git_dir=$(git rev-parse --git-dir 2>/dev/null)
git_common_dir=$(git rev-parse --git-common-dir 2>/dev/null)
if [ "$git_dir" != "$git_common_dir" ]; then
  # We're in a worktree — resolve the parent project name
  parent_project=$(basename "$(dirname "$git_common_dir")")
  echo "Worktree detected. Parent project: $parent_project"
else
  parent_project=$(basename "$PWD")
fi
echo "$parent_project"

curl -s "http://localhost:37777/api/context/inject?project=PROJECT_NAME&full=true"

Timeline fetched: ~X observations, estimated ~Yk tokens.
This analysis will consume approximately Yk input tokens + ~5-10k output tokens.
Proceed? (y/n)

You are a technical historian analyzing a software project's complete development timeline from claude-mem's persistent memory system. The timeline below contains every observation, session boundary, and summary recorded across the project's entire history.

You also have access to the claude-mem SQLite database at ~/.claude-mem/claude-mem.db. Use it to run queries for the Token Economics & Memory ROI section. The database has an "observations" table with columns: id, memory_session_id, project, text, type, title, subtitle, facts, narrative, concepts, files_read, files_modified, prompt_number, discovery_tokens, created_at, created_at_epoch, source_tool, source_input_summary.

Write a comprehensive narrative report titled "Journey Into [PROJECT_NAME]" that covers:

## Required Sections

1. **Project Genesis** -- When and how the project started. What were the first commits, the initial vision, the founding technical decisions? What problem was being solved?

2. **Architectural Evolution** -- How did the architecture change over time? What were the major pivots? Why did they happen? Trace the evolution from initial design through each significant restructuring.

3. **Key Breakthroughs** -- Identify the "aha" moments: when a difficult problem was finally solved, when a new approach unlocked progress, when a prototype first worked. These are the observations where the tone shifts from investigation to resolution.

4. **Work Patterns** -- Analyze the rhythm of development. Identify debugging cycles (clusters of bug fixes), feature sprints (rapid observation sequences), refactoring phases (architectural changes without new features), and exploration phases (many discoveries without changes).

5. **Technical Debt** -- Track where shortcuts were taken and when they were paid back. Identify patterns of accumulation (rapid feature work) and resolution (dedicated refactoring sessions).

6. **Challenges and Debugging Sagas** -- The hardest problems encountered. Multi-session debugging efforts, architectural dead-ends that required backtracking, platform-specific issues that took days to resolve.

7. **Memory and Continuity** -- How did persistent memory (claude-mem itself, if applicable) affect the development process? Were there moments where recalled context from prior sessions saved significant time or prevented repeated mistakes?

8. **Token Economics & Memory ROI** -- Quantitative analysis of how memory recall saved work:
   - Query the database directly for these metrics using `sqlite3 ~/.claude-mem/claude-mem.db`
   - Count total discovery_tokens across all observations (the original cost of all work)
   - Count sessions that had context injection available (sessions after the first)
   - Calculate the compression ratio: average discovery_tokens vs average read_tokens per observation
   - Identify the highest-value observations (highest discovery_tokens -- these are the most expensive decisions, bugs, and discoveries that memory prevents re-doing)
   - Identify explicit recall events (observations where source_tool contains "search", "smart_search", "get_observations", "timeline", or where narrative mentions "recalled", "from memory", "previous session")
   - Estimate passive recall savings: each session with context injection receives ~50 observations. Use a 30% relevance factor (conservative estimate that 30% of injected context prevents re-work). Savings = sessions_with_context × avg_discovery_value_of_50_obs_window × 0.30
   - Estimate explicit recall savings: ~10K tokens per explicit recall query
   - Calculate net ROI: total_savings / total_read_tokens_invested
   - Present as a table with monthly breakdown
   - Highlight the top 5 most expensive observations by discovery_tokens -- these represent the highest-value memories in the system (architecture decisions, hard bugs, implementation plans that cost 100K+ tokens to produce originally)

   Use these SQL queries as a starting point:
   ```sql
   -- Total discovery tokens
   SELECT SUM(discovery_tokens) FROM observations WHERE project = 'PROJECT_NAME';

   -- Sessions with context available (not the first session)
   SELECT COUNT(DISTINCT memory_session_id) FROM observations WHERE project = 'PROJECT_NAME';

   -- Average tokens per observation
   SELECT AVG(discovery_tokens) as avg_discovery, AVG(LENGTH(title || COALESCE(subtitle,'') || COALESCE(narrative,'') || COALESCE(facts,'')) / 4) as avg_read FROM observations WHERE project = 'PROJECT_NAME' AND discovery_tokens > 0;

   -- Top 5 most expensive observations (highest-value memories)
   SELECT id, title, discovery_tokens FROM observations WHERE project = 'PROJECT_NAME' ORDER BY discovery_tokens DESC LIMIT 5;

   -- Monthly breakdown
   SELECT strftime('%Y-%m', created_at) as month, COUNT(*) as obs, SUM(discovery_tokens) as total_discovery, COUNT(DISTINCT memory_session_id) as sessions FROM observations WHERE project = 'PROJECT_NAME' GROUP BY month ORDER BY month;

   -- Explicit recall events
   SELECT COUNT(*) FROM observations WHERE project = 'PROJECT_NAME' AND (source_tool LIKE '%search%' OR source_tool LIKE '%timeline%' OR source_tool LIKE '%get_observations%' OR narrative LIKE '%recalled%' OR narrative LIKE '%from memory%' OR narrative LIKE '%previous session%');

### Step 5: Save the Report

Save the agent's output as a markdown file. Default location:

Or if the user specified a different output path, use that instead.

### Step 6: Report Completion

Tell the user:
- Where the report was saved
- The approximate token cost (input timeline + output report)
- The date range covered
- Number of observations analyzed

## Error Handling

- **Empty timeline:** "No observations found for project 'X'. Check the project name with: `curl -s 'http://localhost:37777/api/search?query=*&limit=1'`"
- **Worker not running:** "The claude-mem worker is not responding on port 37777. Start it with your usual method or check `ps aux | grep worker-service`."
- **Timeline too large:** For projects with 50,000+ observations, the timeline may exceed context limits. Suggest using date range filtering: `curl -s "http://localhost:37777/api/context/inject?project=X&full=true"` -- the current endpoint returns all observations; for extremely large projects, the user may want to analyze in time-windowed segments.

## Example

User: "Write a journey report for the tokyo project"

1. Fetch: `curl -s "http://localhost:37777/api/context/inject?project=tokyo&full=true"`
2. Estimate: "Timeline fetched: ~34,722 observations, estimated ~718K tokens. Proceed?"
3. User confirms
4. Deploy analysis agent with full timeline
5. Save to `./journey-into-tokyo.md`
6. Report: "Report saved. Analyzed 34,722 observations spanning Oct 2025 - Mar 2026 (~718K input tokens, ~8K output tokens)."