timeline-report
The timeline-report skill generates a comprehensive narrative analysis of a project's complete development history by leveraging claude-mem's persistent memory timeline. Use this skill when users request a timeline report, project history analysis, development journey, or comprehensive project summary. The skill requires the claude-mem worker to be running and previously recorded observations about the project.
git clone --depth 1 https://github.com/thedotmack/claude-mem /tmp/timeline-report && cp -r /tmp/timeline-report/plugin/skills/timeline-report ~/.claude/skills/timeline-reportSKILL.md
# 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 `$WORKER_PORT` in every curl call below):
```bash
WORKER_PORT="${CLAUDE_MEM_WORKER_PORT:-$(node -e "const fs=require('fs'),p=require('path'),os=require('os');const uid=(typeof process.getuid==='function'?process.getuid():77);const fallback=String(37700+(uid%100));try{const s=JSON.parse(fs.readFileSync(p.join(os.homedir(),'.claude-mem','settings.json'),'utf-8'));process.stdout.write(String(s.CLAUDE_MEM_WORKER_PORT||fallback));}catch{process.stdout.write(fallback);}" 2>/dev/null)}"
```
This honors `CLAUDE_MEM_WORKER_PORT` env, then `~/.claude-mem/settings.json`, then falls back to the per-UID default `37700 + (uid % 100)` — matching how the worker itself picks its port. Required for multi-account setups (#2101) and any user who has overridden the default port (#2103).
## Workflow
### Step 1: Determine the Project Name
Ask the user which project to analyze if not obvious from context. The project name is typically the directory name of the project (e.g., "tokyo", "my-app"). If the user says "this project", use the current working directory's basename.
**Worktree Detection:** Before using the directory basename, check if the current directory is a git worktree. In a worktree, the data source is the **parent project**, not the worktree directory itself. Run:
```bash
git_dir=$(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 `$parent_project` (the basename of the parent repo) as the project name for all API calls. Inform the user: "Detected git worktree. Using parent project '[name]' as the data source."
### Step 2: Fetch the Full Timeline
Use Bash to fetch the complete timeline from the claude-mem worker API:
```bash
curl -s "http://localhost:${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:${WORKER_PORT}/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, 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 (dedicateWatch a pull request or review cycle until it is ready to merge. Use when asked to babysit, monitor, or keep checking PR comments, reviews, and CI until all actionable issues are resolved.
Audit a design against Dieter Rams' ten "Good design is..." principles, then hand off a /make-plan prompt for one of three outcomes — new design, refine design, or redesign. Use when the user says "audit this design", "design review", "check this UI against Rams", "is this UI good", "critique this design", "design audit", or asks for a critique that should lead to a plan.
Execute a phased implementation plan using subagents. Use when asked to execute, run, or carry out a plan — especially one created by make-plan.
Explain how claude-mem captures observations, when memory injection kicks in, and where data lives. Use when the user asks "how does claude-mem work?" or "what is this thing doing?".
Build and query AI-powered knowledge bases from claude-mem observations. Use when users want to create focused "brains" from their observation history, ask questions about past work patterns, or compile expertise on specific topics.
Prime a codebase by reading every source file in full. Use when starting work on a new or unfamiliar project, or when the user asks to "learn the codebase", "read the codebase", "prime", or "get up to speed".
Create a detailed, phased implementation plan with documentation discovery. Use when asked to plan a feature, task, or multi-step implementation — especially before executing with do.