Blueprint Derive Plans

/blueprint:derive-plans

Retroactively generate Blueprint documentation (PRDs, ADRs, PRPs) from an existing established project by analyzing git history, codebase structure, and existing documentation.

Use case: Onboarding established projects into the Blueprint Development system when PRD/ADR/PRP documents don't exist but the project has implementation history.

When to Use This Skill

Context

• Git repository: !git rev-parse --git-dir
• Blueprint initialized: !find . -path '*/docs/blueprint/*' -maxdepth 3 -name 'manifest.json' -type f
• Total commits: !git rev-list --count HEAD
• First commit: !git log --reverse --format=%ai --max-count=1
• Latest commit: !git log --max-count=1 --format=%ai
• Project type: !find . -maxdepth 1 \( -name 'package.json' -o -name 'pyproject.toml' -o -name 'Cargo.toml' -o -name 'go.mod' -o -name 'pom.xml' \) -type f -print -quit
• Documentation files: !find . -maxdepth 2 \( -name "README.md" -o -name "ARCHITECTURE.md" -o -name "DESIGN.md" \)

Parameters

Parse these from $ARGUMENTS:

• --quick: Fast scan (last 50 commits only)
• --since DATE: Analyze commits from specific date (e.g., --since 2024-01-01)

Default behavior without flags: Standard analysis (last 200 commits with scope estimation).

For detailed templates, manifest format, and report examples, see REFERENCE.md.

Execution

Execute this retroactive documentation generation workflow:

Step 1: Verify prerequisites

Check context values above:

1. If git repository = "NO" → Error: "This directory is not a git repository. Run from project root."
2. If total commits = "0" → Error: "Repository has no commit history"
3. If Blueprint initialized = "NO" → Ask user: "Blueprint not initialized. Initialize now (Recommended) or minimal import only?"
   • If "Initialize now" → Use Task tool to invoke /blueprint:init, then continue with this step 1
   • If "Minimal import only" → Create minimal directory structure: mkdir -p docs/prds docs/adrs docs/prps

Step 2: Determine analysis scope

Parse $ARGUMENTS for --quick or --since:

1. If --quick flag present → scope = last 50 commits
2. If --since DATE present → scope = commits from DATE to now
3. Otherwise → Present options to user:
   • Quick scan (last 50 commits)
   • Standard analysis (last 200 commits)
   • Full history analysis (all commits)
   • Custom date range

Use selected scope for all subsequent git analysis.

Step 3: Analyze git history quality

For commits in scope, calculate:

1. Count total commits: git log --oneline {scope} | wc -l
2. Count conventional commits: git log --format="%s" {scope} | grep -cE "^(feat|fix|docs|style|refactor|perf|test|build|ci|chore|revert)\(?.*\)?:" || echo 0
3. Calculate percentage and assign quality score (see REFERENCE.md)

Report: "Git history quality: {score}/10 ({percentage}% conventional commits)"

Step 4: Extract features and architecture decisions

Using methods from REFERENCE.md:

1. Extract feature boundaries from conventional commit scopes
2. Identify architecture decisions (migrations, major dependencies, breaking changes)
3. Find issue references and future work items (TODOs, skipped tests)
4. Identify release boundaries from git tags

Collect findings in structured format for user confirmation.

Step 5: Analyze codebase and existing documentation

1. Use Explore agent to analyze architecture: directory structure, components, frameworks, patterns, entry points, data layer, API layer, testing structure
2. Extract dependencies from manifest files (package.json, pyproject.toml, Cargo.toml, go.mod, etc.)
3. Read and extract from existing documentation: README.md, docs/, ARCHITECTURE.md, DESIGN.md, CONTRIBUTING.md
4. Detect future work: TODOs in code, open GitHub issues, skipped tests

Step 6: Clarify project context with user

Ask for clarifications via AskUserQuestion. For the full question templates (problem statement, target users, project phase, stakeholder scale), see REFERENCE.md.

