human review: Yes (recommended) / No (fully autonomous)
  5. Fullstack design: Yes / No
    • Yes: For each functional unit, generate backend + frontend Design Docs
    • Note: Requires both agents (technical-designer, technical-designer-frontend)
0.2 Output Configuration
  • PRD output: docs/prd/ or existing PRD directory
  • Design Doc output: docs/design/ or existing design directory
  • Verify directories exist, create if needed

Workflow Overview

Phase 1: PRD Generation
  Step 1: Scope Discovery (unified, single pass → group into PRD units → human review)
  Step 2-5: Per-unit loop (Generation → Verification → Review → Revision)

Phase 2: Design Doc Generation (if requested)
  Step 6: Design Doc Scope Mapping (reuse Step 1 results, no re-discovery)
  Step 7-10: Per-unit loop (Generation → Verification → Review → Revision)
  ※ fullstack=Yes: each unit produces backend + frontend Design Docs

Phase 1: PRD Generation

Step 1: PRD Scope Discovery

Agent tool invocation:

subagent_type: dev-workflows:scope-discoverer
description: "Discover functional scope"
prompt: |
  Discover functional scope targets in the codebase.

  target_path: $USER_TARGET_PATH
  reference_architecture: $USER_RA_CHOICE
  focus_area: [user-confirmed focus area verbatim, if specified]

Store output as: $STEP_1_OUTPUT

Quality Gate:

  • At least one unit discovered → proceed
  • No units discovered → ask user for hints
  • $STEP_1_OUTPUT.prdUnits exists
  • All sourceUnits across prdUnits (flattened, deduplicated) match the set of discoveredUnits IDs — no unit missing, no unit duplicated
  • Each discovered unit's unitInventory has at least one non-empty category (routes, testFiles, or publicExports). Units with all three empty indicate incomplete discovery — re-run scope-discoverer with focus on that unit's relatedFiles

Human Review Point (if enabled): Present $STEP_1_OUTPUT.prdUnits with their source unit mapping. The user confirms, adjusts grouping, or excludes units from scope. This is the most important review point — incorrect grouping cascades into all downstream documents.

Step 2-5: Per-Unit Processing

FOR each unit in $STEP_1_OUTPUT.prdUnits (sequential, one unit at a time):

Step 2: PRD Generation

Agent tool invocation:

subagent_type: dev-workflows:prd-creator
description: "Generate PRD"
prompt: |
  Create reverse-engineered PRD for the following feature.

  Operation Mode: reverse-engineer
  External Scope Provided: true

  Feature: $PRD_UNIT_NAME (current Step 1 PRD unit name unchanged)
  Description: $PRD_UNIT_DESCRIPTION (current Step 1 PRD unit description unchanged)
  Related Files: $PRD_UNIT_COMBINED_RELATED_FILES
  Entry Points: $PRD_UNIT_COMBINED_ENTRY_POINTS

  Use provided scope as investigation starting point.
  If tracing entry points reveals files outside this scope, include them.
  Create final version PRD based on thorough code investigation.

Store output as: $STEP_2_OUTPUT (PRD path)

Step 3: Code Verification

Prerequisite: $STEP_2_OUTPUT (PRD path from Step 2)

Agent tool invocation:

subagent_type: dev-workflows:code-verifier
description: "Verify PRD consistency"
prompt: |
  Verify consistency between PRD and code implementation.

  doc_type: prd
  document_path: $STEP_2_OUTPUT
  unit_inventory: [the current unit's Step 1 unitInventory]

unit_inventory supplies the completeness baseline while repository evidence supplies the search scope.

