# Skill Seekers Intelligence System - Research Topics

**Version:** 1.0
**Status:** 🔬 Research Phase
**Last Updated:** 2026-01-20
**Purpose:** Areas to research and experiment with before/during implementation

---

## 🔬 Research Areas

### 1. Import Analysis Accuracy

**Question:** How accurate is AST-based import analysis for finding relevant skills?

**Hypothesis:** 85-90% accuracy for Python, lower for JavaScript (dynamic imports)

**Research Plan:**
1. **Dataset:** Analyze 10 real-world Python projects
2. **Ground Truth:** Manually identify relevant modules for 50 test files
3. **Measure:** Precision, recall, F1-score
4. **Iterate:** Improve import parser based on results

**Test Cases:**
```python
# Case 1: Simple import
from fastapi import FastAPI
# Expected: Load fastapi.skill

# Case 2: Relative import
from .models import User
# Expected: Load models.skill

# Case 3: Dynamic import
importlib.import_module("my_module")
# Expected: ??? (hard to detect)

# Case 4: Nested import
from src.api.v1.routes import router
# Expected: Load api.skill

# Case 5: Import with alias
from very_long_name import X as Y
# Expected: Load very_long_name.skill
```

**Success Criteria:**
- [ ] >85% precision (no false positives)
- [ ] >80% recall (no false negatives)
- [ ] <100ms parse time per file

**Findings:** (To be filled during research)

---

### 2. Embedding Model Selection

**Question:** Which embedding model is best for code similarity?

**Candidates:**
1. **sentence-transformers/all-MiniLM-L6-v2** (80MB, general purpose)
2. **microsoft/codebert-base** (500MB, code-specific)
3. **sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2** (420MB, multilingual)
4. **Custom fine-tuned** (train on code + docs)

**Evaluation Criteria:**
- **Speed:** Embedding time per file
- **Size:** Model download size
- **Accuracy:** Similarity to ground truth
- **Resource:** RAM/CPU usage

**Benchmark Plan:**
```python
# Dataset: 100 Python files + 20 skills
# For each file:
#   1. Manual: Which skills are relevant? (ground truth)
#   2. Each model: Rank skills by similarity
#   3. Measure: Precision@5, Recall@5, MRR

models = [
    "all-MiniLM-L6-v2",
    "codebert-base",
    "paraphrase-multilingual",
]

results = {}

for model in models:
    results[model] = benchmark(model, dataset)

# Compare
print(results)
```

**Expected Results:**

| Model | Speed | Size | Accuracy | RAM | Winner? |
|-------|-------|------|----------|-----|---------|
| all-MiniLM-L6-v2 | 50ms | 80MB | 75% | 200MB | ✅ Best balance |
| codebert-base | 200ms | 500MB | 85% | 1GB | Too slow/large |
| paraphrase-multi | 100ms | 420MB | 78% | 500MB | Middle ground |

**Success Criteria:**
- [ ] <100ms embedding time
- [ ] <200MB model size
- [ ] >75% accuracy (better than random)

**Findings:** (To be filled during research)

---

### 3. Skill Granularity

**Question:** How fine-grained should skills be?

**Options:**
1. **Coarse:** One skill per 1000+ LOC (e.g., entire backend)
2. **Medium:** One skill per 200-500 LOC (e.g., api, auth, models)
3. **Fine:** One skill per 50-100 LOC (e.g., each endpoint)

**Trade-offs:**

| Granularity | Skills | Skill Size | Context Usage | Accuracy |
|-------------|--------|------------|---------------|----------|
| Coarse | 3-5 | 500 lines | Low | Low (too broad) |
| Medium | 10-15 | 200 lines | Medium | ✅ Good |
| Fine | 50+ | 50 lines | High | Too specific |

**Experiment:**
1. Generate skills at all 3 granularities for skill-seekers
2. Use each set for 1 week of development
3. Measure: usefulness (subjective), context overflow (objective)

**Success Criteria:**
- [ ] Skills feel "right-sized" (not too broad, not too narrow)
- [ ] <5 skills needed for typical task
- [ ] Skills don't overflow context (< 10K tokens total)

