State Tracker - Git-Powered Session & State Awareness Skill

State Tracker - Git-Powered Session & State Awareness

Purpose: Provide Claude with complete awareness of project state using git as source of truth, preventing stale mental models and redundant work.

What This System Tracks:

In-Session State (real-time)
- File modifications (git diff)
- Uncommitted changes (git status)
- Current branch and commit
Between-Session Context (differential)
- What happened since last session
- Commits made (by you or others)
- Files changed externally
- Branch switches
Historical Decisions (queryable)
- Past research findings
- Architectural decisions
- Implementation plans

Token Overhead:

In-session checks: ~200-500 tokens (only when files change)
Between-session context: ~1-2K tokens (differential only)
Decision queries: ~2-5K tokens (selective loading)
Total: <5K tokens vs 50K+ full reload

When This Skill Activates

Auto-activates when you detect:

State Queries:

"what changed since last session?"
"show me what happened"
"what's the current state?"
"what files did we work on?"
"what commits were made?"

Before File Operations:

PreToolUse hook activates automatically
Checks if file changed externally
Warns before Edit/Write if file is stale

Before Duplicating Work:

"research X" → Check if already researched
"decide on Y" → Check if already decided
"plan Z" → Check if plan exists

Your Workflow When This Skill Activates

If User Asks "What Changed?"

Run the manual status script:

./scripts/session-status.sh

This shows:

Git activity since last session
Current working directory status
Decision tracking summary

Token cost: ~500 tokens for complete state summary

If You're About to Edit a File

Trust the PreToolUse hook:

It automatically checks git status
If file changed externally, you'll see warning
Follow the recommendation: Re-read before editing

Don't manually check git status - hook does it automatically!

If Starting Research or Planning

Query decisions.yaml first:

# Before research
uv run scripts/decision-query.py --topic "{topic}" --type research

# Before planning
uv run scripts/decision-query.py --topic "{topic}" --type plans

# Before deciding
uv run scripts/decision-query.py --topic "{topic}" --type decisions

If found: Load existing context (2-5K tokens) If not found: Proceed with new work

###If User Made External Changes

User says: "I made some changes" or "I committed something"

Your response:

# Check what changed
./scripts/session-status.sh

Then summarize what you found for the user.

The Complete State Awareness System

Component 1: In-Session State Sync (Automatic)

PreToolUse Hook checks git state before file operations:

# Happens automatically when you try to Edit/Write
PreToolUse: Intercepts tool call
  → Runs: git status <file>
  → If changed: "⚠️ File modified externally - Re-read before editing"
  → Always continues (non-blocking)

What you see:

⚠️ File State Change Detected

File: hooks/user_prompt_submit.py
Status: MODIFIED
Git Says: File has uncommitted changes

Recommendation:
- Re-read file to see current state
- Use Read tool before Edit

Continuing with your Edit operation...

Token cost: ~300 tokens (only when file actually changed)

Component 2: Between-Session Context (Automatic)

SessionStart Hook injects git context automatically:

Session starts:
  → SessionStart reads .contextune/last_session.yaml
  → Runs: git log <last_commit>..HEAD
  → Runs: git diff --stat <last_commit>..HEAD
  → Generates differential summary
  → Injects as additionalContext

What you see at session start:

📋 Git Context Since Last Session (2 hours ago)

**Git Activity:**
- 5 new commits
- 8 files changed (+250, -30)
- Branch: master

**Recent Commits:**
  a95478f feat: add three-layer git enforcement
  1e1a15a feat: add plan extraction support
  ... and 3 more

**Files Changed:**
  Added (2):
    - commands/ctx-git-commit.md
    - hooks/pre_tool_use_git_advisor.py
  Modified (6):
    - hooks/user_prompt_submit.py
    - hooks/hooks.json
    ... and 4 more

**Current Status:** 2 uncommitted changes

Ready to continue work!

Token cost: ~1-2K tokens (only NEW information since last session)

Component 3: Manual Status Check

When user asks "what changed?" or you need to check state:

# Run the status script
./scripts/session-status.sh

Shows:

Current git state (branch, commit, uncommitted files)
Changes since last session (commits, files, diff stats)
Decision tracking status
Full git summary

When to use:

User asks "what's the current state?"
You need to verify what happened
Before major operations
After user says "I made some changes"

Token cost: ~500 tokens

Complete Workflow Examples

Example 1: File Modified Externally (In-Session)

10:00 - You: Read hooks/user_prompt_submit.py
        [File contents loaded into context]

10:15 - User edits file in VS Code
        [Makes changes, saves]

10:20 - You: Edit hooks/user_prompt_submit.py

        PreToolUse Hook: ⚠️ File State Change Detected
          File: hooks/user_prompt_submit.py
          Status: MODIFIED
          Recommendation: Re-read before editing

