Agent skill

Skill Factory

by sangrokjung in sangrokjung/claude-forge

Analyze session work and automatically convert reusable patterns into Claude Code skills.

MITAuto-check passedAgent Workflows

Install Skill Factory

skills CLI
$ npx skills add sangrokjung/claude-forge --skill skill-factory -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sangrokjung/claude-forge skill-factory --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sangrokjung/claude-forge.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/skill-factory .claude/skills/skill-factory && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
skill-factory
GitHub stars
852
Token cost
~2.7k tokens
SKILL.md length
659 words
Files
7 (incl. scripts, references)
Skills in repo
24
Repo updated
First seen
Licence
MIT

At a glance

Analyze session work and automatically convert reusable patterns into Claude Code skills.

  • Works in 5 steps: Session Analysis → Similarity Check → Blueprint → …
  • Make this a skill
  • SKILL.md covers Parameter Parsing, Phase 1: Session Analysis, Phase 2: Similarity Check and Phase 3: Blueprint, plus 5 more sections
  • Runs Shell and Python scripts from its folder; calls git, bash and python3

What it does

Skill Factory is an agent skill from sangrokjung/claude-forge. Analyze session work and automatically convert reusable patterns into Claude Code skills. Use when: "세션을 스킬로", "스킬 만들어", "이거 스킬로", "skill factory", "이 작업 자동화해", "스킬 추출", "make this a skill", "extract skill", "convert to skill", "스킬 팩토리", "자동 스킬 생성". Differs from skill-creator (archived) and manage-skills (drift detection): this skill actively analyzes sessions, checks for duplicates, and creates skills via Agent Teams.

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/decision-tree.md`, `references/skill-templates.md` and `references/team-composition.md`).

It sits in Agent Workflows, covering Skill management, GitOps and Skill authoring. The repository describes itself as: oh-my-zsh for Claude Code — 16 agents, 35 commands, 32 skills, 21 safety hooks in one install. v4.0 adds an adversarial review loop: a second agent that never sees the first… The licence is MIT.

When your agent uses it

  • Make this a skill
  • Convert to skill

Example prompts

  • “skill factory”
  • “make this a skill”
  • “extract skill”
  • “/skill-factory”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Session Analysis
  2. Similarity Check
  3. Blueprint
  4. Execution
  5. Registration

What it can do on your machine

Read from SKILL.md and the folder at commit 34d881d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Shell and Python), which the agent can run.

    Shell commands in SKILL.md call:

    • git
    • bash
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Skill Factory loads about 2.7k tokens when it runs, and up to ~5.8k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 659 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~109
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from sangrokjung/claude-forge at commit 34d881d, republished under its MIT licence (© sangrokjung). 659 words, ~2,652 tokens.

Download SKILL.mdSave it as .claude/skills/skill-factory/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
skill-factory
description
Analyze session work and automatically convert reusable patterns into Claude Code skills. Use when: "세션을 스킬로", "스킬 만들어", "이거 스킬로", "skill factory", "이 작업 자동화해", "스킬 추출", "make this a skill", "extract skill", "convert to skill", "스킬 팩토리", "자동 스킬 생성". Differs from skill-creator (archived) and manage-skills (drift detection): this skill actively analyzes sessions, checks for duplicates, and creates skills via Agent Teams.
disable-model-invocation
true
argument-hint
[--dry-run] [--no-team] [--target name] [--scope global|project]

Skill Factory

Automated pipeline: session analysis -> duplicate check -> skill creation. Requires: Python 3.8+, bash, git. Agent Teams path requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1.

Existing SkillRoleskill-factory Difference
skill-creator (archived)Manual 6-step guideAutomated pipeline
manage-skillsDrift detection (verify-* skills)Proactive skill generation (manage-skills verifies existing; skill-factory creates new)
continuous-learningPassive pattern extractionOn-demand + team execution

Parameter Parsing

Parse $ARGUMENTS for flags:

FlagDefaultDescription
--dry-runfalseAnalyze and report only, no file creation
--no-teamfalseRun sequentially without Agent Teams
--target(auto)Specific pattern name to extract
--scopeglobalglobal (~/. claude/skills/) or project (.claude/skills/)

If no arguments, run full auto-detection pipeline.

