blind-spot-pass
Use *before* starting work in a domain you don't know well, to surface the "unknown unknowns" — the things you don't even know to ask about — and learn just…
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
$ npx -y skills add sangrokjung/claude-forge --skill skill-factory --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/skill-factoryContext preview
The summary Claude sees to decide when to auto-load this skill.
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
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]"
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 Skill | Role | skill-factory Difference | |----------------|------|--------------------------| | skill-creator (archived) | Manual 6-step guide | Automated pipeline | | manage-skills | Drift detection (verify-* skills) | Proactive skill generation (manage-skills verifies existing; skill-factory creates new) | | continuous-learning | Passive pattern extraction | On-demand + team execution |
Parse `$ARGUMENTS` for flags:
| Flag | Default | Description | |------|---------|-------------| | `--dry-run` | false | Analyze and report only, no file creation | | `--no-team` | false | Run sequentially without Agent Teams | | `--target` | (auto) | Specific pattern name to extract | | `--scope` | global | `global` (~/. claude/skills/) or `project` (.claude/skills/) |
If no arguments, run full auto-detection pipeline.
Collect what happened in this session:
# 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):
{
"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.
For each selected pattern, check against existing inventory.
**Step 1: Scan inventory**
bash $HOME/.claude/skills/skill-factory/scripts/scan-inventory.sh --scope all > /tmp/sf-manifest.json
**Step 2: Score similarity**
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](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.
For each CREATE/UPDATE/MERGE decision, design the skill structure.
Select template type from [references/skill-templates.md](references/skill-templates.md):
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.
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
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
Two paths based on `--no-team` flag and Agent Teams availability.
Check Agent Teams availability:
[ "${CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS:-0}" = "1" ] && echo "teams" || echo "no-team"If `--no-team` is set or env var is missing/0,
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 one's reasoning. MIT.
Repo: sangrokjung/claude-forge
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