This file provides guidance to AI coding agents like Claude Code (claude.ai/code), Cursor AI, Codex, Gemini CLI, GitHub Copilot, and other AI coding assistants when working with code in this repository.
## Project Overview
Plan2Code is a structured 4-step workflow methodology for AI-assisted software development. It provides prompt templates that can be installed globally or per-project for various AI coding tools (Claude Code, Cursor, Copilot, Continue, Windsurf, Codeium).
**Version:** Check `version.json` for current version
A separate Node.js CLI tool that autonomously implements specs by looping through tasks.
### Loop Architecture
The loop uses an **LLM-driven discovery** approach:
- Node app just orchestrates iterations and parses completion markers
- The LLM reads spec files (`overview.md`, `phase-X.md`) to discover tasks
- The LLM finds unchecked checkboxes, implements ONE task per iteration, marks it complete
- No regex parsing of markdown in Node - the AI handles all task discovery
### Loop Commands
```bash
# Build the loop CLI
cd plan2code-loop && npm run build
# Run the loop (after linking) - fully interactive
plan2code-loop
```
The CLI auto-detects specs in `./specs/`, prompts for selection if multiple found, and handles session continuation interactively. Session state is stored per-spec in `specs/<feature>/.plan2code-loop/`.
- **One task per loop** (default) - Each agent invocation implements exactly one task. The Node controller handles git commits.
- **One phase per loop** - Each agent invocation implements all remaining tasks in the current phase. The LLM handles git commits (with JIRA ticket ID if provided). The controller parses multiple completion markers from a single iteration.
A recursive self-improvement toolchain for plan2code contributors. Collects run metrics, aggregates by prompt generation, diagnoses weak steps via AI, and proposes surgical prompt edits.
### Metrics Data Flow
```
Collect → Aggregate → Analyze → Improve → Apply
```
1.**Collector** reads project artifacts (`specs/<feature>/`) → writes `RunMetrics` JSON per run
2.**Aggregator** groups runs by prompt SHA fingerprint (cohorts) → `aggregated.json`
3.**Analyzer** invokes AI with aggregated metrics + prompt contents → diagnosis markdown
4.**Improver** invokes AI with diagnosis → validated `PromptEdit[]` proposals (char limit + verbatim checks)
The collector parses an optional `## User Feedback` table from `overview.md`:
```markdown
## User Feedback
| Field | Value |
|-------|-------|
| Rating | 8 |
| Reason | Smooth workflow |
| Went Well | Planning was thorough |
| Went Poorly | Some tasks unclear |
```
Feedback is collected during finalize (Step 5) or retroactively via the CLI. Pipe characters in values are escaped as `\|`. The aggregator computes `avg_user_rating` and `feedback_count` per cohort.
### Supported AI Agents
- **Claude Code** (recommended): `claude` CLI with `--inputFile` for prompt delivery
- **GitHub Copilot CLI**: `copilot` CLI with stdin prompt delivery
### Metric Targets
| Metric | Target | Direction |
|--------|--------|-----------|
| `avg_confidence` | ≥ 90 | higher is better |
| `avg_task_completion_rate` | ≥ 0.95 | higher is better |
| `avg_blocker_count` | ≤ 1.5 | lower is better |
| `avg_completion_marker_success_rate` | ≥ 0.95 | higher is better |
| `avg_verification_failures_found` | ≤ 1.0 | lower is better |
| `archival_success_rate` | ≥ 0.99 | higher is better |
| `avg_user_rating` | ≥ 7.0 | higher is better |
Data stored in `.plan2code-metrics/` (runs/, aggregated.json, proposals/).
- **Version sync:** When adding a new version to `CHANGELOG.md`, also update `version.json` and `package.json` to match. The installer displays the version from `version.json` in its header.
- **Loop `.gitignore` setup:** `ensureGitignore()` runs at startup in `Controller.run()` as a pre-flight step, not just inside `createTaskCommit()`. This is critical for phase mode where the Node controller doesn't handle commits — without it, `git add -A` would stage spec files.
- **Workflow file character limit:** All `src/plan2code-*.md` files must be ≤ 11,000 characters. A husky pre-commit hook enforces this. The 11,000 limit leaves buffer for platform-specific YAML headers (106-142 chars) to stay under Windsurf's 12,000 char limit.
- **Metrics internal prompts have no char limit:** Files in `plan2code-metrics/src/prompts/` are NOT subject to the 11,000 char limit — only `src/plan2code-*.md` consumer-facing prompts are.
- **User Feedback table format:** The `## User Feedback` markdown table in `overview.md` has a strict format the collector regex depends on. Field names must be exactly `Rating`, `Reason`, `Went Well`, `Went Poorly`. Pipe characters in values must be escaped as `\|`.
The project has a mascot called "Planny" - an ASCII art robot that appears in installer output and workflow prompts. Mascot variants are defined in `MASCOT` constant in `install.js` and appear in workflow markdown files.