Store output as: $STEP_3_OUTPUT

Quality Gate:

  • summary.status is blocked, inventory coverage is missing, or counts do not balance → re-run or escalate with the exact unusable input or evidence
  • Any balanced non-blocked result → proceed to review with the complete verifier output; needs_review / inconsistent and any unaccounted items become explicit review evidence
Step 4: Review

Required Input: $STEP_3_OUTPUT (verification JSON from Step 3)

Agent tool invocation:

subagent_type: dev-workflows:document-reviewer
description: "Review PRD"
prompt: |
  Review the following PRD considering code verification findings.

  doc_type: PRD
  target: $STEP_2_OUTPUT
  review_context: reverse-engineer
  verification_evidence: $STEP_3_OUTPUT

Store output as: $STEP_4_OUTPUT

Step 5: Revision (conditional)

Pass $STEP_3_OUTPUT to document-reviewer as verification evidence, then apply the Review Resolution Gate to $STEP_4_OUTPUT. Run revision only when at least one finding is apply; a decline-only result completes the review.

Agent tool invocation:

subagent_type: dev-workflows:prd-creator
description: "Revise PRD"
prompt: |
  Update PRD based on review feedback and code verification results.

  Operation Mode: update
  Existing PRD: $STEP_2_OUTPUT

  ## Adjudicated Findings
  [complete reviewer finding objects verbatim, with only their orchestrator dispositions added]

  Treat these findings as the complete revision scope and preserve adjacent content.

Re-validation: After each revision, re-run code-verifier on the revised document with the original unit_inventory, then re-run document-reviewer with the latest verification_evidence and prior_feedback.

Unit Completion
  • No apply findings remain
  • Human review passed (if enabled in Step 0)

Next: Proceed to next unit. After all units → Phase 2.

Phase 2: Design Doc Generation

Execute only if Design Docs were requested in Step 0

Show full SKILL.md (484 more words)Show less
Step 6: Design Doc Scope Mapping

No additional discovery required. Use $STEP_1_OUTPUT.discoveredUnits (implementation-granularity units) for technical profiles. Use $STEP_1_OUTPUT.prdUnits[].sourceUnits to trace which discovered units belong to each PRD unit.

Each PRD unit from Phase 1 maps to Design Doc unit(s):

  • Standard mode (fullstack=No): 1 PRD unit → 1 Design Doc (using technical-designer)
  • Fullstack mode (fullstack=Yes): 1 PRD unit → 2 Design Docs (technical-designer + technical-designer-frontend)

Map $STEP_1_OUTPUT units to Design Doc generation targets, carrying forward:

  • technicalProfile.primaryModules → Primary Files
  • technicalProfile.publicInterfaces → Public Interfaces
  • dependencies → Dependencies
  • relatedFiles → Scope boundary
  • unitInventory → Unit Inventory (routes, test files, public exports)

In fullstack mode, partition each unit inventory by the owning path into backend and frontend target inventories. Assign a shared entry to each Design Doc whose public contract must account for it and record that shared reason; otherwise assign it once. Each Step 7 and Step 8 invocation receives its target's inventory, not the unpartitioned combined unit.

Store output as: $STEP_6_OUTPUT

Step 7-10: Per-Unit Processing

FOR each unit in $STEP_6_OUTPUT (sequential, one unit at a time):

Step 7: Design Doc Generation

Scope: Document the current architecture exactly as implemented in code.

Standard mode (fullstack=No):

Agent tool invocation:

subagent_type: dev-workflows:technical-designer
description: "Generate Design Doc"
prompt: |
  Create Design Doc for the following feature based on existing code.

  Operation Mode: reverse-engineer

  Feature: $UNIT_NAME (current Step 6 target name unchanged)
  Description: $UNIT_DESCRIPTION (current Step 6 target description unchanged)
  Primary Files: $UNIT_PRIMARY_MODULES
  Public Interfaces: $UNIT_PUBLIC_INTERFACES
  Dependencies: $UNIT_DEPENDENCIES
  Unit Inventory: $DESIGN_DOC_UNIT_INVENTORY

  Parent PRD: $APPROVED_PRD_PATH

  Document current architecture as-is. Use Unit Inventory as a completeness baseline — all routes and exports should be accounted for in the Design Doc.

