skill-distill

name	skill-distill
description	Analyzes AI agent session history across Claude Code, Codex CLI, pi-mono, and Cursor to discover recurring workflows and auto-generate Agent Skills. Use this skill whenever you want to mine your coding history for patterns worth turning into skills, even if you just say something like "what do I keep doing over and over" or "find patterns in my history". Invoke manually with /skill-distill.
allowed-tools	Read, Write, Edit, Bash, Glob, Grep, WebFetch
disable-model-invocation	true
user-invocable	true

Skill Distill

Analyze session history across AI coding tools, find recurring patterns, and generate new skills from them.

Safety

• Never paste raw log content into a skill. Paraphrase and sanitize all examples.
• Before reading any history file, list the exact files for the user and get explicit approval.
• Mask secrets (tokens, passwords, keys) found in commands. Never copy stdout/stderr into skills.
• Use [ and ] instead of angle brackets in generated YAML frontmatter and skill bodies.

Workflow

Run these 6 phases in order. Confirm with the user before starting Phase 5.

Progress checklist:

• Phase 1: Data Collection
• Phase 2: Pattern Extraction
• Phase 3: Suitability Evaluation
• Phase 4: User Selection
• Phase 5: Skill Generation
• Phase 6: Quality Validation

---

Phase 1: Data Collection

Consent Gate

Before reading anything, present these choices and get approval:

1. Which tools: Claude Code, Codex CLI, pi-mono, Cursor (any combination, default: all)
2. Scope: current project or all projects
3. Time range: last N days
4. Files to read: list them explicitly per tool

Running the Extractors

All scripts live in this skill's directory and output normalized JSONL: {"source", "display", "timestamp", "project", "sessionId"}

For single-source runs:

Tool	Command
Claude Code	python3 {skill-dir}/filter-history.py --days {N} --project "{path}" --noise-filter
Codex CLI	python3 {skill-dir}/extract-codex-history.py --days {N} --project "{path}"
pi-mono	python3 {skill-dir}/extract-pi-history.py --days {N} --project "{path}"
Cursor	python3 {skill-dir}/extract-cursor-history.py --days {N} --project "{path}"

For multi-source runs (preferred):

python3 {skill-dir}/merge-histories.py --days {N} --sources claude,codex,pi,cursor --project "{path}" --noise-filter --stats

The merge script calls the individual extractors and deduplicates the output. Use --stats to show per-source counts.

If Claude Code's history.jsonl is too large for the Read tool (>256KB), the filter script handles it. For Cursor, ensure sqlite3 is available and Cursor is not running (locked DB).

---

Phase 2: Pattern Extraction

Analyze the normalized prompts for concrete, recurring task patterns. Focus on what the user actually repeats, not broad categories.

Three-Axis Extraction

WHAT - The goal the user wants to achieve. Examples: "commit with appropriate granularity", "fix lint errors", "review PR feedback"

HOW - The method or workflow repeatedly specified. Examples: "spin up agent team for parallel work", "create git worktree for isolation" HOW patterns cross-cut multiple WHATs. This cross-cutting nature is strong evidence of skillification value.

FLOW - Session-level prompt chains forming a cohesive workflow. Examples: "identify issues -> address each -> commit" repeated across 4 sessions.

Extraction Steps

1. Exclude noise: /clear, /resume, /status, /usage, /plugin, /init, /mcp, empty prompts, bare [Pasted text].
2. For each prompt, identify the WHAT (goal). If it also specifies a HOW (means), count it under both.
3. Group by sessionId, sort chronologically, summarize prompt chains. Record flows appearing 3+ times as FLOW patterns.
4. Name each pattern as verb + object.
5. For HOW patterns, record which WHATs they combined with.
6. For each pattern, record which tools it appeared in.

Counting Rules

• WHAT/HOW contained within a FLOW: count on the FLOW side only.
• Only independently-appearing WHAT/HOW count as standalone WHAT/HOW.
• Still record the cross-cutting nature of HOW even when it appears inside a FLOW.

