timeline-report skill

Timeline Report

Generate a comprehensive narrative analysis of a project's entire development history using claude-mem's persistent memory timeline.

When to Use

Use when users ask for:

• "Write a timeline report"
• "Journey into [project]"
• "Analyze my project history"
• "Full project report"
• "Summarize the entire development history"
• "What's the story of this project?"

Prerequisites

The claude-mem worker must be running. The project must have claude-mem observations recorded.

Resolve the worker port (do this once at the start and reuse $WORKER_PORT in every curl call below):

WORKER_PORT="${CLAUDE_MEM_WORKER_PORT:-$(node -e "const fs=require('fs'),p=require('path'),os=require('os');const uid=(typeof process.getuid==='function'?process.getuid():77);const fallback=String(37700+(uid%100));try{const s=JSON.parse(fs.readFileSync(p.join(os.homedir(),'.claude-mem','settings.json'),'utf-8'));process.stdout.write(String(s.CLAUDE_MEM_WORKER_PORT||fallback));}catch{process.stdout.write(fallback);}" 2>/dev/null)}"

This honors CLAUDEMEMWORKER_PORT env, then ~/.claude-mem/settings.json, then falls back to the per-UID default 37700 + (uid % 100) — matching how the worker itself picks its port. Required for multi-account setups (#2101) and any user who has overridden the default port (#2103).

Workflow

Step 1: Determine the Project Name

Ask the user which project to analyze if not obvious from context. The project name is typically the directory name of the project (e.g., "tokyo", "my-app"). If the user says "this project", use the current working directory's basename.

Worktree Detection: Before using the directory basename, check if the current directory is a git worktree. In a worktree, the data source is the parent project, not the worktree directory itself. Run:

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"

If a worktree is detected, use $parent_project (the basename of the parent repo) as the project name for all API calls. Inform the user: "Detected git worktree. Using parent project '[name]' as the data source."

Step 2: Fetch the Full Timeline

Use Bash to fetch the complete timeline from the claude-mem worker API:

curl -s "http://localhost:${WORKER_PORT}/api/context/inject?project=PROJECT_NAME&full=true"

This returns the entire compressed timeline -- every observation, session boundary, and summary across the project's full history. The response is pre-formatted markdown optimized for LLM consumption.

Token estimates: The full timeline size depends on the project's history:

• Small project (< 1,000 observations): ~20-50K tokens
• Medium project (1,000-10,000 observations): ~50-300K tokens
• Large project (10,000-35,000 observations): ~300-750K tokens

If the response is empty or returns an error, the worker may not be running or the project name may be wrong. Try curl -s "http://localhost:${WORKERPORT}/api/search?query=&limit=1" to verify the worker is healthy.

Step 3: Estimate Token Count

Before proceeding, estimate the token count of the fetched timeline (roughly 1 token per 4 characters). Report this to the user:

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

Wait for user confirmation before continuing if the timeline exceeds 100K tokens.

Step 4: Analyze with a Subagent

Deploy an Agent (using the Task tool) with the full timeline and the following analysis prompt. Pass the ENTIRE timeline as context to the agent. The agent should also be instructed to query the SQLite database at ~/.claude-mem/claude-mem.db for the Token Economics section.

Agent prompt:

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, content_hash, generated_by_model, relevance_count, merged_into_project, agent_type, agent_id, metadata.

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 

-- Total discovery tokens SELECT SUM(discoverytokens) FROM observations WHERE project = 'PROJECTNAME';

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

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

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

-- Monthly breakdown SELECT strftime('%Y-%m', createdat) as month, COUNT() as obs, SUM(discoverytokens) as totaldiscovery, COUNT(DISTINCT memorysessionid) as sessions FROM observations WHERE project = 'PROJECT_NAME' GROUP BY month ORDER BY month;

-- Explicit recall events SELECT COUNT() FROM observations WHERE project = 'PROJECTNAME' AND (narrative LIKE '%recalled%' OR narrative LIKE '%from memory%' OR narrative LIKE '%previous session%');

9. **Timeline Statistics** -- Quantitative summary:
   - Date range (first observation to last)
   - Total observations and sessions
   - Breakdown by observation type (features, bug fixes, discoveries, decisions, changes)
   - Most active days/weeks
   - Longest debugging sessions

10. **Lessons and Meta-Observations** -- What patterns emerge from the full history? What would a new developer learn about this codebase from reading the timeline? What recurring themes or principles guided development?

## Writing Style

- Write as a technical narrative, not a list of bullet points
- Use specific observation IDs and timestamps when referencing events (e.g., "On Dec 14 (#26766), the root cause was finally identified...")
- Connect events across time -- show how early decisions created later consequences
- Be honest about struggles and dead ends, not just successes
- Target 3,000-6,000 words depending on project size
- Use markdown formatting with headers, emphasis, and code references where appropriate

## Important

- Analyze the ENTIRE timeline chronologically -- do not skip early history
- Look for narrative arcs: problem -> investigation -> solution
- Identify turning points where 

Step 5: Save the Report

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

./journey-into-PROJECT_NAME.md

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:${WORKERPORT}/api/search?query=&limit=1\""
• Worker not running: "The claude-mem worker is not responding on port ${WORKER_PORT}. Start it with your usual method or check ps aux | grep worker-service."
• Timeline too large: For projects with 50,000+ observations, the timeline may exceed context limits. Suggest using date range filtering: curl -s "http://localhost:${WORKER_PORT}/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:${WORKER_PORT}/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)."