DAG Skills Matcher

Matches tasks to skills, ranks candidates, and maintains the skill catalog.

Decision Points

When to Retry vs. Escalate

task_description → extract_intent()
├─ fit_score ≥ 0.5 for top candidate?
│  ├─ YES → check NOT clauses
│  │  ├─ passes → assign skill
│  │  └─ fails → try next candidate
│  └─ NO → retry with broader search terms
│     └─ still no fit_score ≥ 0.5?
│        └─ escalate to skill-architect (new skill needed)

Search Strategy Selection

task_complexity = count(technical_terms, domain_words, constraints)
├─ complexity ≤ 3 → keyword_search_only
├─ 4-8 → keyword + semantic_similarity
└─ >8 → full_pipeline (keyword + semantic + domain_tags + thompson)

Ranking Threshold Decision

candidate_count → ranking_strategy
├─ 1-2 candidates → simple fit_score ranking
├─ 3-5 candidates → weighted: fit(0.5) + elo(0.3) + cost(0.2)
└─ >5 candidates → full scoring with thompson sampling

Cost vs. Quality Trade-off

task_priority + budget_constraints → selection_criteria
├─ high_priority + unlimited_budget → maximize fit_score + elo
├─ medium_priority → balanced scoring (default weights)
└─ low_priority + cost_sensitive → weight cost(0.5) + fit(0.3) + elo(0.2)

Failure Modes

Schema Drift

Symptoms: fit_scores consistently < 0.3, many "skill not found" escalations Detection: If >20% of searches in 24h escalate to skill-architect Fix: Update skill descriptions with recent task language patterns

Thompson Exploitation Lock-in

Symptoms: Same 2-3 skills always selected, no skill performance comparison data Detection: If top skill selection rate > 80% for any domain over 100 tasks Fix: Increase Thompson sampling beta parameter by 10%, force exploration

NOT-Clause Bypass

Symptoms: Skills assigned to incompatible tasks, high downstream failure rates Detection: If downstream acceptance_rate < 0.7 for any skill Fix: Strengthen NOT-clause checking, add regex patterns for exclusion terms

Semantic Similarity False Positives

Symptoms: Skills matched on superficial word similarity, not actual capability Detection: If fit_score > 0.7 but downstream acceptance_rate < 0.5 Fix: Add domain-specific negative embeddings, weight keyword matching higher

Cost Optimization Trap

Symptoms: Always selecting cheapest skills, degrading output quality Detection: If avg_cost_per_use drops >30% while acceptance_rate drops >15% Fix: Set minimum fit_score threshold (0.5), reject candidates below threshold regardless of cost

Worked Examples

Example 1: Python ML Classification Task

Task: "Build a scikit-learn classifier for customer churn prediction with hyperparameter tuning"

Process:

1. Extract intent: ML classification + scikit-learn + hyperparameter tuning
2. Search yields: sklearn-tuner (fit=0.85, elo=1750), automl-skill (fit=0.72, elo=1820), python-ml-basic (fit=0.65, elo=1680)
3. Apply decision tree: >5 candidates → full scoring
4. Scores: sklearn-tuner: 0.85×0.4 + 0.72×0.3 + 0.8×0.3 = 0.796, automl-skill: 0.766
5. Check NOT clauses: sklearn-tuner NOT clause says "not for deep learning" ✓ (this is classical ML)
6. Decision: Assign sklearn-tuner

Novice miss: Would pick automl-skill due to higher Elo, missing that sklearn-tuner is more specifically matched Expert catch: Recognizes hyperparameter tuning keyword strongly favors sklearn-tuner despite slightly lower Elo

Example 2: Ambiguous Code Review Request

Task: "Review this code for issues"

Process:

1. Extract intent: code review (but no language specified)
2. fit_score for all code-review skills < 0.5 (too vague)
3. Apply decision tree: fit_score < 0.5 → retry with broader search
4. Broader search finds: code-review-general (fit=0.55)
5. Still marginal fit → escalate to skill-architect
6. Decision: Create task-specific code review skill

Expert insight: Recognizes that vague tasks need either clarification or new specialized skills

Quality Gates

• Top candidate has fit_score ≥ 0.5
• Selected skill's NOT clauses don't match task requirements
• Composite score > 0.6 OR task explicitly escalated to skill-architect
• Search found at least 1 candidate (not empty result set)
• If multiple candidates with score difference < 0.1, thompson sampling was applied
• Cost per use is within budget constraints (if specified)
• Selected skill's domain tags overlap with extracted task domain
• Downstream acceptance rate for selected skill > 0.6 (historical data)
• Search took < 2 seconds (performance gate)
• Match reasoning is logged for skill improvement feedback

NOT-FOR Boundaries

This skill should NOT be used for:

• Creating new skills → use skill-architect instead
• Grading skill quality or performance → use skill-grader instead
• Actually executing the matched skill → use dag-runtime instead
• Modifying skill parameters or configs → use skill-configurator instead
• Handling skill versioning or deployment → use skill-lifecycle-manager instead

Domain boundaries:

• For skill marketplace operations → use skill-marketplace instead
• For skill analytics and reporting → use skill-analytics instead
• For cross-agent skill sharing → use skill-federation instead