You: "I see the file was modified externally. Let me re-read it first."
     [Read hooks/user_prompt_submit.py]
     [Now have current state]
     [Proceed with Edit]

Token saved: Prevented edit conflict + re-work

Example 2: New Session After External Changes

Session 1 ends:
  SessionEnd: Records metadata to .contextune/last_session.yaml
    - session_id, timestamp, last_commit, branch, files_worked_on

[User works outside Claude]
  - Commits via terminal: git commit -m "quick fix"
  - Edits 3 files manually
  - Switches to develop branch

Session 2 starts:
  SessionStart: Loads .contextune/last_session.yaml
    → git log <last_commit>..HEAD
    → git diff --stat <last_commit>..HEAD
    → Generates summary

Claude sees:
  📋 Git Context Since Last Session (3 hours ago)

  **Git Activity:**
  - 1 new commit: "quick fix"
  - 3 files changed
  - Branch: master → develop (switched)

  **Current Status:** Clean ✅

Claude: "I see you made a commit and switched to develop branch.
        The 3 files that changed are now in my context. Ready to continue!"

Token cost: ~1.5K (vs 50K+ full reload)

The Decision Tracking System

Structure

decisions.yaml - YAML database with 3 types of entries:

research:
  entries:
    - id: "res-001-authentication-libraries"
      topic: "Authentication libraries for Node.js"
      findings: "Compared Passport.js, Auth0, NextAuth..."
      recommendation: "Use NextAuth for React apps"
      created_at: "2025-10-28"
      expires_at: "2026-04-28"  # 6 months
      tags: [authentication, libraries, nodejs]

plans:
  entries:
    - id: "plan-001-jwt-implementation"
      title: "JWT Authentication Implementation"
      summary: "5 tasks: AuthService, middleware, tokens..."
      status: "completed"
      created_at: "2025-10-28"
      tags: [authentication, implementation]

decisions:
  entries:
    - id: "dec-001-dry-strategy"
      title: "Unified DRY Strategy"
      status: "accepted"
      context: "CHANGELOG grows unbounded..."
      alternatives_considered: [...]
      decision: "Use scripts for git workflows"
      consequences: {positive: [...], negative: [...]}
      tags: [architecture, cost-optimization]

CLI Tools

Query existing context:

# Check if we already researched a topic
uv run scripts/decision-query.py --topic "authentication" --type research

# Check for existing decisions
uv run scripts/decision-query.py --topic "DRY" --type decisions

# Check for active plans
uv run scripts/decision-query.py --type plans --status active

# Query by tags
uv run scripts/decision-query.py --tags architecture cost-optimization

Output format:

# Filtered entries matching your query
# Load only relevant context (2-5K tokens vs 150K full CHANGELOG)

Your Workflow (IMPORTANT!)

Before Starting Research

ALWAYS query first:

# Check if we already researched this topic
uv run scripts/decision-query.py --topic "{research_topic}" --type research

If found:

Load the existing findings (2K tokens)
Check expiration date (research expires after 6 months)
If recent → Use existing research
If expired → Research again, update entry

If NOT found:

Proceed with research
SessionEnd hook will auto-extract to decisions.yaml

Savings:

Skip $0.07 redundant research
Load 2K tokens instead of researching again

Before Making Decisions

ALWAYS query first:

# Check for existing decisions on this topic
uv run scripts/decision-query.py --topic "{decision_topic}" --type decisions

If found:

Load the decision context
Check status (accepted, rejected, superseded)
If accepted → Follow existing decision
If superseded → Find superseding decision
If rejected → Understand why, avoid same approach

If NOT found:

Proceed with decision-making
SessionEnd hook will auto-extract to decisions.yaml

Savings:

Skip 15-30 min re-discussion
Consistent decisions across sessions

Before Planning

ALWAYS query first:

# Check for existing plans on this topic
uv run scripts/decision-query.py --topic "{feature_name}" --type plans

If found:

Load existing plan (2-3K tokens)
Check status (active, completed, archived)
If active → Continue existing plan
If completed → Reference, don't recreate

If NOT found:

Create new plan with /ctx:plan
Plan will be auto-extracted to decisions.yaml

Auto-Population

decision-sync.py scans conversation history and auto-populates decisions.yaml:

# Scan all conversations for decisions (run once)
uv run scripts/decision-sync.py

# Result: Populates decisions.yaml with historical context

How it works:

Scans ~/.claude/projects/*/conversation/ for transcripts
Uses extraction patterns to detect decisions/research/plans
Extracts and appends to decisions.yaml
Deduplicates (won't add same decision twice)

Already populated: Check current state:

