pi-map/design-doc.md

# Design Doc: Hierarchical Project Analysis Skill for Pi

## 1. Goals and Success Criteria

### Primary Goal
Enable a Pi coding agent to understand a software project's architecture and code relationships without scanning the entire repository. The agent should have a compact, hierarchical "internal representation" of the project that it can consume in-context.

### Success Criteria
- The agent can orient itself in a new or familiar project without reading dozens of source files.
- The agent understands cross-package dependencies, data flows, and architectural patterns from the analysis files alone.
- Analysis files stay sufficiently fresh that the agent does not make decisions based on stale information.
- The representation is token-dense: maximum information per token, optimized for LLM consumption, not human readability.

## 2. Format Specification: Dense Markdown with Conventions

### Design Rationale
- **Not JSON/YAML**: Brackets, quotes, and indentation add token overhead with no benefit to LLM comprehension.
- **Not a custom DSL**: Fragile, requires a parser, and LLMs may hallucinate syntax.
- **Dense markdown**: Hierarchical headings, bullet points, and abbreviations are natively understood by LLMs and extremely token-efficient.

### Structure
Each directory in the project gets one analysis file named `.pi-map.md` (hidden by default, excluded from git via `.gitignore`).

```markdown
# <relative-path>
## role
<one-line package role> | Dep: <comma-separated upstream deps>
## files
- <filename> | <one-line purpose> | exp: <exported symbols> | dep: <internal/external deps>
- <filename> | <one-line purpose> | exp: <exported symbols> | dep: <internal/external deps>
## arch
<free-form architectural notes: patterns, data flow, invariants, design decisions>
## dirty
<timestamp or flag indicating staleness>
```

### Abbreviation Conventions
| Abbreviation | Meaning |
|-------------|---------|
| `exp:` | exported symbols (functions, classes, types, constants) |
| `dep:` | dependencies (other packages, files, or external libs) |
| `pkg/` | project-internal package reference |
| `ext/` | external dependency reference |
| `->` | data flow direction |
| `|` | field delimiter within a line |

### Example

```markdown
# pkg/auth
## role
Auth layer: JWT issuance, validation, refresh. Stateless. Dep: pkg/crypto, pkg/db.
## files
- tokens.ts | JWT gen/val | exp: issueToken, verifyToken, refreshToken | dep: crypto/hmac, db/sessions
- middleware.ts | HTTP auth guard | exp: requireAuth, requireRole | dep: tokens/verifyToken
- types.ts | shared auth types | exp: AuthToken, UserClaims, Role
## arch
Guard pattern on routes. Tokens short-lived (15m), refresh long-lived (7d). Rotation on every use.
Session state stored in Redis via db/sessions. No server-side JWT storage.
## dirty
-
```

### Rules
- One file per directory, placed inside that directory.
- Every non-excluded file in the directory gets one bullet under `## files`.
- Subdirectories are referenced in `## role` via `Dep:` or in `## arch` as structural notes, not duplicated.
- The `## dirty` section is empty (`-`) when clean, or contains a timestamp/flag when stale.

## 3. Pipeline Architecture

### Hybrid Extraction: LLM + AST

Two independent extraction layers contribute to the same output file.

#### Layer 1: LLM-Based Extraction (All Files)
- **Input**: Raw file contents of every non-excluded file in the directory.
- **Output**: Purpose description, architectural role, and cross-file relationships.
- **Applies to**: Code files, config files, Dockerfiles, READMEs, YAML, JSON, shell scripts — everything.
- **When it runs**: Once per file during init; again on changed files during patching.
- **Implementation**: Calls an actual LLM (not regex heuristics). Inside Pi, it uses Pi's built-in LLM via the ExtensionAPI. Standalone CLI falls back to an external LLM API (OpenAI-compatible).

#### Layer 2: AST-Based Extraction (Code Files Only)
- **Input**: Source code of files where a tree-sitter or LSP parser is available.
- **Output**: Precise symbol lists (functions, classes, types), signatures, import/export graphs, class hierarchies.
- **Applies to**: Supported languages only (TypeScript, Python, Go, Rust, etc.).
- **When it runs**: Once per file during init; again on changed files during patching.

#### Merging
The two layers merge into a single line per file under `## files`:

```
- tokens.ts | JWT gen/val | exp: issueToken, verifyToken, refreshToken | dep: crypto/hmac, db/sessions
  ^ LLM       ^ LLM         ^ AST                                      ^ AST + LLM
```

- File name and purpose: LLM.
- Exported symbols and signatures: AST (augmented by LLM if AST unavailable).
- Dependency list: AST for imports; LLM for inferred architectural dependencies.

### LLM Client Architecture

The LLM client is abstracted behind a unified interface:

```typescript
interface LLMClient {
  complete(prompt: string): Promise<string>;
}
```

Two implementations:

1. **PiLLMClient** (Pi extension): Uses `ctx.model` or `ctx.modelRegistry` to invoke Pi's configured LLM. Called from `pi-extension.ts` when the skill runs inside Pi.
2. **ExternalLLMClient** (standalone CLI): Calls an external OpenAI-compatible API. Configured via environment variable (e.g., `OPENAI_API_KEY`) or config file.