Cross-Tool Bonus

Patterns appearing in 2+ tools are tool-agnostic and especially valuable. Note the tools for every pattern.

Output Per Pattern

• Pattern name (verb + object), axis (WHAT/HOW/FLOW), occurrence count
• Sources (which tools), 2-3 representative prompts, common steps
• FLOW only: flow steps as arrows, contained WHAT/HOW patterns
• HOW only: which WHATs it combined with
• Variations across occurrences

---

Phase 3: Suitability Evaluation

Cross-Reference Existing Skills

1. Glob for ~/.claude/skills/*/SKILL.md and .claude/skills/*/SKILL.md.
2. Read name + description from each.
3. Classify patterns: Fully covered (exclude), Partially covered (keep the gap), Not covered (keep).

Scoring (Internal)

Evaluate each pattern on:

• Frequency: occurrence count
• Consistency: HIGH (nearly identical each time), MEDIUM (shared core, varying details), LOW (different each time)
• Automatable steps: fraction that is routine/templatable
• Cross-tool reach: patterns in more tools score higher

Ranking (Internal - Do Not Show)

• Recommend: FLOW with freq >= 3 and consistency MEDIUM+, or WHAT with high freq and HIGH consistency, or any pattern in 3+ tools
• Worth skillifying: medium freq or MEDIUM consistency, or HOW across 3+ WHATs, or pattern in 2 tools
• Not suitable: LOW consistency with low automation rate

Present to User

Show a simple list without scores:

Recommended for skillification:
1. [Pattern name] (Nx, tools: X + Y) - [description]

Worth skillifying:
2. [Pattern name] (Nx, tools: X) - [description]

Include 1-2 representative prompts per candidate. Note when a FLOW contains a WHAT/HOW.

---

Phase 4: User Selection

1. Ask which patterns to skillify (multiple OK).
2. Show matching prompts from the relevant sources.
3. Confirm for each selection:
   • Scope: which part of the workflow to cover
   • Variation handling: options within one skill vs. separate skills
   • Placement: global (~/.claude/skills/) or project-specific (.claude/skills/)
   • Trigger phrases for the description
4. If more context is needed, read 2-3 individual session files.

---

Phase 5: Skill Generation

Rules

• Never paste raw session content into a skill.
• Never include angle brackets in YAML frontmatter or body. Use [ and ].
• Never include secrets, tokens, or private identifiers.
• Paraphrase all examples.

Structure

{skill-name}/
  SKILL.md          # Main procedure (required)
  scripts/          # Helper scripts (if needed)
  references/       # Supplementary docs (if needed)

SKILL.md Requirements

Frontmatter must include at minimum:

---
name: my-skill
description: >-
  What this skill does and when to use it. Include trigger phrases.
allowed-tools: Read, Bash, Grep
disable-model-invocation: true
user-invocable: true
---

Body must include:

• Clear numbered workflow steps
• Error handling for common failures
• At least 2 usage examples
• Under 500 lines total

Note in the skill which tools the pattern was observed in, for context.

Placement

• Global: ~/.claude/skills/{skill-name}/
• Project-specific: .claude/skills/{skill-name}/

---

Phase 6: Quality Validation

1. Fetch best practices from https://platform.claude.com/docs/en/agents-and-tools/agent-skills/best-practices using WebFetch.
2. Check the generated skill against those guidelines.
3. Fix any issues and re-validate.
4. Report the result. If everything passes, say "All checks passed."

---

Troubleshooting

Problem	Fix
Cursor SQLite fails	Check that sqlite3 exists and Cursor is not running
pi-mono sessions missing	Verify ~/.pi/agent/sessions/ exists; expand time range
history.jsonl too large	Use filter-history.py (handles files >256KB)
JSON parse error	Narrow scope or change time range
No sessions found	Expand time range or switch to all-projects scope