Phase 1: Session Analysis

Collect what happened in this session:

bash
# Uncommitted changes
git diff HEAD --name-only 2>/dev/null

# Recent commits on current branch
git log --oneline -20 2>/dev/null

# Branch diff from main
git diff main...HEAD --name-only 2>/dev/null

From collected changes, identify candidate patterns - repeatable workflows that appeared:

  1. Multi-step sequences - 3+ actions performed in consistent order
  2. Tool combinations - Specific tools used together (e.g., Grep + Read + Edit)
  3. Domain procedures - File types or directories accessed with specific operations
  4. Repeated transformations - Same type of change applied to multiple files

If --target is specified, focus analysis on that named pattern only.

For each candidate, produce a JSON entry (internal, not shown to user):

json
{
  "name": "pattern-name",
  "description": "What was done repeatedly",
  "files": ["path/a.ts", "path/b.ts"],
  "steps": ["Step1", "Step2", "Step3"],
  "step_count": 3
}

Present findings to user:

Session Analysis Complete

Candidate Patterns Found: N

1. [pattern-name] - "Description of what was done repeatedly"
   Files: path/a.ts, path/b.ts (N files)
   Steps: Step1 -> Step2 -> Step3

2. [pattern-name] - "Description"
   ...

Which patterns should become skills? (select or 'all')

Wait for user selection before proceeding.

Phase 2: Similarity Check

For each selected pattern, check against existing inventory.

Step 1: Scan inventory

bash
bash $HOME/.claude/skills/skill-factory/scripts/scan-inventory.sh --scope all > /tmp/sf-manifest.json

Step 2: Score similarity

bash
python3 $HOME/.claude/skills/skill-factory/scripts/similarity-scorer.py \
  --candidate "<pattern description>" \
  --candidate-name "<pattern-name>" \
  --manifest /tmp/sf-manifest.json \
  --top 3

Step 3: Apply decision logic (see references/decision-tree.md)

Present results to user:

Similarity Check Results

Pattern: "pdf-batch-edit"
  Top match: nano-pdf (score: 0.72) -> MERGE
  Recommendation: Extend nano-pdf with batch operations

Pattern: "config-updater"
  Top match: init-project (score: 0.45) -> UPDATE
  Recommendation: Add config-update subsection to init-project

Pattern: "api-load-test"
  Top match: e2e (score: 0.24) -> CREATE
  Recommendation: Create new skill

Action for each pattern? (CREATE / UPDATE / MERGE / SKIP)

Wait for user decision per pattern.

Phase 3: Blueprint

For each CREATE/UPDATE/MERGE decision, design the skill structure.

CREATE Blueprint

Select template type from references/skill-templates.md:

  • Workflow for sequential processes
  • Task/Tool for operation collections
  • Reference for domain knowledge
  • Verification for automated checks

Generate blueprint:

Blueprint: api-load-test

Type: Workflow
Scope: global (~/.claude/skills/)
Structure:
  api-load-test/
  ├── SKILL.md (~200 lines)
  │   ├── Frontmatter: name, description with triggers
  │   ├── Overview
  │   ├── Prerequisites
  │   ├── Workflow (4 steps)
  │   └── Output Format
  └── scripts/
      └── run-load-test.sh

Key sections:
  1. Target URL configuration
  2. Load profile definition
  3. Test execution
  4. Results analysis

Approve this blueprint? (y/n/edit)

Wait for user approval.

UPDATE Blueprint

For UPDATE verdicts (score 0.3-0.6), plan a lightweight addition to the existing skill:

UPDATE Blueprint: config-updater -> init-project

Target skill: ~/.claude/skills/init-project/SKILL.md
Action: Add subsection "## Config Update" with steps
Estimated diff: +20-40 lines in existing SKILL.md
MERGE Blueprint

For MERGE verdicts (score 0.6-0.8), plan a significant extension of the existing skill:

MERGE Blueprint: pdf-batch-edit -> nano-pdf

Target skill: ~/.claude/skills/nano-pdf/SKILL.md
Sections to add: "## Batch Operations" (new workflow section)
Scripts to add: scripts/batch-process.sh
Estimated diff: +60-100 lines in SKILL.md, +1 script

Phase 4: Execution

Two paths based on --no-team flag and Agent Teams availability.