# See what's already in decisions.yaml
uv run scripts/decision-query.py --all

Token Efficiency

Context Loading Comparison

Old approach (CHANGELOG.md):

Import entire CHANGELOG: 150K tokens
Problem: Loads everything, most irrelevant
Cost: High context usage

New approach (decisions.yaml with queries):

Query specific topic: 2-5K tokens (83-97% reduction!)
Example: decision-query.py --topic "authentication"
Loads: Only relevant 2-3 entries

Selective Loading Strategy

Scenario 1: Starting authentication work

# Query for authentication context
uv run scripts/decision-query.py --topic "authentication"

# Loads:
- Research: Authentication libraries (if exists)
- Decisions: Auth approach decisions (if exists)
- Plans: Auth implementation plans (if exists)

# Total: ~3K tokens vs 150K full CHANGELOG

Scenario 2: User asks "why did we choose X?"

# Query for specific decision
uv run scripts/decision-query.py --topic "DRY strategy"

# Loads: Single decision with full context
# Total: ~1K tokens

Integration with Hooks

SessionEnd Hook (Automatic)

session_end_extractor.py already extracts to decisions.yaml:

Detects decisions in conversation (## Decision: pattern)
Extracts structured data
Appends to decisions.yaml automatically

You don't need to do anything - it happens automatically at session end!

What You Should Do

During conversation:

Output decisions in extraction-optimized format (see output style)
SessionEnd hook extracts automatically
Next session, query for context if needed

Examples

Example 1: Before Researching Libraries

User: "Research best state management libraries for React"

You: Let me check if we already researched this.

[Run decision-query.py --topic "state management" --type research]

Result: Found existing research from 2 months ago
  - Compared: Redux, Zustand, Jotai, Valtio
  - Recommendation: Zustand for simple apps, Jotai for complex
  - Tags: [react, state-management, libraries]

You: We already researched this! Here's what we found:
     [Load 2K tokens vs spending $0.07 to research again]

Example 2: Before Making Architecture Decision

User: "Should we use microservices or monolith?"

You: Let me check if we already decided on architecture approach.

[Run decision-query.py --topic "architecture" --type decisions]

Result: Found decision "dec-002-monolith-first"
  - Decision: Start with modular monolith
  - Rationale: Team size <5, single deployment simpler
  - Status: accepted
  - Date: 2025-09-15

You: We already decided this! Here's the context:
     [Load 1K tokens vs re-discussing for 30 minutes]

Example 3: Before Planning Feature

User: "Plan implementation for user dashboard"

You: Let me check for existing plans.

[Run decision-query.py --topic "dashboard" --type plans]

Result: Found plan "plan-005-dashboard-v1"
  - Status: completed
  - Summary: "5 tasks implemented, merged to main"
  - Created: 2025-10-01

You: We already implemented this! Let me load the existing plan.
     [Load 3K tokens, reference existing work]

Lifecycle Management

Research entries expire after 6 months:

Rationale: Technology evolves, best practices change
Old research becomes stale (2024 → 2025 practices differ)
Expired entries moved to archives

Plans archive 90 days after completion:

Rationale: Useful during implementation, less useful after
Completed plans moved to docs/archive/

Decisions never auto-expire:

Unless explicitly superseded by new decision
Architectural decisions stay relevant

Check lifecycle status:

# See active vs expired entries
uv run scripts/decision-query.py --show-expired

Cost Impact

Annual savings (assuming 50 research sessions):

Old: 50 × $0.07 = $3.50 in redundant research
New: Query first (free), research only if needed
Savings: ~$3.00/year + avoid 25 hours of redundant work

Token savings per query:

Load full CHANGELOG: 150K tokens
Load specific query: 2-5K tokens
Savings: 97% reduction per lookup

Quick Reference

Check before researching:

uv run scripts/decision-query.py --topic "{topic}" --type research

Check before deciding:

uv run scripts/decision-query.py --topic "{topic}" --type decisions

Check before planning:

uv run scripts/decision-query.py --topic "{topic}" --type plans

See all active context:

uv run scripts/decision-query.py --all

Integration Points

Before /ctx:research - Query for existing research first
Before /ctx:plan - Query for existing plans first
Before /ctx:design - Query for existing decisions first
When user asks "why" - Query for decision rationale
At SessionEnd - Automatic extraction (no action needed)

Summary

Key principle: Query before doing work that might already be done.

Benefits:

83-97% token reduction for context loading
Avoid $0.07 redundant research
Consistent decisions across sessions
Queryable, structured context
Auto-populated from conversation history

Remember: decisions.yaml is plugin-local, works for all users who install Contextune!

Agent Skills: State Tracker - Git-Powered Session & State Awareness

Skill Files