Developer situational awareness for the agentic coding era.
Project description
wip — Developer situational awareness for the agentic coding era
AI agents ship code while you sleep. They merge PRs, create branches, and push commits across your repos — and you need to know what happened. wip scans your git repositories, passively detects agent activity (Claude, Copilot, Cursor, Devin), and gives you a complete picture: what changed, what's dirty, what's stashed, and what needs your attention. With AI-powered briefings, it turns raw git signals into narrative summaries so you can pick up exactly where you — and your agents — left off.
Demo
Features
- 🕵️ Agent detection — passively detect coding agent activity (Claude, Copilot, Cursor, Devin) from git signals, with active/recent/stale status tracking
- 🤖 AI-powered briefings — narrative summaries, standup drafts, natural language queries — all agent-aware
- 🧭 Context-aware git help — ask how to untangle branches, recover stashes, or fix mistakes — the AI sees your actual repo state
- 🔌 Multi-provider LLM — Anthropic, OpenAI, and Gemini all implemented
- 🔍 Auto-discover git repos in configured directories
- 📊 Status overview — dirty files, stashes, ahead/behind tracking
- 🌿 Recent branches — see branches you've touched recently
- 💬 Recent commits — your commits from the last 24 hours
- 📂 Enriched file-level context — changed files with diff stats, stash descriptions, commit bodies and file lists
- 📝 Work-in-progress tracker — jot down tasks, link them to repos, see them in your briefing
- 🎨 Rich terminal output — color-coded status with icons
- 📦 Multiple output modes — human-friendly or JSON for scripting
Installation
From PyPI (recommended)
pip install wip-cli
Or with pipx for an isolated install:
pipx install wip-cli
From source
git clone git@github.com:drmnaik/wip.git
cd wip
pip install -e .
Requirements: Python 3.9+ PyPI: https://pypi.org/project/wip-cli/
Quick Start
1. Set up wip (interactive)
wip config init
This walks you through:
- Directories — which folders to scan for git repos
- Author name — your git identity (auto-detected from
git config) - LLM provider — optional, enables AI-powered briefings (Anthropic, OpenAI, or Gemini)
2. Run your first briefing
wip # Show briefing
wip --verbose # Full details
wip --json # JSON output for scripting
3. Set up an LLM provider (optional)
AI features (wip ai briefing, wip ai standup, wip ai ask) require an LLM provider. You can configure this during wip config init, or manually — pick one below.
Anthropic (Claude)
- Get an API key at console.anthropic.com
- Export it:
export ANTHROPIC_API_KEY="sk-ant-..." - Add to your config:
[llm]
provider = "anthropic"
model = "claude-haiku-4-5-20251001"
api_key_env = "ANTHROPIC_API_KEY"
OpenAI (GPT)
- Get an API key at platform.openai.com
- Export it:
export OPENAI_API_KEY="sk-..." - Add to your config:
[llm]
provider = "openai"
model = "gpt-4o"
api_key_env = "OPENAI_API_KEY"
Google Gemini
- Get an API key at aistudio.google.com
- Export it:
export GEMINI_API_KEY="..." - Add to your config:
[llm]
provider = "gemini"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
Tip: Add the
exportline to your~/.bashrcor~/.zshrcso the key persists across sessions. Leavemodelempty to use the provider's default.
Configuration Reference
Config is stored at ~/.wip/config.toml. You can edit it directly or re-run wip config init. View current settings with wip config show.
directories = ["/Users/you/projects", "/Users/you/work"]
author = "Your Name"
scan_depth = 3
recent_days = 14
[llm]
provider = "anthropic"
model = "claude-haiku-4-5-20251001"
api_key_env = "ANTHROPIC_API_KEY"
# Optional: customize agent detection patterns
[agents]
authors = ["claude", "copilot", "cursor", "devin", "codex", "github-actions", "bot"]
branch_patterns = ["agent/", "claude/", "copilot/", "devin/", "cursor/"]
| Field | Description | Default |
|---|---|---|
directories |
Folders to scan for git repos | current directory |
author |
Your git author name (filters commits) | auto-detected |
scan_depth |
How deep to recurse into directories | 3 |
recent_days |
Lookback window for recent branches | 14 |
[llm] provider |
anthropic, openai, or gemini |
— |
[llm] model |
Model ID (empty = provider default) | — |
[llm] api_key_env |
Env var name holding your API key | — |
[agents] authors |
Substrings matched against commit author names | Claude, Copilot, Cursor, Devin, etc. |
[agents] branch_patterns |
Branch prefixes indicating agent activity | agent/, claude/, copilot/, etc. |
Commands
Core
wip # Show briefing (default command)
wip scan # Alias for wip
wip --json # Output as JSON
wip --verbose # Show full details
wip config init # Interactive setup
wip config show # Display current config
wip version # Show version
Work-in-progress tracker
wip add "fix auth bug" # Add item (auto-links to current repo)
wip add "read docs" --repo /path/to/repo # Add item linked to specific repo
wip done 1 # Mark item #1 as done
wip list # Show open items
wip list --all # Show all items including completed
AI-powered commands
Requires an LLM provider configured in ~/.wip/config.toml and the corresponding API key set as an environment variable.
