
skill-builder
by Nnnsightnnn
Context system for Claude Code with memory management, skills, hooks, and pain point tracking.
SKILL.md
name: Skill Builder description: Transforms AI pain points into working skills. Use when creating new skills from identified patterns. allowed-tools: Read, Write, Edit, Grep, Glob, Bash
Skill Builder Skill
Purpose
Transform AI pain points into working skills. This skill is the second stage of the self-improvement loop, converting identified error patterns into automated solutions.
Auto-Activation Triggers
This skill activates when:
- AI Error Learner escalates a pain point (3+ occurrences)
- User requests "create a skill for [problem]"
- High-priority pain point needs resolution
- Manual invocation with pain point ID
CRITICAL: Quality Standards
Every skill must be complete, testable, and maintainable.
Required Skill Components
YAML Frontmatter → name, description, allowed-tools
Purpose Section → Clear explanation of what and why
Triggers Section → Specific, observable conditions
Workflow Section → Complete, sequential steps
Edge Cases Section → Known exceptions and handling
Error Handling → Recovery from failures
Metadata Section → Version, date, category
Skill Creation Architecture
┌─────────────────────────────────────────────────────────┐
│ SKILL BUILDER FLOW │
│ │
│ Pain Point Investigation Design Create │
│ ┌────────┐ ┌──────────┐ ┌───────┐ ┌───────┐ │
│ │ Select │────▶│ Deep │───▶│Trigger│──▶│ Write │ │
│ │ Pain │ │ Dive │ │ & Flow│ │ Skill │ │
│ │ Point │ │ │ │ │ │ File │ │
│ └────────┘ └──────────┘ └───────┘ └───────┘ │
│ │ │
│ ┌─────▼───┐ │
│ │ Verify │ │
│ │ & │ │
│ │ Update │ │
│ └─────────┘ │
└─────────────────────────────────────────────────────────┘
Core Workflow
Step 1: Select Pain Point
Choose pain point to address:
By ID:
grep -A 20 "AI-PAIN-0012" .claude/pain-points/ai-pain-points.md
By highest occurrence:
python3 -c "
import json
data = json.load(open('.claude/pain-points/ai-error-history.json'))
sorted_errors = sorted(data.get('errors', {}).items(), key=lambda x: x[1].get('count', 0), reverse=True)
for fp, info in sorted_errors[:5]:
print(f\"{info.get('count', 0):3d} | {fp} | {info.get('pain_point_id', 'N/A')}\")
"
Step 2: Deep Investigation
Thoroughly understand the problem:
-
Reproduce the error context
- What actions trigger it?
- What environment conditions exist?
- What files/tools are involved?
-
Analyze root cause
- Is it a knowledge gap? (needs documentation)
- Is it a process gap? (needs workflow)
- Is it a tool gap? (needs automation)
-
Review existing solutions
- Do similar skills exist?
- Are there related patterns in memory?
- What approaches have been tried?
Step 3: Design Skill Triggers
Define specific, observable triggers:
Good Triggers:
- "When user runs
pytestand tests fail with import errors" - "When creating files in
/scripts/directory" - "When git push is rejected due to hooks"
Bad Triggers:
- "When something goes wrong" (too vague)
- "When user is frustrated" (not observable)
- "When appropriate" (undefined)
Step 4: Design Workflow
Create complete, sequential steps:
- Each step should be atomic - one clear action
- Include decision points - what to check before proceeding
- Show concrete commands - exact syntax to use
- Handle branches - what if step fails?
Step 5: Create Skill File
Write the complete skill using this template:
---
name: [Skill Name]
description: [One-line description]. Use when [trigger condition].
allowed-tools: [Required tools]
---
# [Skill Name] Skill
## Purpose
[Clear explanation of what this skill does and why it exists]
## Auto-Activation Triggers
This skill activates when:
- [Observable trigger 1]
- [Observable trigger 2]
- [User phrase triggers]
## CRITICAL: [Key Protocol]
[Most important behavioral requirement]
## Core Workflow
### Step 1: [First Step]
[Detailed instructions with commands]
### Step 2: [Second Step]
[Detailed instructions with commands]
[Continue for all steps...]