**Findings:** (To be filled during research)

---

### 4. Clustering Strategy Performance

**Question:** Which clustering strategy is best?

**Strategies:**
1. **Import-only:** Fast, deterministic
2. **Embedding-only:** Flexible, catches semantics
3. **Hybrid (70/30):** Best of both
4. **Hybrid (50/50):** Equal weight
5. **Hybrid with learning:** Adjust weights based on feedback

**Evaluation:**
```python
# Dataset: 50 files with manually labeled relevant skills

strategies = {
    "import_only": ImportBasedEngine(),
    "embedding_only": EmbeddingBasedEngine(),
    "hybrid_70_30": HybridEngine(0.7, 0.3),
    "hybrid_50_50": HybridEngine(0.5, 0.5),
}

for name, engine in strategies.items():
    scores = evaluate(engine, dataset)
    print(f"{name}: Precision={scores.precision}, Recall={scores.recall}")
```

**Expected Results:**

| Strategy | Precision | Recall | F1 | Speed | Winner? |
|----------|-----------|--------|-----|-------|---------|
| Import-only | 90% | 75% | 82% | 50ms | Fast, precise |
| Embedding-only | 75% | 85% | 80% | 100ms | Flexible |
| Hybrid 70/30 | 88% | 82% | 85% | 80ms | ✅ Best balance |
| Hybrid 50/50 | 85% | 85% | 85% | 80ms | Equal weight |

**Success Criteria:**
- [ ] Hybrid beats both individual strategies
- [ ] <100ms clustering time
- [ ] >85% F1-score

**Findings:** (To be filled during research)

---

### 5. Git Hook Performance

**Question:** How long does skill regeneration take?

**Variables:**
- Codebase size (100, 500, 1000, 5000 files)
- Analysis depth (surface, deep, full)
- Incremental vs full regeneration

**Benchmark:**
```python
# Test on real projects
projects = [
    ("skill-seekers", 140, "Python"),
    ("fastapi", 500, "Python"),
    ("react", 1000, "JavaScript"),
    ("vscode", 5000, "TypeScript"),
]

for name, files, lang in projects:
    # Full regeneration
    time_full = time_regeneration(name, incremental=False)

    # Incremental (10% changed)
    time_incr = time_regeneration(name, incremental=True, changed_ratio=0.1)

    print(f"{name}: Full={time_full}s, Incremental={time_incr}s")
```

**Expected Results:**

| Project | Files | Full | Incremental | Acceptable? |
|---------|-------|------|-------------|-------------|
| skill-seekers | 140 | 3 min | 30 sec | ✅ Yes |
| fastapi | 500 | 8 min | 1 min | ✅ Yes |
| react | 1000 | 15 min | 2 min | ⚠️ Borderline |
| vscode | 5000 | 60 min | 10 min | ❌ Too slow |

**Optimizations if too slow:**
1. Parallel analysis (multiprocessing)
2. Smarter incremental (only changed modules)
3. Background daemon (non-blocking)

**Success Criteria:**
- [ ] <5 min for typical project (500 files)
- [ ] <2 min for incremental update
- [ ] Can run in background without blocking

**Findings:** (To be filled during research)

---

### 6. Context Window Management

**Question:** How to handle context overflow with large skills?

**Problem:** Claude has 200K context, but large projects generate huge skills

**Solutions:**
1. **Skill Summarization:** Compress skills (API signatures only, no examples)
2. **Dynamic Loading:** Load skill sections on-demand
3. **Skill Splitting:** Further split large skills into sub-skills
4. **Priority System:** Load most important skills first

**Experiment:**
```python
# Generate skills for large project (5000 files)
# Measure context usage

skills = generate_skills("large-project")
total_tokens = sum(count_tokens(s) for s in skills)

print(f"Total tokens: {total_tokens}")
print(f"Context budget: 200,000")
print(f"Remaining: {200_000 - total_tokens}")

if total_tokens > 150_000:  # Leave room for conversation
    print("WARNING: Context overflow!")
    # Try solutions
    compressed = compress_skills(skills)
    print(f"After compression: {count_tokens(compressed)}")
```

**Success Criteria:**
- [ ] Skills fit in context (< 150K tokens)
- [ ] Quality doesn't degrade significantly
- [ ] User has control (can choose which skills to load)

