# Reference Curator - Complete Skill Set

This document contains all 6 skills for curating, processing, and exporting reference documentation.

---

# 1. Reference Discovery

Searches for authoritative sources, validates credibility, and produces curated URL lists for crawling.

## Source Priority Hierarchy

| Tier | Source Type | Examples |
|------|-------------|----------|
| **Tier 1** | Official documentation | docs.anthropic.com, docs.claude.com, platform.openai.com/docs |
| **Tier 1** | Engineering blogs (official) | anthropic.com/news, openai.com/blog |
| **Tier 1** | Official GitHub repos | github.com/anthropics/*, github.com/openai/* |
| **Tier 2** | Research papers | arxiv.org, papers with citations |
| **Tier 2** | Verified community guides | Cookbook examples, official tutorials |
| **Tier 3** | Community content | Blog posts, tutorials, Stack Overflow |

## Discovery Workflow

### Step 1: Define Search Scope

```python
search_config = {
    "topic": "prompt engineering",
    "vendors": ["anthropic", "openai", "google"],
    "source_types": ["official_docs", "engineering_blog", "github_repo"],
    "freshness": "past_year",
    "max_results_per_query": 20
}
```

### Step 2: Generate Search Queries

```python
def generate_queries(topic, vendors):
    queries = []
    for vendor in vendors:
        queries.append(f"site:docs.{vendor}.com {topic}")
        queries.append(f"site:{vendor}.com/docs {topic}")
        queries.append(f"site:{vendor}.com/blog {topic}")
        queries.append(f"site:github.com/{vendor} {topic}")
    queries.append(f"site:arxiv.org {topic}")
    return queries
```

### Step 3: Validate and Score Sources

```python
def score_source(url, title):
    score = 0.0
    if any(d in url for d in ['docs.anthropic.com', 'docs.claude.com', 'docs.openai.com']):
        score += 0.40  # Tier 1 official docs
    elif any(d in url for d in ['anthropic.com', 'openai.com', 'google.dev']):
        score += 0.30  # Tier 1 official blog/news
    elif 'github.com' in url and any(v in url for v in ['anthropics', 'openai', 'google']):
        score += 0.30  # Tier 1 official repos
    elif 'arxiv.org' in url:
        score += 0.20  # Tier 2 research
    else:
        score += 0.10  # Tier 3 community
    return min(score, 1.0)

def assign_credibility_tier(score):
    if score >= 0.60:
        return 'tier1_official'
    elif score >= 0.40:
        return 'tier2_verified'
    else:
        return 'tier3_community'
```

## Output Format

```json
{
  "discovery_date": "2025-01-28T10:30:00",
  "topic": "prompt engineering",
  "total_urls": 15,
  "urls": [
    {
      "url": "https://docs.anthropic.com/en/docs/prompt-engineering",
      "title": "Prompt Engineering Guide",
      "credibility_tier": "tier1_official",
      "credibility_score": 0.85,
      "source_type": "official_docs",
      "vendor": "anthropic"
    }
  ]
}
```

---

# 2. Web Crawler Orchestrator

Manages crawling operations using Firecrawl MCP with rate limiting and format handling.

## Crawl Configuration

```yaml
firecrawl:
  rate_limit:
    requests_per_minute: 20
    concurrent_requests: 3
  default_options:
    timeout: 30000
    only_main_content: true
```

## Crawl Workflow

### Determine Crawl Strategy

```python
def select_strategy(url):
    if url.endswith('.pdf'):
        return 'pdf_extract'
    elif 'github.com' in url and '/blob/' in url:
        return 'raw_content'
    elif any(d in url for d in ['docs.', 'documentation']):
        return 'scrape'
    else:
        return 'scrape'
```

### Execute Firecrawl

```python
# Single page scrape
firecrawl_scrape(
    url="https://docs.anthropic.com/en/docs/prompt-engineering",
    formats=["markdown"],
    only_main_content=True,
    timeout=30000
)

# Multi-page crawl
firecrawl_crawl(
    url="https://docs.anthropic.com/en/docs/",
    max_depth=2,
    limit=50,
    formats=["markdown"]
)
```

### Rate Limiting

```python
class RateLimiter:
    def __init__(self, requests_per_minute=20):
        self.rpm = requests_per_minute
        self.request_times = deque()

    def wait_if_needed(self):
        now = time.time()
        while self.request_times and now - self.request_times[0] > 60:
            self.request_times.popleft()
        if len(self.request_times) >= self.rpm:
            wait_time = 60 - (now - self.request_times[0])
            if wait_time > 0:
                time.sleep(wait_time)
        self.request_times.append(time.time())
```

## Error Handling

| Error | Action |
|-------|--------|
| Timeout | Retry once with 2x timeout |
| Rate limit (429) | Exponential backoff, max 3 retries |
| Not found (404) | Log and skip |
| Access denied (403) | Log, mark as `failed` |

---

# 3. Content Repository

Manages MySQL storage for the reference library. Handles document storage, version control, deduplication, and retrieval.

