my-self-improve

name	my-self-improve
description	Use when you want to analyze Claude session history and surface automation opportunities — finds repeated patterns, skill candidates, agent workflows, config improvements, and coding style fixes. Also use for "self improve", "analyze sessions", "what should I automate", or "find patterns".
argument-hint	< all | alltime | (no arg: current project only) >
allowed-tools	Read, Write, Edit, Glob, Grep, Bash(cat:*), Bash(find:*), Bash(python3:*), Bash(wc:*), Bash(date:*), Bash(head:*), Bash(stat:*), Agent

Self-Improve

Analyze Claude Code session history to surface automation opportunities. Finds repeated patterns and writes actionable suggestions to ~/.claude/learning/self-improve.md.

Arguments

• No args: process current project only (detect via git rev-parse --show-toplevel, extract project name from path)
• all: process all projects incrementally (since each project's last-processed timestamp)
• alltime: process all projects, ignore timestamps (reprocess everything)

State File

~/.claude/learning/self-improve.md — single source of truth. Contains:

• Processed table: one row per project with last-processed ISO timestamp
• Suggestions: one line per finding, deleted once implemented

Format rules are in the file's HTML comments. Read them. Follow them.

---

Step 1 — Read State

Read ~/.claude/learning/self-improve.md. Parse the Processed table into a map of project -> timestamp. Projects not in the table default to 2021-01-01T00:00:00Z.

---

Step 2 — Find Session Files

Session JSONL files live at ~/.claude/projects/<encoded-path>/. The encoded path format is - + the absolute path with / replaced by -.

find ~/.claude/projects/ -name "*.jsonl" -not -path "*subagent*" 2>/dev/null

For each JSONL file:

1. Extract the project name from the directory path (reverse the encoding: last segment after -Users-youruser-code-github- or -Users-youruser-data-code-github-)
2. Get the file's modification time
3. If no args (current project mode), skip files not matching the current project
4. If not alltime mode, skip files whose mtime is older than the project's last-processed timestamp
5. Collect qualifying files grouped by project

If no qualifying files found, print "No new sessions to process since last run." and exit.

---

Step 3 — Extract User Messages

For each qualifying JSONL file, extract user messages using python3:

import json, sys
for line in open(sys.argv[1]):
    line = line.strip()
    if not line:
        continue
    try:
        obj = json.loads(line)
        if obj.get('type') == 'user':
            content = obj.get('message', {}).get('content', '')
            if isinstance(content, list):
                for c in content:
                    if isinstance(c, dict) and c.get('type') == 'text':
                        text = c['text']
                        # Skip system-reminder content
                        if '<system-reminder>' not in text:
                            print(text[:500])
            elif isinstance(content, str) and '<system-reminder>' not in content:
                print(content[:500])
    except:
        pass

Truncate each message to 500 chars — we need intent, not full content.

---

Step 4 — Classify Patterns

Dispatch haiku subagents to classify. Batch sessions by project — one subagent per project (or per 10 sessions if a project has many).

Each subagent receives:

• The extracted user messages (concatenated, separated by ---)
• The project name
• The existing suggestions from self-improve.md (to avoid duplicates)

Subagent prompt:

You are analyzing Claude Code session transcripts to find automation opportunities.

Project: {project_name}

Below are the user's messages from {N} sessions. Look for:

1. **SKILL candidates** — tasks the user triggers manually and repeatedly (e.g., "run tests then commit", "fetch this URL and classify it"). Must appear 2+ times.
2. **AGENT candidates** — multi-step workflows that could run autonomously (e.g., "research these repos, classify, write entries"). Must appear 2+ times.
3. **CONFIG candidates** — preferences the user states repeatedly that should be baked into CLAUDE.md (e.g., "don't add comments", "use integer cents"). Must appear 2+ times.
4. **CODING-STYLE candidates** — corrections the user makes repeatedly to Claude's output (e.g., "use early returns", "don't wrap in try/catch"). Must appear 2+ times.

Existing suggestions (do NOT duplicate these):
{existing_suggestions}

Rules:
- Only report patterns that appear 2+ times across sessions
- One line per finding: [type] "description" — seen Nx
- If nothing qualifies, return "NONE"
- Do not invent patterns. Only report what is clearly repeated in the transcripts.
- Maximum 10 findings per project

SESSION TRANSCRIPTS:
{messages}

---

Step 5 — Update State File

After all subagents complete:

1. Merge suggestions: Collect all non-NONE findings. Deduplicate against existing suggestions in self-improve.md (fuzzy — if a new finding is essentially the same as an existing one, skip it). Append project names to existing suggestions if the same pattern is seen in a new project.

2. Update timestamps: For each processed project, set its timestamp to the newest JSONL file's mtime (ISO format). If the project already has a row, overwrite the timestamp. If not, add a new row.

3. Write the file: Read self-improve.md, update the Processed table and Suggestions section, write it back. Keep the HTML comments intact. Remove the _default row once real projects are added.

4. Sync to repo: Copy the updated file to the claude-config repo:

   cp ~/.claude/learning/self-improve.md ~/data/code/github/claude-config/learning/self-improve.md
   

---

Step 6 — Present Results

Print a summary:

Self-Improve Analysis Complete
  Sessions processed: 12 across 4 projects
  New suggestions: 3

  [skill] "description" — seen 4x across project1, project2
  [config] "description" — seen 3x across project3
  [coding-style] "description" — seen 2x across project1

  Next: review suggestions, implement with relevant skill/config, then delete the line from self-improve.md

If no new suggestions found: "No new patterns detected. Your setup is well-optimized or more sessions needed."

---

Gotchas

• JSONL files can be large (400K+). Extract user messages via python3 script, don't read the full file into Claude context.
• Some JSONL files are mostly hook progress messages with few user messages — skip files with <2 user messages.
• Subagent model must be haiku — this is a classification task on large text, not creative work.
• Session files from subagents (in subagents/ directories) are excluded — they contain Claude-to-Claude dialogue, not user patterns.

Quick Help

What: Analyzes your Claude session history to find repeated patterns worth automating as skills, agents, config rules, or coding style fixes. Usage:

• /my-self-improve — process current project since last run
• /my-self-improve all — all projects since last run
• /my-self-improve alltime — all projects, reprocess everything Output: Updates ~/.claude/learning/self-improve.md with actionable suggestions, sorted by frequency. When: Periodically (weekly-ish), or when you feel like you keep repeating yourself.