### Caching

LLM results are cached to avoid re-querying unchanged files.

- **Key**: SHA-256 hash of file contents.
- **Storage**: JSON file at `~/.cache/pi-project-map/llm-cache.json`.
- **Behavior**: Before calling the LLM, compute the file hash and check the cache. If hit, reuse the cached result. If miss, call the LLM and store the result.
- **Invalidation**: Cache entries are implicitly invalidated when the file content changes (because the hash changes). There is no TTL; the cache is append-only.

### Parallelization and Rate Limiting

- **Concurrency**: 4-8 LLM calls in parallel, controlled by `p-limit`.
- **Batch delays**: A small delay (e.g., 100ms) is inserted between batches to avoid triggering rate limits.
- **Retry policy**: Each LLM call retries up to 3 times with exponential backoff (1s, 2s, 4s). If all retries fail, the entire operation stops with a hard error.

### Error Handling

- **Hard error on failure**: If an LLM call fails after all retries, `init` or `patch` stops immediately and prints a clear error. There is no heuristic fallback. The user must resolve the issue (set API key, wait for rate limit, check network).
- **Context limit protection**: Files larger than the LLM's context window are truncated from the end (with a note in the prompt) before being sent.

### Init Pipeline
```
For each directory (depth-first):
  1. List all non-excluded files.
  2. For each file (parallel, 4-8 concurrent):
     a. Compute SHA-256 of file contents.
     b. Check disk cache. If hit, use cached result.
     c. If miss: call LLM (with retries/backoff) to extract purpose and role.
     d. Store result in cache.
     e. If code file + parser available: run AST extraction (symbols, imports).
  3. Merge per-file outputs into lines.
  4. Run LLM on merged lines + directory context to generate:
     - `## role` (package-level summary)
     - `## arch` (architectural notes)
  5. Write `.pi-map.md` to directory.
```

### Patch Pipeline
```
When agent edits file(s) in directory:
  1. Determine patch strategy:
     - If directory has < 10 files: full rewrite.
     - Else: section-level patch for changed file(s) only.
  2. For each changed file:
     a. Recompute SHA-256.
     b. Check cache. If miss or stale, call LLM with retries/backoff.
  3. Re-run AST extraction on changed file(s) if applicable.
  4. Update `## files` section (rewrite or patch).
  5. Update `## dirty` flag if full regeneration is deferred.
```

## 4. LLM Prompt Design

### File-Level Prompt

The LLM prompt for a single file is designed to produce a structured, concise analysis.

```
You are analyzing a source file for a project map. Read the file below and summarize:

1. PURPOSE: What does this file do? Describe its role in the project (2-3 sentences max).
2. DEPENDENCIES: What does this file depend on? List internal modules/packages and external libraries.
3. KEY CONCEPTS: Mention any important patterns, algorithms, or domain concepts.

File path: <file-path>

```
<file-contents-truncated>
```

Respond in this exact format:
PURPOSE: <concise description>
DEPS: <comma-separated list, or "none">
CONCEPTS: <comma-separated list, or "none">
```

### Package-Level Prompt

After all file summaries are collected for a directory, a second LLM call synthesizes the package role and architecture.

```
You are analyzing a directory in a software project. Below is a list of files in this directory with their purposes.

Directory: <dir-path>
Files:
- <file1>: <purpose1>
- <file2>: <purpose2>
...

Respond in this exact format:
ROLE: <one-line description of this directory's role in the project>
ARCH: <2-4 sentences describing architecture, data flow, patterns, and design decisions>
```

### Output Parsing

The LLM client's response is parsed to extract `PURPOSE`, `DEPS`, `CONCEPTS`, `ROLE`, and `ARCH` fields. These are merged with AST data into the final `.pi-map.md` format.

### Context Limit Protection

- Files are truncated from the end if they exceed a configurable max token budget (default: 4000 tokens of source).
- A marker `[...truncated]` is appended to the truncated content so the LLM knows it is not seeing the full file.
- Very large binary or generated files are skipped entirely for LLM analysis (they still appear in `.pi-map.md` with a note like "Large/generated file").

## 5. Consumption Model

### Session Start
1. Agent discovers all `.pi-map.md` files (e.g., via `find . -name ".pi-map.md"`).
2. Agent reads **all** files into context. This is a one-time cost at session start.
3. Agent constructs an internal mental model of the project hierarchy.

### During Session
- An **auto-injected summary** stays in context (e.g., a condensed top-level `.pi-map.md` or a synthesized project overview).
- When the agent needs deeper detail about a specific package, it already has the full `.pi-map.md` in memory from step 2.
- If the agent enters a new package not yet loaded, it reads that package's `.pi-map.md` on demand.

### Context Management
- For very large projects, the agent may summarize or prune the initial read, keeping only the top N levels of the hierarchy in active context.
- The skill can provide a "context budget" parameter: max tokens to spend on analysis files.