wip ai briefing # Narrative briefing
wip ai standup # Generate a standup update from git activity
wip ai ask "what was I working on yesterday?"
wip ai ask "anything I forgot to push?"
wip ai ask "summarize my week"
# Context-aware git help — the AI sees your actual branches, dirty files, and stashes
wip ai ask "I have diverged branches, how do I cleanly get back to main?"
wip ai ask "how do I recover what I stashed last week?"
wip ai ask "what git commands do I need to untangle this mess?"
Example Output
Standard briefing (wip)
wip — 3 repos scanned
work-in-progress — 2 items
#1 fix auth token refresh (auth-service) — 2h ago
#3 update API docs (api-gateway) — 1d ago
auth-service (fix/token-refresh) ⚠
3 dirty · 1 stash · last commit 14h ago
2 ahead, 0 behind origin
agents:
claude on agent/add-tests — 12 commits, 14 files (23m ago) ● active
copilot on copilot/logout — 4 commits, 3 files (7h ago) ○ stale
wip:
#1 fix auth token refresh (2h ago)
recent: main (3d), feat/oauth (5d)
commits today:
a1b2c3 fix retry logic for token refresh (2h ago)
frontend (main) ✓
clean · 2 stashes
0 ahead, 0 behind origin
api-gateway (main) ↓
clean · 3 behind origin
wip:
#3 update API docs (1d ago)
AI briefing (wip ai briefing)
## Briefing
### auth-service
Claude was busy overnight — 12 commits on agent/add-tests, touching
14 files. It added unit tests for the token refresh flow and the retry
logic you were working on. The branch is still active (last commit 23m ago).
Meanwhile, you have 3 dirty files on fix/token-refresh with a stash that
looks like an alternative approach. Review Claude's test coverage before
resuming your fix — there may be overlap.
### frontend
Clean, nothing to do here. You left a TODO about form validation on
Tuesday but no urgency.
### api-gateway
3 commits behind origin — just needs a pull. Your note says to update
the API docs once auth-service lands.
Suggested focus: review Claude's agent/add-tests branch in auth-service,
then resume your token refresh fix.
Status Icons
- ✓ — Clean repo, up to date
- ⚠ — Dirty files (modified, staged, or untracked)
- ↓ — Behind remote (needs pull)
Privacy
wip scanruns entirely locally — no data leaves your machine.wip aicommands send repository metadata (commit messages, branch names, file paths, work items) to your configured LLM provider (Anthropic, OpenAI, or Gemini). No file contents or diffs are sent.- API keys are never stored in config — only the environment variable name is saved.
Development
# Install dependencies
pip install -e .
# Run from source
python -m wip.cli
Roadmap
Phase 1: Foundation + Scanner ✅
- Config management, repo discovery, git status scanning, terminal output
Phase 2: Interactive Worklist ✅
wip add/done/listcommands with repo linking and persistent state
Phase 3: LLM Integration ✅
- Provider abstraction (Anthropic, OpenAI, and Gemini all implemented)
wip ai briefing,wip ai standup,wip ai askwith streaming- Prompt assembly from scan data, config-driven provider/model selection
Phase 4: Passive Agent Detection ✅
- Detect coding agent activity from git signals (author names, branch patterns)
- Agent sessions surface in
wip,wip --json, and all AI commands automatically - Configurable author/branch patterns with sensible defaults (zero config required)
- Status tracking: active (<1h), recent (<24h), stale (>24h)
Phase 5: Enriched File-Level Context ✅
- Changed files with diff stats (insertions/deletions), color-coded by stage in verbose output
- Stash descriptions surfaced in verbose display and LLM prompts
- Commit bodies (capped at 3 lines) and per-commit file lists (capped at 10 paths) in LLM context
Ideas and contributions welcome — see docs/CONTEXT.md for architecture details.
Contact
- LinkedIn: Mahesh Naik
- Issues: GitHub Issues
License
MIT
Author
Built by Mahesh Naik with Claude
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