decorated-pi

decorated-pi is a practical enhancement pack for Pi — token-efficient workflow, cache-friendly design, and smarter tools.

Install

pi install npm:decorated-pi
pi install git:github.com/lcwecker/decorated-pi
pi install /path/to/decorated-pi

Features

1. Token Efficiency

Multiple layers of token savings that compound across every session.

RTK — integrates RTK to compress bash output into structured summaries, so the LLM never sees raw noise. Just install the CLI, zero config.

Codegraph — integrates codegraph to offer a code map of your project, so the LLM can navigate symbols and call graphs without chaining ls → grep → read. You should manage the code source index yourself, seecodegraph doc.

Auxiliary Models — offloads heavy-but-dumb tasks to cheaper models so your primary model only pays for the hard work:

• Image Read Fallback — detects image type via magic bytes, calls a configured vision-capable model, and injects the analysis text, so your main model never touches image tokens
• Compact Model — handles context compaction with a smaller model instead of burning main-model capacity

Configured via /dp-model.

Cache‑friendly Design — stable system prompt prefix:

• tool definitions, guidelines, and skills are sorted alphabetically so the system prompt is identical across sessions
• volatile elements like Current date: … are stripped before prompt assembly
• MCP tool schemas are persisted to a local cache, so the tool list stays stable regardless of network conditions or server availability

Pi Native Prompt Slimming

• move the default Pi documentation block out of the system prompt and into a builtin pi-docs skill, so the docs reference loads on demand instead of sitting in every turn's prompt
• unregister the native write tool, since bash can handle it

2. Smarter Tools

Drop‑in replacements for Pi's built‑in tools, with better UX and fewer wasted turns.

Patch Tool

Capability	Pi native edit	patch
Anchor‑based search	❌ extending oldText for uniqueness	✅ anchor bounds scope for precise matching
Fuzzy whitespace match	❌ only reports "not found"	✅ auto‑corrects tab↔space / trailing whitespace mismatches
Edit fault diagnostics	❌ only reports "not found"	✅ pinpoint faults for LLM comprehension
Stale‑read protection	❌ Blind to external changes	✅ read captures mtime, patch rejects stale targets

Smarter @ File Search

decorated-pi replaces pi's built-in @ autocomplete with a high-speed file finder backed by @ff-labs/fff-node— a Rust SIMD fuzzy file search engine with in-memory index, frecency ranking, and git status awareness. Pi's native provider shells out to fd on every keystroke.

Aspect	Pi native @	decorated-pi (FFF)
Speed	❌ walks filesystem via fd subprocess on every keystroke	✅ in‑memory index built once per session, ~0.1 ms / query
Ranking	❌ 4‑bucket case‑sensitive score (exact/starts/contains/path)	✅ fuzzy match + frecency + git status (boots from git log)
Noise	❌ shows every file in the project, including .git, node_modules, dist	✅ filters git‑ignored files; substring filter on path keeps short queries relevant

Benchmark of @

┌─ smart-at benchmark
├─ generated 500,000 files in 3.9 s
├─ FFF scan complete in 664 ms
│  RSS after FFF index: 408 MB  (+330 MB over baseline)
├─ accuracy (14 queries)
│                  top‑1   top‑3   top‑5   false‑pos
│  smart-at         93%     93%     93%    0
│  native (fd)      86%     93%     93%    0
└─ corpus cleaned

name                                            hz      mean
· smart-at                                      8.10    123 ms
· native @  (fd subprocess)                     3.12    320 ms

Summary: smart-at is 2.59x faster than native @  (fd subprocess)

LSP support

Covers what codegraph can't: real-time compiler and lint errors.

• lsp_diagnostics — file diagnostics with severity filtering

Supported languages: c/cpp, go, java, lua, json, python, ruby, rust, svelte, typescript

3. MCP Ecosystem

Zero-config MCP client with built-in servers:

Server	Tool Prefix	Source
Context7	context7_*	https://mcp.context7.com/mcp
Exa	exa_*	https://mcp.exa.ai/mcp
codegraph	codegraph_*	bundled binary

Custom servers in .pi/agent/mcp.json (project) or ~/.pi/agent/mcp.json (global). Project overrides global. Tool prompts and schemas are cached locally so MCP tools are available immediately on startup.

{
  "mcpServers": {
    "my-server": {
      "url": "https://my-mcp.example.com/mcp",
      "enabled": true
    },
    "my-sse": {
      "url": "https://my-mcp.example.com/sse",
      "enabled": false
    },
    "my-stdio": {
      "command": "npx",
      "args": ["-y", "my-mcp-server"],
      "env": { "DEBUG": "1" }
    }
  }
}

Use /mcp to view connection status and toggle servers.

4. Secret Redaction

Three-layer detection: high-confidence known-format patterns (AWS, GitHub, OpenAI, etc.), config-key regex matching, and adjusted Shannon entropy heuristics for unknown secret-like values.

Example redaction on a read / bash output:

{
  "aws_access_key_id": "AKI**************PLE",
  "github_token": "ghp***************def",
  "database_password": "Sup#######t99",
  "api_key": "sk_**************f5a",
  "random_secret": "a1b??????5f5"
}

* = known pattern, # = config key regex, ? = entropy heuristic.

5. Other

• /usage — token stats with cache‑hit rate, per‑model breakdown (Session / Today / This Week / This Month / All Time)
• /retry — continue after interruption
• Progressive context — supports subdirectory AGENTS.md / CLAUDE.md discovery and injection
• WakaTime — coding activity tracking via WakaTime

Configuration

Runtime settings in ~/.pi/agent/decorated-pi.json. Modules can be toggled via /dp-settings (changes take effect after /reload).

{
  "modules": {
    "tools": {
      "patchOverrideEdit": true,
      "ask": true,
      "lsp": true,
      "mcp": true
    },
    "hooks": {
      "secretRedaction": true,
      "rtk": true,
      "wakatime": true
    },
    "commands": {
      "atOverride": true,
      "retry": true,
      "usage": true
    }
  }
}

License

MIT