Store output as: $STEP_7_OUTPUT

Fullstack mode (fullstack=Yes):

For each unit, invoke 7a then 7b sequentially (7b depends on 7a output):

7a. Backend Design Doc:

subagent_type: dev-workflows:technical-designer
description: "Generate backend Design Doc"
prompt: |
  Create a backend Design Doc for the following feature based on existing code.

  Operation Mode: reverse-engineer

  Feature: $UNIT_NAME (current Step 6 target name unchanged)
  Description: $UNIT_DESCRIPTION (current Step 6 target description unchanged)
  Primary Files: $UNIT_PRIMARY_MODULES
  Public Interfaces: $UNIT_PUBLIC_INTERFACES
  Dependencies: $UNIT_DEPENDENCIES
  Unit Inventory: $BACKEND_UNIT_INVENTORY

  Parent PRD: $APPROVED_PRD_PATH

  Focus on: API contracts, data layer, business logic, service architecture.
  Document current architecture as-is. Use Unit Inventory as completeness baseline.

Store output as: $STEP_7a_OUTPUT

7b. Frontend Design Doc:

subagent_type: dev-workflows-frontend:technical-designer-frontend
description: "Generate frontend Design Doc"
prompt: |
  Create a frontend Design Doc for the following feature based on existing code.

  Operation Mode: reverse-engineer

  Feature: $UNIT_NAME (current Step 6 target name unchanged)
  Description: $UNIT_DESCRIPTION (current Step 6 target description unchanged)
  Primary Files: $UNIT_PRIMARY_MODULES
  Public Interfaces: $UNIT_PUBLIC_INTERFACES
  Dependencies: $UNIT_DEPENDENCIES
  Unit Inventory: $FRONTEND_UNIT_INVENTORY

  Parent PRD: $APPROVED_PRD_PATH
  Backend Design Doc: $STEP_7a_OUTPUT

  Reference backend Design Doc for API contracts.
  Focus on: component hierarchy, state management, UI interactions, data fetching.
  Document current architecture as-is. Use Unit Inventory as completeness baseline.

Store output as: $STEP_7b_OUTPUT

Step 8: Code Verification

Standard mode: Verify $STEP_7_OUTPUT.

Fullstack mode: Verify each Design Doc separately.

Agent tool invocation (per Design Doc):

subagent_type: dev-workflows:code-verifier
description: "Verify Design Doc consistency"
prompt: |
  Verify consistency between Design Doc and code implementation.

  doc_type: design-doc
  document_path: $STEP_7_OUTPUT (or $STEP_7a_OUTPUT / $STEP_7b_OUTPUT)
  unit_inventory: [the current Design Doc target's Step 6 unitInventory]

Store output as: $STEP_8_OUTPUT

Verification gate (per Design Doc):

  • blocked, missing inventoryCoverage, or unbalanced category counts → correct the invocation/input and rerun; stop for the user only when repository evidence cannot resolve the input defect.
  • Any balanced non-blocked result proceeds to document review. Carry needs_review, inconsistent, and every unaccounted item as explicit verifier evidence.
Step 9: Review

Required Input: $STEP_8_OUTPUT (verification JSON from Step 8)

Agent tool invocation (per Design Doc):

subagent_type: dev-workflows:document-reviewer
description: "Review Design Doc"
prompt: |
  Review the following Design Doc considering code verification findings.

  doc_type: DesignDoc
  target: $STEP_7_OUTPUT (or $STEP_7a_OUTPUT / $STEP_7b_OUTPUT)
  review_context: reverse-engineer
  verification_evidence: $STEP_8_OUTPUT

  ## Parent PRD
  $APPROVED_PRD_PATH

  ## Additional Review Focus
  - Technical accuracy of documented interfaces
  - Consistency with parent PRD scope
  - Completeness of unit boundary definitions

Store output as: $STEP_9_OUTPUT

Step 10: Revision (conditional)

Pass $STEP_8_OUTPUT to document-reviewer as verification evidence, then apply the Review Resolution Gate to $STEP_9_OUTPUT. Run revision only when at least one finding is apply; a decline-only result completes the review.

