/plan

This skill is self-contained — follow the steps below instead of delegating to external planning skills (superpowers, etc.).

Research the codebase and create a spec + phased implementation plan. Zero interactive questions — explores the code instead.

When to use

Creates a track for any feature, bug fix, or refactor with a concrete, file-level implementation plan. Works with or without /setup.

MCP Tools (use if available)

• session_search(query) — find similar past work in Claude Code chat history
• project_code_search(query, project) — find reusable code across projects
• codegraph_query(query) — check dependencies of affected files
• codegraph_explain(project) — architecture overview: stack, languages, directory layers, key patterns, top dependencies, hub files
• kb_search(query) — search knowledge base for relevant methodology

If MCP tools are not available, fall back to Glob + Grep + Read.

Steps

1. Parse task description from $ARGUMENTS.
• If empty, ask via AskUserQuestion: "What feature, bug, or refactor do you want to plan?"
• This is the ONE question maximum.

1. Detect context — determine where plan files should be stored:

Project context (normal project with code):

• Detected by: package.json, pyproject.toml, Cargo.toml, *.xcodeproj, or build.gradle.kts exists in working directory
• Plan path: docs/plan/{trackId}/

Knowledge base context (documentation-centric project):

• Detected by: NO package manifest found, BUT directories like docs/, notes/, or structured numbered directories exist
• Plan path: docs/plan/{shortname}/
• Note: the shortname is derived from the task (kebab-case, no date suffix for the directory)

Set $PLAN_ROOT based on detected context. All subsequent file paths use $PLAN_ROOT.

1. Load project context (parallel reads):
• CLAUDE.md — architecture, constraints, Do/Don't
• docs/prd.md — what the product does (if exists)
• docs/workflow.md — TDD policy, commit strategy (if exists)
• package.json or pyproject.toml — stack, versions, deps

1. Auto-classify track type from keywords in task description:
• Contains "fix", "bug", "broken", "error", "crash" → bug
• Contains "refactor", "cleanup", "reorganize", "migrate" → refactor
• Contains "update", "upgrade", "bump" → chore
• Default → feature

1. Research phase — explore the codebase to understand what needs to change:

a. Get architecture overview (if MCP available — do this FIRST):

```

codegraph_explain(project="{project name from CLAUDE.md or directory name}")

```

Gives you: stack, languages, directory layers, key patterns, top dependencies, hub files.

b. Find relevant files — Glob + Grep for patterns related to the task:

• Search for keywords from the task description
• Look at directory structure to understand architecture
• Identify files that will need modification

c. Precedent retrieval (context graph pattern — search past solutions BEFORE planning):

• Search past sessions (if MCP available):

```

session_search(query="{task description keywords}")

```

Look for: how similar tasks were solved, what went wrong, what patterns worked.

• Search KB for relevant methodology:

```

kb_search(query="{task type}: {keywords}")

```

Check for: harness patterns, architectural constraints, quality scores.

d. Search code across projects (if MCP available):

```

project_code_search(query="{relevant pattern}")

```

e. Check dependencies of affected files (if MCP available):

```

codegraph_query(query="MATCH (f:File {path: '{file}'})-[:IMPORTS]->(dep) RETURN dep.path")

```

f. Read existing tests in the affected area — understand testing patterns used.

g. Read CLAUDE.md architecture constraints — understand boundaries and conventions.

• Check for harness section: module boundaries, data validation rules, lint configs.
• Read docs/ARCHITECTURE.md and docs/QUALITY_SCORE.md if they exist.

h. Detect deploy infrastructure — search for deploy scripts/configs to include deploy phase in plan:

```bash

find . -maxdepth 3 \( -name 'deploy.sh' -o -name 'Dockerfile' -o -name 'docker-compose.yml' -o -name 'fly.toml' -o -name 'wrangler.toml' \) -type f 2>/dev/null

```

If found, read them to understand deploy targets. Include a deploy phase in the plan with concrete commands.

1. Generate track ID:
• Extract a short name (2-3 words, kebab-case) from task description.
• Format: {shortname}_{YYYYMMDD} (e.g., user-auth_20260209).

1. Create track directory:

```bash

mkdir -p $PLAN_ROOT

```

• Project context: docs/plan/{trackId}/
• KB context: docs/plan/{shortname}/

1. Generate $PLAN_ROOT/spec.md:

Based on research findings, NOT generic questions.

```markdown

# Specification: {Title}

Track ID: {trackId}

Type: {Feature|Bug|Refactor|Chore}

Created: {YYYY-MM-DD}

Status: Draft

## Summary

{1-2 paragraph description based on research}

## Acceptance Criteria

• [ ] {concrete, testable criterion}
• [ ] {concrete, testable criterion}

{3-8 criteria based on research findings}

## Dependencies

• {external deps, packages, other tracks}

## Out of Scope

• {what this track does NOT cover}

## Technical Notes

• {architecture decisions from research}
• {relevant patterns found in codebase}
• {reusable code from other projects}

```

1. Generate $PLAN_ROOT/plan.md:

Concrete, file-level plan from research. Keep it tight: 2-4 phases, 5-15 tasks total.