## Core Operations

**Store New Document:**
```python
def store_document(cursor, source_id, title, url, doc_type, raw_content_path):
    sql = """
    INSERT INTO documents (source_id, title, url, doc_type, crawl_date, crawl_status, raw_content_path)
    VALUES (%s, %s, %s, %s, NOW(), 'completed', %s)
    ON DUPLICATE KEY UPDATE
        version = version + 1,
        crawl_date = NOW(),
        raw_content_path = VALUES(raw_content_path)
    """
    cursor.execute(sql, (source_id, title, url, doc_type, raw_content_path))
    return cursor.lastrowid
```

**Check Duplicate:**
```python
def is_duplicate(cursor, url):
    cursor.execute("SELECT doc_id FROM documents WHERE url_hash = SHA2(%s, 256)", (url,))
    return cursor.fetchone() is not None
```

## Table Quick Reference

| Table | Purpose | Key Fields |
|-------|---------|------------|
| `sources` | Authorized content sources | source_type, credibility_tier, vendor |
| `documents` | Crawled document metadata | url_hash (dedup), version, crawl_status |
| `distilled_content` | Processed summaries | review_status, compression_ratio |
| `review_logs` | QA decisions | quality_score, decision |
| `topics` | Taxonomy | topic_slug, parent_topic_id |

## Status Values

- **crawl_status:** `pending` → `completed` | `failed` | `stale`
- **review_status:** `pending` → `in_review` → `approved` | `needs_refactor` | `rejected`
- **decision:** `approve` | `refactor` | `deep_research` | `reject`

---

# 4. Content Distiller

Transforms raw crawled content into structured, high-quality reference materials.

## Distillation Goals

1. **Compress** - Reduce token count while preserving essential information
2. **Structure** - Organize content for easy retrieval and reference
3. **Extract** - Pull out code snippets, key concepts, and actionable patterns
4. **Annotate** - Add metadata for searchability and categorization

## Extract Key Components

**Extract Code Snippets:**
```python
def extract_code_snippets(content):
    pattern = r'```(\w*)\n([\s\S]*?)```'
    snippets = []
    for match in re.finditer(pattern, content):
        snippets.append({
            "language": match.group(1) or "text",
            "code": match.group(2).strip(),
            "context": get_surrounding_text(content, match.start(), 200)
        })
    return snippets
```

**Extract Key Concepts:**
```python
def extract_key_concepts(content, title):
    prompt = f"""
    Analyze this document and extract key concepts:

    Title: {title}
    Content: {content[:8000]}

    Return JSON with:
    - concepts: [{{"term": "...", "definition": "...", "importance": "high|medium|low"}}]
    - techniques: [{{"name": "...", "description": "...", "use_case": "..."}}]
    - best_practices: ["..."]
    """
    return claude_extract(prompt)
```

## Summary Template

```markdown
# {title}

**Source:** {url}
**Type:** {source_type} | **Tier:** {credibility_tier}

## Executive Summary
{2-3 sentence overview}

## Key Concepts
{bulleted list of core concepts}

## Techniques & Patterns
{extracted techniques with use cases}

## Code Examples
{relevant code snippets}

## Best Practices
{actionable recommendations}
```

## Quality Metrics

| Metric | Target |
|--------|--------|
| Compression Ratio | 25-35% of original |
| Key Concept Coverage | ≥90% of important terms |
| Code Snippet Retention | 100% of relevant examples |

---

# 5. Quality Reviewer

Evaluates distilled content, routes decisions, and triggers refactoring or additional research.