## Edge Cases
### [Edge Case Name]
**Condition**: [When this occurs]
**Handling**: [What to do]
## Error Handling
### If [Failure Mode]
1. [Recovery step 1]
2. [Recovery step 2]
3. [Fallback behavior]
## Skill Metadata
**Version:** 1.0.0
**Created:** YYYY-MM-DD
**Category:** [Category]
**Origin:** AI-PAIN-NNNN
Step 6: Update Documentation
After creating skill:
-
Update CLAUDE.md (if skill is frequently used)
**[SKILL-NNNNN]** [Brief description] > TRIGGER: [When to use] -
Update skill metrics
{ "skills": { "new-skill-name": { "invocations": 0, "successes": 0, "failures": 0, "origin": "AI-PAIN-NNNN", "created": "YYYY-MM-DD" } } } -
Update error history
- Mark pain point as resolved
- Link to new skill
-
Log in episodic memory
### New Skill Created - Skill: [skill-name] - Origin: AI-PAIN-NNNN - Purpose: [brief description]
Step 7: Verify Integration
Confirm skill is properly integrated:
# Check skill file exists and has valid frontmatter
head -10 .claude/skills/new-skill-name/SKILL.md
# Verify YAML is valid
python3 -c "import yaml; yaml.safe_load(open('.claude/skills/new-skill-name/SKILL.md').read().split('---')[1])"
# Check metrics file updated
cat .claude/skills/skill-metrics.json | python3 -m json.tool
Edge Cases
Pain Point Has No Clear Solution
Condition: Investigation doesn't reveal automated solution Handling: Create documentation pattern instead of skill, mark pain point as "documented"
Similar Skill Already Exists
Condition: Existing skill covers 80%+ of use case Handling: Extend existing skill rather than create new one
Solution Requires External Changes
Condition: Fix requires config/environment changes, not automation Handling: Document in quick-reference, create setup skill if applicable
Skill Would Be Too Complex
Condition: Solution requires >400 lines or multiple sub-skills Handling: Break into smaller skills with clear dependencies
Integration Points
With AI Error Learner
- Receives prioritized pain points
- Gets context from error history
- Reports completion status
With Skill Improver
- New skills start at version 1.0.0
- Skill improver monitors effectiveness
- Triggers improvements if skill fails
With Memory System
- Logs skill creation in episodic memory
- May discover related patterns in procedural memory
- Updates quick-reference for common skills
Output Examples
Example 1: Creating Permission Fix Skill
Trigger: AI-PAIN-0012 escalated (script permission errors) Response:
## Skill Creation: Permission Fixer
### Pain Point Analysis
**ID**: AI-PAIN-0012
**Fingerprint**: `BASH-PERMISSION_DENIED-scripts/`
**Occurrences**: 5
### Root Cause
Scripts created by Write tool have 644 permissions by default.
Execution requires 755.
### Skill Design
**Triggers:**
- When creating files in `/scripts/` directory
- When bash command fails with "Permission denied"
- User says "make it executable"
**Workflow:**
1. Detect script creation context
2. Check current permissions
3. Apply chmod +x if needed
4. Verify execution works
### Created: `.claude/skills/permission-fixer/SKILL.md`
[Full skill content...]
### Updates Made
- Added to skill-metrics.json
- Marked AI-PAIN-0012 as resolved
- Logged in episodic memory
Error Handling
If Skill Write Fails
- Save content to temp file
- Report failure with content
- Suggest manual creation
If YAML Frontmatter Invalid
- Validate before writing
- Use template defaults if needed
- Log validation warnings
If Pain Point Not Found
- Search for similar entries
- Offer to create new pain point
- Proceed with available context
Skill Metadata
Version: 1.0.0 Created: 2026-01-16 Category: Self-Improvement Integration: AI Error Learner, Skill Improver, Memory System Maintenance: On-demand (triggered by pain point escalation)
スコア
総合スコア
リポジトリの品質指標に基づく評価
SKILL.mdファイルが含まれている
ライセンスが設定されている
100文字以上の説明がある
GitHub Stars 100以上
1ヶ月以内に更新
10回以上フォークされている
オープンIssueが50未満
プログラミング言語が設定されている
1つ以上のタグが設定されている
レビュー
レビュー機能は近日公開予定です