**Findings:** (To be filled during research)

---

### 7. Multi-Language Support

**Question:** How well does the system work for non-Python languages?

**Languages to Support:**
1. **Python** (primary, best support)
2. **JavaScript/TypeScript** (common frontend)
3. **Go** (backend microservices)
4. **Rust** (systems programming)
5. **Java** (enterprise)

**Challenges:**
- Import syntax varies (import vs require vs use)
- Module systems differ (CommonJS, ESM, Go modules)
- Embedding accuracy may vary

**Research Plan:**
1. Implement import parsers for each language
2. Test on real projects
3. Measure accuracy vs Python baseline

**Expected Results:**

| Language | Import Parse | Embedding | Overall | Support? |
|----------|-------------|-----------|---------|----------|
| Python | 90% | 85% | 88% | ✅ Excellent |
| JavaScript | 80% | 85% | 83% | ✅ Good |
| TypeScript | 85% | 85% | 85% | ✅ Good |
| Go | 75% | 80% | 78% | ⚠️ Acceptable |
| Rust | 70% | 80% | 75% | ⚠️ Acceptable |
| Java | 65% | 80% | 73% | ⚠️ Basic |

**Success Criteria:**
- [ ] Python: >85% accuracy (primary focus)
- [ ] JS/TS: >80% accuracy (important)
- [ ] Others: >70% accuracy (nice to have)

**Findings:** (To be filled during research)

---

### 8. Library Skill Quality

**Question:** How good are auto-generated library skills vs handcrafted?

**Experiment:**
1. Generate library skills for popular frameworks:
   - FastAPI (from docs)
   - React (from docs)
   - PostgreSQL (from docs)
2. Compare to handcrafted skills (manually written)
3. Measure: completeness, accuracy, usefulness

**Evaluation Criteria:**
- **Completeness:** Does it cover all key APIs?
- **Accuracy:** Is information correct?
- **Usefulness:** Do developers find it helpful?
- **Freshness:** Is it up-to-date?

**Test Plan:**
```python
# For each framework:
#   1. Auto-generate skill
#   2. Handcraft skill (1 hour of work)
#   3. A/B test with 5 developers
#   4. Measure: time to complete task, satisfaction

frameworks = ["FastAPI", "React", "PostgreSQL"]

for framework in frameworks:
    auto_skill = generate_skill(framework)
    hand_skill = handcraft_skill(framework)

    results = ab_test(auto_skill, hand_skill, n_users=5)

    print(f"{framework}:")
    print(f"  Auto: {results.auto_score}/10")
    print(f"  Hand: {results.hand_score}/10")
```

**Expected Results:**

| Framework | Auto | Hand | Difference | Acceptable? |
|-----------|------|------|------------|-------------|
| FastAPI | 7/10 | 9/10 | -2 | ✅ Close enough |
| React | 6/10 | 9/10 | -3 | ⚠️ Needs work |
| PostgreSQL | 5/10 | 9/10 | -4 | ❌ Too far |

**Optimization:**
- If auto-generated is <7/10, use handcrafted
- Offer both: curated (handcrafted) + auto-generated
- Community contributions for popular frameworks

**Success Criteria:**
- [ ] Auto-generated is >7/10 quality
- [ ] Users find library skills helpful
- [ ] Skills stay up-to-date (auto-regenerate)

**Findings:** (To be filled during research)

---

### 9. Skill Update Frequency

**Question:** How often do skills need updating?

**Variables:**
- Codebase churn rate (commits/day)
- Trigger: every commit vs every merge vs weekly
- Impact: staleness vs performance

**Experiment:**
```python
# Track a real project for 1 month
# Measure:
#   - How often code changes affect skills
#   - How stale skills get if not updated
#   - User tolerance for staleness

project = "skill-seekers"
duration = "30 days"

events = track_changes(project, duration)

print(f"Total commits: {events.commits}")
print(f"Skill-affecting changes: {events.skill_changes}")
print(f"Ratio: {events.skill_changes / events.commits}")

# Test different update frequencies
frequencies = ["every-commit", "every-merge", "daily", "weekly"]

for freq in frequencies:
    staleness = measure_staleness(freq)
    perf_cost = measure_performance_cost(freq)

    print(f"{freq}: Staleness={staleness}, Cost={perf_cost}")
```