Check Agent Teams availability:

bash
[ "${CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS:-0}" = "1" ] && echo "teams" || echo "no-team"

If --no-team is set or env var is missing/0, use Path B automatically.

Path A: Agent Teams (default)

Read references/team-composition.md for full team details.

Team: 3 teammates (tami, jiwon, duri)

TeamCreate -> "skill-factory-run"

TaskCreate -> tami's analysis tasks (T1-T6)
TaskCreate -> jiwon's creation tasks (T7-T12, blocked by T6)
TaskCreate -> duri's validation tasks (T13-T18, blocked by T12)

Task -> tami (Explore, sonnet, blue)
  "Analyze session, run scan-inventory.sh, run similarity-scorer.py, report findings"

Task -> jiwon (general-purpose, sonnet, green)
  "For CREATE: read skill-templates.md, create SKILL.md + resources based on blueprint"
  "For UPDATE/MERGE: read target skill, apply diff from blueprint, add new sections/scripts"

Task -> duri (general-purpose, sonnet, yellow)
  "Run validate-skill.sh, verify triggers, register skill"

Pipeline:

  1. tami completes analysis -> reports to lead
  2. Lead confirms with user (Checkpoint 1-2)
  3. jiwon creates skill files -> reports to lead
  4. Lead confirms with user (Checkpoint 3)
  5. duri validates and registers -> reports to lead
  6. Lead confirms with user (Checkpoint 4)
  7. Shutdown all teammates, TeamDelete
Show full SKILL.md (273 more words)Show less
Path B: Sequential (--no-team)

Execute the same phases inline without Agent Teams:

  1. Run scan-inventory.sh and similarity-scorer.py directly
  2. Checkpoint 1-2: Present similarity results, ask user for CREATE/UPDATE/MERGE/SKIP per pattern
  3. Design blueprint based on template selection
  4. Checkpoint 3: Present blueprint, wait for user approval
  5. Create/update skill directory and files based on approved blueprint
  6. Run validate-skill.sh to verify
  7. Checkpoint 4: Present validation results, ask user to register or edit
  8. Register and log
--dry-run Mode

Stop after Phase 3 (blueprint). Print the blueprint and exit without creating files:

DRY RUN COMPLETE

Patterns analyzed: N
Decisions: X CREATE, Y MERGE, Z SKIP
Blueprints generated: X

No files were created. Remove --dry-run to execute.

Phase 5: Registration

After validation passes:

  1. Log creation - Append to ~/.claude/skill-factory.log:

    [2026-02-18T14:30:00] CREATED api-load-test (global) from session patterns
    [2026-02-18T14:30:00] MERGED batch-operations into nano-pdf
  2. Scope placement:

    • --scope global: ~/.claude/skills/<name>/
    • --scope project: .claude/skills/<name>/
  3. Optional CLAUDE.md update: If project-scoped, offer to add skill reference to project CLAUDE.md.

Output Format

Final report after all patterns are processed:

Skill Factory Report

Session: <branch-name or "main">
Patterns found: N
Patterns processed: M

Results:
  CREATED: api-load-test (global) - 4 files, 180 lines
  MERGED:  batch-ops into nano-pdf - 2 sections added
  SKIPPED: data-transform (0.85 match with data-research)

Files created/modified:
  ~/.claude/skills/api-load-test/SKILL.md
  ~/.claude/skills/api-load-test/scripts/run-load-test.sh
  ~/.claude/skills/nano-pdf/SKILL.md (updated)

Validation: ALL PASS
Log: ~/.claude/skill-factory.log

Next steps:
  Test the new skill: /api-load-test
  Review: cat ~/.claude/skills/api-load-test/SKILL.md