Agent tool invocation (per Design Doc):

subagent_type: dev-workflows:technical-designer (or dev-workflows-frontend:technical-designer-frontend for frontend Design Docs)
description: "Revise Design Doc"
prompt: |
  Update Design Doc based on review feedback and code verification results.

  Operation Mode: update
  Existing Design Doc: $STEP_7_OUTPUT (or $STEP_7a_OUTPUT / $STEP_7b_OUTPUT)

  ## Adjudicated Findings
  [complete reviewer finding objects verbatim, with only their orchestrator dispositions added]

  Treat these findings as the complete revision scope and preserve adjacent content.

Re-validation: After each revision, re-run code-verifier on the revised document with the original unit_inventory, then re-run document-reviewer with the latest verification_evidence and prior_feedback.

Unit Completion
  • No apply findings remain
  • Human review passed (if enabled in Step 0)

Next: Proceed to next unit. After all units → Final Report.

Final Report

Output summary including:

  • Generated documents table (Type, Name, Verification Status, Review Status)
  • Action items (undocumented features, flagged items)
  • Declined actionable findings with ID, governing reason, and evidence, when any occurred
  • Next steps checklist

Error Handling

ErrorAction
Discovery finds nothingAsk user for project structure hints
Generation failsLog failure, continue with other units, report in summary
Verification is inconsistent or inventory remains unaccounted after correctionFlag for mandatory human review — require explicit human approval

© shinpr, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/recipe-reverse-engineer of shinpr/claude-code-workflows.

Open the folder on GitHubat commit a4ecd62

Compare with similar skills

Recipe Reverse Engineer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Recipe Reverse Engineer

What does Recipe Reverse Engineer do?

Generate PRD and Design Docs from existing codebase through discovery, generation, verification, and review workflow. Recipe Reverse Engineer is an agent skill from shinpr/claude-code-workflows.

When should I use Recipe Reverse Engineer?

Recipe Reverse Engineer fits situations like: tasks that involve PRD writing; tasks that involve Architecture decision records.

How do I install Recipe Reverse Engineer in Claude Code?

Run `npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a claude-code`. Or copy the skill folder (skills/recipe-reverse-engineer in shinpr/claude-code-workflows) into .claude/skills/recipe-reverse-engineer in your project. Claude Code loads it when a task matches its description.

How do I install Recipe Reverse Engineer in Codex?

Run `npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a codex`. Or copy the skill folder (skills/recipe-reverse-engineer in shinpr/claude-code-workflows) into .agents/skills/recipe-reverse-engineer in your project. Codex loads it when a task matches its description.

Can I use Recipe Reverse Engineer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/recipe-reverse-engineer, .gemini/skills/recipe-reverse-engineer, .github/skills/recipe-reverse-engineer and .opencode/skills/recipe-reverse-engineer in your project.

What does Recipe Reverse Engineer need to run?

SKILL.md names no scripts, command-line tools or credentials: Recipe Reverse Engineer is instructions for the agent only.

Does Recipe Reverse Engineer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Recipe Reverse Engineer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Recipe Reverse Engineer use?

Recipe Reverse Engineer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Recipe Reverse Engineer use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Recipe Reverse Engineer?

Skills that share tags, products or a category with Recipe Reverse Engineer: Cabloy Spec Generation (cabloy/cabloy, 982 stars), Schematic (blader/schematic, 240 stars), Shep Workstreams (shep-ai/shep, 264 stars) and Write Update Tidb Docs (pingcap/docs, 616 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Recipe Reverse Engineer?

shinpr (a GitHub user) maintains it in shinpr/claude-code-workflows, which has 691 GitHub stars. The repository holds 30 skills in this directory. The repository was last updated on October 1, 2026.

Source: shinpr/claude-code-workflows on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.