Critical format rules (parsed by /build):

• Phase headers: ## Phase N: Name
• Tasks: - [ ] Task N.Y: Description (with period or detailed text)
• Subtasks: indented - [ ] Subtask description
• All tasks use [ ] (unchecked), [~] (in progress), [x] (done)

```markdown

# Implementation Plan: {Title}

Track ID: {trackId}

Spec: spec.md

Created: {YYYY-MM-DD}

Status: [ ] Not Started

## Overview

{1-2 sentences on approach}

## Phase 1: {Name}

{brief description of phase goal}

### Tasks

• [ ] Task 1.1: {description with concrete file paths}
• [ ] Task 1.2: {description}

### Verification

• [ ] {what to check after this phase}

## Phase 2: {Name}

### Tasks

• [ ] Task 2.1: {description}
• [ ] Task 2.2: {description}

### Verification

• [ ] {verification steps}

{2-4 phases total}

## Phase {N-1}: Deploy (if deploy infrastructure exists)

_Include this phase ONLY if the project has deploy scripts/configs (deploy.sh, Dockerfile, docker-compose.yml, fly.toml, wrangler.toml, vercel.json). Skip if no deploy infra found._

### Tasks

• [ ] Task {N-1}.1: {concrete deploy step — e.g. "Run python/deploy.sh to push Docker image to VPS", "wrangler deploy", etc.}
• [ ] Task {N-1}.2: Verify deployment — health check, logs, HTTP status

### Verification

• [ ] Service is live and healthy
• [ ] No runtime errors in production logs

## Phase {N}: Docs & Cleanup

### Tasks

• [ ] Task {N}.1: Update CLAUDE.md with any new commands, architecture changes, or key files
• [ ] Task {N}.2: Update README.md if public API or setup steps changed
• [ ] Task {N}.3: Remove dead code — unused imports, orphaned files, stale exports

### Verification

• [ ] CLAUDE.md reflects current project state
• [ ] Linter clean, tests pass

## Final Verification

• [ ] All acceptance criteria from spec met
• [ ] Tests pass
• [ ] Linter clean
• [ ] Build succeeds
• [ ] Documentation up to date

---

_Generated by /plan. Tasks marked [~] in progress and [x] complete by /build._

```

Plan quality rules:

• Every task mentions specific file paths (from research).
• Tasks are atomic — one commit each.
• Phases are independently verifiable.
• Total: 5-15 tasks (not 70).
• Last phase is always "Docs & Cleanup".
• Harness-aware: if the task introduces new patterns, include a task to update lint rules or CLAUDE.md constraints. If it touches module boundaries, include verification of dependency direction. Think: "what harness change prevents future agents from breaking this?"

1. Create progress task list for pipeline visibility:

After writing plan.md, create TaskCreate entries so progress is trackable:

• One task per phase: "Phase 1: {name}" with task list as description.
• This gives the user and pipeline real-time visibility into what's planned.
• /build will update these tasks as it works through them.

If superpowers:writing-plans skill is available, follow its granularity format: bite-sized tasks (2-5 minutes each), complete code in task descriptions, exact file paths, verification steps per task. This enhances the built-in format above.

1. Show plan for approval via AskUserQuestion:

Present the spec summary + plan overview. Options:

• "Approve and start" — ready for /build
• "Edit plan" — user wants to modify before implementing
• "Cancel" — discard the track

If "Edit plan": tell user to edit $PLAN_ROOT/plan.md manually, then run /build.

Output

Track created: {trackId}

  Type:   {Feature|Bug|Refactor|Chore}
  Phases: {N}
  Tasks:  {N}
  Spec:   $PLAN_ROOT/spec.md
  Plan:   $PLAN_ROOT/plan.md

Research findings:
  - {key finding 1}
  - {key finding 2}
  - {reusable code found, if any}

Next: /build {trackId}

Rationalizations Catalog

These thoughts mean STOP — you're skipping research:

Compatibility Notes

• Plan format must match what /build parses: ## Phase N:, - [ ] Task N.Y:.
• /build reads docs/workflow.md for TDD policy and commit strategy (if exists).
• If docs/workflow.md missing, /build uses sensible defaults (moderate TDD, conventional commits).

Common Issues

Plan has too many tasks

Cause: Feature scope too broad or tasks not atomic enough.

Fix: Target 5-15 tasks across 2-4 phases. Split large features into multiple tracks.

Context detection wrong (project vs KB)

Cause: Directory has both code manifests and KB-style directories.

Fix: Project context takes priority if package.json/pyproject.toml exists.

Research phase finds no relevant code

Cause: New project with minimal codebase or MCP tools unavailable.

Fix: Skill falls back to Glob + Grep. For new projects, the plan will rely more on CLAUDE.md architecture and stack conventions.