## Review Workflow

```
[Distilled Content]
       │
       ▼
┌─────────────────┐
│ Score Criteria  │ → accuracy, completeness, clarity, PE quality, usability
└─────────────────┘
       │
       ├── ≥ 0.85 → APPROVE → markdown-exporter
       ├── 0.60-0.84 → REFACTOR → content-distiller (with instructions)
       ├── 0.40-0.59 → DEEP_RESEARCH → web-crawler (with queries)
       └── < 0.40 → REJECT → archive with reason
```

## Scoring Criteria

| Criterion | Weight | Checks |
|-----------|--------|--------|
| **Accuracy** | 0.25 | Factual correctness, up-to-date info, proper attribution |
| **Completeness** | 0.20 | Covers key concepts, includes examples, addresses edge cases |
| **Clarity** | 0.20 | Clear structure, concise language, logical flow |
| **PE Quality** | 0.25 | Demonstrates techniques, before/after examples, explains why |
| **Usability** | 0.10 | Easy to reference, searchable keywords, appropriate length |

## Calculate Final Score

```python
WEIGHTS = {
    "accuracy": 0.25,
    "completeness": 0.20,
    "clarity": 0.20,
    "prompt_engineering_quality": 0.25,
    "usability": 0.10
}

def calculate_quality_score(assessment):
    return sum(
        assessment[criterion]["score"] * weight
        for criterion, weight in WEIGHTS.items()
    )
```

## Route Decision

```python
def determine_decision(score, assessment):
    if score >= 0.85:
        return "approve", None, None
    elif score >= 0.60:
        instructions = generate_refactor_instructions(assessment)
        return "refactor", instructions, None
    elif score >= 0.40:
        queries = generate_research_queries(assessment)
        return "deep_research", None, queries
    else:
        return "reject", f"Quality score {score:.2f} below minimum", None
```

## Prompt Engineering Quality Checklist

- [ ] Demonstrates specific techniques (CoT, few-shot, etc.)
- [ ] Shows before/after examples
- [ ] Explains *why* techniques work, not just *what*
- [ ] Provides actionable patterns
- [ ] Includes edge cases and failure modes
- [ ] References authoritative sources

---

# 6. Markdown Exporter

Exports approved content as structured markdown files for Claude Projects or fine-tuning.

## Export Structure

**Nested by Topic (recommended):**
```
exports/
├── INDEX.md
├── prompt-engineering/
│   ├── _index.md
│   ├── 01-chain-of-thought.md
│   └── 02-few-shot-prompting.md
├── claude-models/
│   ├── _index.md
│   └── 01-model-comparison.md
└── agent-building/
    └── 01-tool-use.md
```

## Document File Template

```python
def generate_document_file(doc, include_metadata=True):
    content = []
    if include_metadata:
        content.append("---")
        content.append(f"title: {doc['title']}")
        content.append(f"source: {doc['url']}")
        content.append(f"vendor: {doc['vendor']}")
        content.append(f"tier: {doc['credibility_tier']}")
        content.append(f"quality_score: {doc['quality_score']:.2f}")
        content.append("---")
        content.append("")
    content.append(doc['structured_content'])
    return "\n".join(content)
```

## Fine-tuning Export (JSONL)

```python
def export_fine_tuning_dataset(content_list, config):
    with open('fine_tuning.jsonl', 'w') as f:
        for doc in content_list:
            sample = {
                "messages": [
                    {"role": "system", "content": "You are an expert on AI and prompt engineering."},
                    {"role": "user", "content": f"Explain {doc['title']}"},
                    {"role": "assistant", "content": doc['structured_content']}
                ],
                "metadata": {
                    "source": doc['url'],
                    "topic": doc['topic_slug'],
                    "quality_score": doc['quality_score']
                }
            }
            f.write(json.dumps(sample) + '\n')
```

## Cross-Reference Generation

```python
def add_cross_references(doc, all_docs):
    related = []
    doc_concepts = set(c['term'].lower() for c in doc['key_concepts'])

    for other in all_docs:
        if other['doc_id'] == doc['doc_id']:
            continue
        other_concepts = set(c['term'].lower() for c in other['key_concepts'])
        overlap = len(doc_concepts & other_concepts)
        if overlap >= 2:
            related.append({
                "title": other['title'],
                "path": generate_relative_path(doc, other),
                "overlap": overlap
            })

    return sorted(related, key=lambda x: x['overlap'], reverse=True)[:5]
```

---

# Integration Flow

| From | Output | To |
|------|--------|-----|
| **reference-discovery** | URL manifest | web-crawler |
| **web-crawler** | Raw content + manifest | content-repository |
| **content-repository** | Document records | content-distiller |
| **content-distiller** | Distilled content | quality-reviewer |
| **quality-reviewer** (approve) | Approved IDs | markdown-exporter |
| **quality-reviewer** (refactor) | Instructions | content-distiller |
| **quality-reviewer** (deep_research) | Queries | web-crawler |