## 6. Stale Data Mitigation

### Combined Strategy

#### 5.1 Dirty Markers
- Whenever the agent edits a file, it appends a dirty flag to the directory's `.pi-map.md`:
  ```markdown
  ## dirty
  2024-06-09T14:32:00Z: tokens.ts modified
  ```
- A background or post-session reconciliation step regenerates dirty files.
- The agent can also be instructed to reconcile before making architectural decisions.

#### 5.2 Periodic Full Re-init
- On every new session start, or on a configurable schedule (e.g., daily), the skill offers to run a full re-scan.
- This catches any changes made outside the agent's awareness (e.g., by other developers).

#### 5.3 Validation Command
- A `validate` tool/command that the agent can invoke:
  - Checks for missing files (new files not in `.pi-map.md`).
  - Checks for orphaned entries (files listed but deleted).
  - Checks for changed signatures (AST mismatch between listed symbols and actual code).
  - Reports discrepancies and suggests corrections.

### Recovery
- If validation finds staleness beyond a threshold (e.g., > 3 dirty packages), the skill recommends a full re-init.
- The agent can also trigger re-init for a specific subtree.

## 7. Scope Boundaries and Non-Goals

### In Scope
- Every directory in the project gets a `.pi-map.md` file.
- Every non-excluded file gets analyzed by the LLM layer.
- Code files get augmented by the AST layer where parsers exist.
- Respect `.gitignore` and known junk patterns (node_modules, .git, dist, build, coverage, .next, .venv, __pycache__, .DS_Store).

### Out of Scope (Non-Goals)
- **Human-readable documentation**: These files are machine-only. Human docs live elsewhere.
- **Line-by-line code explanation**: The format captures symbols and architecture, not implementation details.
- **Auto-regeneration on filesystem events**: The skill relies on agent-initiated updates and periodic re-init, not filesystem watchers.
- **Cross-project analysis**: Each project is independent. No global index across repos.
- **IDE integration**: This is a Pi agent skill, not a VS Code extension or LSP server.

## 8. Pi Skill Package Structure

```
pi-project-map/
├── SKILL.md                 # Skill definition for Pi
├── package.json             # npm package metadata
├── src/
│   ├── init.ts              # Full project scan + generation
│   ├── patch.ts             # Incremental patch logic
│   ├── validate.ts          # Consistency checker
│   ├── ast-extract.ts       # Tree-sitter / LSP wrappers
│   ├── llm-extract.ts       # LLM prompt templates for extraction
│   ├── merge.ts             # Merge AST + LLM outputs
│   ├── format.ts            # Dense markdown formatter
│   └── config.ts            # Skill configuration (thresholds, ignore patterns)
├── hooks/
│   └── on-prompt.ts         # Injects maintenance command into prompts
└── README.md                # Setup and usage for humans
```

### Custom Tools
- `project-map:init` — Run full project scan. Creates all `.pi-map.md` files.
- `project-map:patch <file-path>` — Update analysis for a specific file/directory.
- `project-map:validate` — Run consistency check across all `.pi-map.md` files.
- `project-map:reinit [path]` — Force re-initialization of entire project or subtree.

### Prompt Hook
- On every prompt, the skill appends a lightweight instruction:
  > "If you modify any source file, run `project-map:patch <path>` to update the analysis. If you suspect staleness, run `project-map:validate`."

## 9. Risks and Tradeoffs

| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|
| Token bloat (1000+ dirs) | Medium | High | Summary mode, lazy loading, context budget |
| Stale analysis files | High | High | Dirty markers + periodic re-init + validation |
| Agent trusts stale data | Medium | High | Clear instructions to validate before architectural decisions |
| Expensive init on large repos | Medium | Medium | Parallelization, caching, optional incremental init |
| Overlap with LSP/typedoc | Low | Low | This is agent-context, not IDE tooling. Different use case. |
| AST parser unavailable | Medium | Low | Graceful fallback to LLM-only extraction |

## 10. Concrete Example: Full Project Snapshot

```
project-root/
├── .pi-map.md
├── src/
│   ├── .pi-map.md
│   ├── auth/
│   │   ├── .pi-map.md
│   │   ├── tokens.ts
│   │   ├── middleware.ts
│   │   └── types.ts
│   └── db/
│       ├── .pi-map.md
│       ├── connection.ts
│       └── migrations/
│           ├── .pi-map.md
│           └── 001_init.sql
├── docker/
│   ├── .pi-map.md
│   ├── Dockerfile
│   └── docker-compose.yml
└── README.md
```

Each `.pi-map.md` follows the format in Section 2, creating a navigable hierarchy.

## 11. Future Extensions

- **Cross-reference graph**: A top-level `project-graph.md` linking all packages with dependency arrows.
- **Search index**: A lightweight FTS5 index over all `.pi-map.md` files for fast symbol lookup.
- **Diff-aware patching**: Only re-run LLM on changed functions, not entire files.
- **Multi-repo workspaces**: Support monorepos with independent package boundaries.