**Expected Results:**

| Frequency | Staleness | Perf Cost | CPU Usage | Acceptable? |
|-----------|-----------|-----------|-----------|-------------|
| Every commit | 0% | High | 50%+ | ❌ Too much |
| Every merge | 5% | Medium | 10% | ✅ Good |
| Daily | 15% | Low | 2% | ✅ Good |
| Weekly | 40% | Very low | <1% | ⚠️ Too stale |

**Recommendation:** Update on merge to watched branches (main, dev)

**Success Criteria:**
- [ ] Skills <10% stale
- [ ] Performance overhead <10% CPU
- [ ] User doesn't notice staleness

**Findings:** (To be filled during research)

---

### 10. Plugin Integration Patterns

**Question:** What's the best way to integrate with Claude Code?

**Options:**
1. **File Hooks:** React to file open/save events
2. **Command Palette:** User manually loads skills
3. **Automatic:** Always load best skills
4. **Hybrid:** Auto-load + manual override

**User Experience Testing:**
```python
# Test with 5 developers for 1 week each

patterns = [
    "file_hooks",      # Auto-load on file open
    "command_palette", # Manual: Cmd+Shift+P -> "Load Skills"
    "automatic",       # Always load, no user action
    "hybrid",          # Auto + manual override
]

for pattern in patterns:
    feedback = test_with_users(pattern, n_users=5, days=7)

    print(f"{pattern}:")
    print(f"  Ease of use: {feedback.ease}/10")
    print(f"  Control: {feedback.control}/10")
    print(f"  Satisfaction: {feedback.satisfaction}/10")
```

**Expected Results:**

| Pattern | Ease | Control | Satisfaction | Winner? |
|---------|------|---------|--------------|---------|
| File Hooks | 9/10 | 7/10 | 8/10 | ✅ Automatic |
| Command Palette | 6/10 | 10/10 | 7/10 | Power users |
| Automatic | 10/10 | 5/10 | 7/10 | Too magic |
| Hybrid | 9/10 | 9/10 | 9/10 | ✅✅ Best |

**Recommendation:** Hybrid approach
- Auto-load on file open (convenience)
- Show notification (transparency)
- Allow manual override (control)

**Success Criteria:**
- [ ] Users don't think about it (automatic)
- [ ] Users can control it (override)
- [ ] Users trust it (transparent)

**Findings:** (To be filled during research)

---

## 🧪 Experimental Ideas

### Idea 1: Conversation-Aware Clustering

**Concept:** Use chat history to improve skill clustering

**Algorithm:**
```python
def find_relevant_skills_with_context(
    current_file: Path,
    conversation_history: list[str]
) -> list[Path]:
    # Extract topics from recent messages
    topics = extract_topics(conversation_history[-10:])
    # Examples: "authentication", "database", "API endpoints"

    # Find skills matching these topics
    topic_skills = find_skills_by_topic(topics)

    # Combine with file-based clustering
    file_skills = find_relevant_skills(current_file)

    # Merge with weighted ranking
    return merge(topic_skills, file_skills, weights=[0.3, 0.7])
```

**Example:**
```
User: "How do I add authentication to the API?"
Claude: [loads auth.skill, api.skill]

User: "Now show me the database models"
Claude: [keeps auth.skill (context), adds models.skill]

User: "How do I test this?"
Claude: [adds tests.skill, keeps auth.skill, models.skill]
```

**Potential:** High (conversation context is valuable)
**Complexity:** Medium (need to parse conversation)
**Risk:** Low (can fail gracefully)

---

### Idea 2: Feedback Loop Learning

**Concept:** Learn from user corrections to improve clustering

**Algorithm:**
```python
class FeedbackLearner:
    def __init__(self):
        self.history = []  # (file, loaded_skills, user_feedback)

    def record_feedback(self, file: Path, loaded: list, feedback: str):
        """
        feedback: "skill X was not helpful" or "missing skill Y"
        """
        self.history.append({
            "file": file,
            "loaded": loaded,
            "feedback": feedback,
            "timestamp": now()
        })

    def adjust_weights(self):
        """
        Learn from feedback to adjust clustering weights
        """
        # If skill X frequently marked "not helpful" for files in dir Y:
        #   → Reduce X's weight for Y

        # If skill Y frequently requested for files in dir Z:
        #   → Increase Y's weight for Z

        # Update clustering engine weights
        self.clustering_engine.update_weights(learned_weights)
```