Error Handling

SituationAction
No git historyAnalyze only staged/unstaged changes
No patterns found"No reusable patterns detected. Try after a more complex session."
scan-inventory.sh failsFall back to manual inventory (glob SKILL.md files)
similarity-scorer.py failsSkip similarity check, default to CREATE
Agent Teams unavailableAuto-fallback to --no-team mode
validate-skill.sh failsShow errors, let user fix or cancel
User cancels at checkpointAbort gracefully, no partial files left
FilePurposeWhen to Read
scripts/scan-inventory.shScan all skills/commands/agents to JSONPhase 2 - always
scripts/similarity-scorer.py4-dim similarity scoringPhase 2 - per pattern
scripts/validate-skill.shValidate created skill structurePhase 5 - after creation
references/decision-tree.mdCREATE/UPDATE/MERGE/SKIP logicPhase 2 - for decisions
references/team-composition.mdtami/jiwon/duri team setupPhase 4 - Agent Teams path
references/skill-templates.mdSkill type templatesPhase 3 - blueprint design

© sangrokjung, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references) in skills/skill-factory of sangrokjung/claude-forge.

  • SKILL.md
  • references/decision-tree.md
  • references/skill-templates.md
  • references/team-composition.md
  • scripts/scan-inventory.sh
  • scripts/similarity-scorer.py
  • scripts/validate-skill.sh

Open the folder on GitHubat commit 34d881d

Compare with similar skills

Skill Factory next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Skill Factory compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Skill Factory this skillsangrokjung/claude-forge852—~2.7kAutomated safety check: PassMIT
Darwin Skill Optimizeralchaincyf/darwin-skill6.2k1 repos~4.7kAutomated safety check: PassMIT
Skill Creatorzhayujie/CowAgent47k—~4.7kAutomated safety check: NotesMIT
Open-Science Skill Creatoraipoch/open-science5.5k—~1.7kAutomated safety check: PassApache-2.0
Skill Quality ReviewerGalaxy-Dawn/claude-scholar5.7k1 repos~3kAutomated safety check: PassMIT
Prismer Skill CreatorPrismer-AI/PrismerCloud1.6k—~2.6kAutomated safety check: NotesMIT

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Categories

Questions about Skill Factory

What does Skill Factory do?

Analyze session work and automatically convert reusable patterns into Claude Code skills. Skill Factory is an agent skill from sangrokjung/claude-forge. Analyze session work and automatically convert reusable patterns into Claude Code skills.

When should I use Skill Factory?

Skill Factory fits situations like: make this a skill; convert to skill.

How do I install Skill Factory in Claude Code?

Run `npx skills add sangrokjung/claude-forge --skill skill-factory -a claude-code`. Or copy the skill folder (skills/skill-factory in sangrokjung/claude-forge) into .claude/skills/skill-factory in your project. Claude Code loads it when a task matches its description.

How do I install Skill Factory in Codex?

Run `npx skills add sangrokjung/claude-forge --skill skill-factory -a codex`. Or copy the skill folder (skills/skill-factory in sangrokjung/claude-forge) into .agents/skills/skill-factory in your project. Codex loads it when a task matches its description.

Can I use Skill Factory in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sangrokjung/claude-forge --skill skill-factory -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-factory, .gemini/skills/skill-factory, .github/skills/skill-factory and .opencode/skills/skill-factory in your project.

What does Skill Factory need to run?

Going by SKILL.md and its folder, Skill Factory needs a shell and Python for the scripts in its folder and the command-line tools its instructions call (git, bash and python3). Our summary lists: Python 3; A Bash shell.

Does Skill Factory access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Skill Factory safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Skill Factory use?

Skill Factory is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skill Factory use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.2k tokens, read only when the agent opens those files.

What are the alternatives to Skill Factory?

Skills that share tags, products or a category with Skill Factory: Darwin Skill Optimizer (alchaincyf/darwin-skill, 6.2k stars), Skill Creator (zhayujie/CowAgent, 47k stars), Open-Science Skill Creator (aipoch/open-science, 5.5k stars) and Skill Quality Reviewer (Galaxy-Dawn/claude-scholar, 5.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Factory?

sangrokjung (a GitHub user) maintains it in sangrokjung/claude-forge, which has 852 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on September 3, 2026.

Source: sangrokjung/claude-forge on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.