1. Project purpose (if not clear from README): present the inferred description for confirmation, or ask the user to provide one
2. Target users: developers, end users, or both — steers the PRD's framing
3. Project phase: MVP / active development / maintenance / planning major changes — sets feature-vs-stability emphasis
4. Stakeholders: scale (solo / small team / larger org / OSS community) → drives the depth of the PRD's stakeholder matrix (see REFERENCE.md)
5. Feature confirmation: present {N} features extracted from git for review/prioritization
6. Architecture rationale: for each identified decision, ask the main driver
7. Generation confirmation: show the summary below and ask if ready to generate

For the confirmation step, present:

• Git history quality: {score}/10
• Features identified: {N}
• Architecture decisions: {N}
• Future work items: {N}
• Proposed documents: PRD (with stakeholder matrix), {N} ADRs, {N} PRPs

Step 7: Generate documents

Create directory structure: mkdir -p docs/prds docs/adrs docs/prps

For each document type, use templates and patterns from REFERENCE.md:

1. Generate PRD as docs/prds/project-overview.md

   • Use sections and structure from REFERENCE.md
   • Include extracted features with priorities and sources
   • Mark sections with confidence scores

2. Generate ADRs as docs/adrs/{NNNN}-{title}.md (one per decision)

   • Use ADR template from REFERENCE.md
   • Include git evidence (commit SHA, date, files changed)
   • Mark with confidence score
   • Conflict & supersede check: after writing the ADRs, run /blueprint:adr-relationships to detect same-domain conflicts and /blueprint:adr-validate to enforce bidirectional supersede consistency. Do not re-implement conflict scoring here — those skills own it.

3. Create ADR index at docs/adrs/README.md

   • Table of all ADRs with status and dates
   • Link to MADR template for new ADRs

4. Generate PRPs as docs/prps/{feature}.md (one per future work item)

   • Use PRP template from REFERENCE.md
   • Include source reference and confidence score
   • Suggest implementation based on codebase patterns

Step 8: Update manifest and report results

1. Update docs/blueprint/manifest.json with import metadata: timestamp, commits analyzed, confidence scores, generated artifacts

Step 8.5: Offer GitHub issues for generated docs (optional)

For the generated PRD (and optionally each PRP), prompt with AskUserQuestion whether to open a tracking GitHub issue. See REFERENCE.md for the prompt template and the gh issue create command. When the user accepts:

1. Create the issue with title [{PRD-NNN}] {Project Name}.
2. Add the issue number to the doc's github-issues frontmatter.
3. Record it in the manifest id_registry (documents[ID].github_issues and the github_issues map).

Skip silently when no gh remote is configured or the user declines.

Step 9: Update task registry

Update the task registry entry in docs/blueprint/manifest.json:

jq --arg now "$(date -u +%Y-%m-%dT%H:%M:%SZ)" \
  --arg sha "$(git rev-parse HEAD 2>/dev/null)" \
  --argjson analyzed "${COMMITS_ANALYZED:-0}" \
  --argjson created "${DOCS_GENERATED:-0}" \
  '.task_registry["derive-plans"].last_completed_at = $now |
   .task_registry["derive-plans"].last_result = "success" |
   .task_registry["derive-plans"].context.commits_analyzed_up_to = $sha |
   .task_registry["derive-plans"].context.commits_analyzed_count = $analyzed |
   .task_registry["derive-plans"].stats.runs_total = ((.task_registry["derive-plans"].stats.runs_total // 0) + 1) |
   .task_registry["derive-plans"].stats.items_processed = $analyzed |
   .task_registry["derive-plans"].stats.items_created = $created' \
  docs/blueprint/manifest.json > tmp.json && mv tmp.json docs/blueprint/manifest.json

2. Create summary report showing:

   • Commits analyzed with date range
   • Git quality score
   • Documents generated (PRD, ADR count, PRP count)
   • Sections needing review (confidence < 7)
   • Recommended next steps

3. Prompt user for next action:

   • Review and refine documents
   • Generate project rules from PRD
   • Generate workflow commands
   • Exit (documents saved)

Agentic Optimizations

For detailed templates, git analysis patterns, document generation examples, and error handling guidance, see REFERENCE.md.