**Potential:** Very High (personalized to user)
**Complexity:** High (ML/learning system)
**Risk:** Medium (could learn wrong patterns)

---

### Idea 3: Multi-File Context

**Concept:** Load skills for all open files, not just current

**Algorithm:**
```python
def find_relevant_skills_multi_file(
    open_files: list[Path]
) -> list[Path]:
    all_skills = set()

    for file in open_files:
        skills = find_relevant_skills(file)
        all_skills.update(skills)

    # Rank by frequency across files
    ranked = rank_by_frequency(all_skills)

    return ranked[:10]  # Top 10 (more files = more skills needed)
```

**Example:**
```
Open tabs:
  - src/api/users.py
  - src/models/user.py
  - src/auth/jwt.py

Loaded skills:
  - api.skill (from users.py)
  - models.skill (from user.py)
  - auth.skill (from jwt.py)
  - fastapi.skill (common across all)
```

**Potential:** High (developers work on multiple files)
**Complexity:** Low (just aggregate)
**Risk:** Low (might load too many skills)

---

### Idea 4: Skill Versioning

**Concept:** Track skill changes over time, allow rollback

**Implementation:**
```
.skill-seekers/skills/
├── codebase/
│   └── api.skill
│
└── versions/
    └── api/
        ├── api.skill.2026-01-20-v1
        ├── api.skill.2026-01-19-v1
        └── api.skill.2026-01-15-v1
```

**Commands:**
```bash
# View skill history
skill-seekers skill-history api.skill

# Diff versions
skill-seekers skill-diff api.skill --from 2026-01-15 --to 2026-01-20

# Rollback
skill-seekers skill-rollback api.skill --to 2026-01-19
```

**Potential:** Medium (useful for debugging)
**Complexity:** Low (just file copies)
**Risk:** Low (storage cost)

---

### Idea 5: Skill Analytics

**Concept:** Track which skills are most useful

**Metrics:**
- Load frequency (how often loaded)
- Dwell time (how long in context)
- User rating (thumbs up/down)
- Task completion (helped solve problem?)

**Dashboard:**
```
Skill Analytics
===============

Most Loaded:
  1. api.skill (45 times)
  2. models.skill (38 times)
  3. fastapi.skill (32 times)

Most Helpful (by rating):
  1. api.skill (4.8/5.0)
  2. auth.skill (4.5/5.0)
  3. tests.skill (4.2/5.0)

Least Helpful:
  1. deprecated.skill (2.1/5.0) ← Maybe remove?
```

**Potential:** Medium (helps improve system)
**Complexity:** Medium (tracking infrastructure)
**Risk:** Low (privacy concerns if shared)

---

## 📊 Research Checklist

### Phase 0: Before Implementation
- [ ] Import analysis accuracy (Research #1)
- [ ] Embedding model selection (Research #2)
- [ ] Skill granularity (Research #3)
- [ ] Git hook performance (Research #5)

### Phase 1-3: During Implementation
- [ ] Clustering strategy (Research #4)
- [ ] Multi-language support (Research #7)
- [ ] Skill update frequency (Research #9)

### Phase 4-5: Advanced Features
- [ ] Context window management (Research #6)
- [ ] Library skill quality (Research #8)
- [ ] Plugin integration (Research #10)

### Experimental (Optional)
- [ ] Conversation-aware clustering
- [ ] Feedback loop learning
- [ ] Multi-file context
- [ ] Skill versioning
- [ ] Skill analytics

---

## 🎯 Success Metrics

### Technical Metrics
- Import parse accuracy: >85%
- Embedding similarity: >75%
- Clustering F1-score: >85%
- Regeneration time: <5 min
- Context usage: <150K tokens

### User Metrics
- Satisfaction: >8/10
- Ease of use: >8/10
- Trust: >8/10
- Would recommend: >80%

### Business Metrics
- GitHub stars: >1000
- Active users: >100
- Community contributions: >10
- Issue response time: <24 hours

---

**Version:** 1.0
**Status:** Research Phase
**Next:** Conduct experiments, fill in findings