Agent skill

cross-task-learning

Pattern for aggregating insights across multiple tasks to enable data-driven evolution.

Stars 232
Forks 15

Install this agent skill to your Project

npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/clouder0/cross-task-learning

SKILL.md

Cross-Task Learning Skill

Pattern for maintaining aggregated insights across all completed tasks.

When to Load This Skill

  • Reflector: After writing individual reflection
  • Evolver: Before analyzing reflections (to get aggregated view)

Core Concept

Individual reflections capture task-specific learnings. Cross-task learning aggregates these to identify:

  • Patterns that keep appearing → Skill candidates
  • Strategies that consistently work → Best practices
  • Strategies that keep failing → Anti-patterns
  • Bottlenecks that recur → System weaknesses
  • Proposals that keep emerging → Priority improvements

Aggregate File

Location: memory/reflections/_aggregate.json

Example structure (compact JSON):

json
{"last_updated":"ISO-8601","tasks_analyzed":15,"strategy_effectiveness":[{"strategy":"Spawn parallel explorers for context","uses":12,"successes":10,"effectiveness_score":0.83,"notes":"Works well for unfamiliar codebases"}],"failure_patterns":[{"pattern":"Contract conflicts in parallel implementation","occurrences":4,"severity":"high","status":"active"}],"skill_candidates":[{"pattern":"Read → Explore → Implement → Test → Verify","frequency":8,"effectiveness":"high","proposed_skill_name":"implementation-cycle"}]}

Update Protocol (for Reflector)

After writing individual reflection, update aggregate:

1. Read current _aggregate.json
2. Read the reflection just written

3. Update task_history:
   - Add new entry with task_id, timestamp, outcome
   - Keep last 20 entries (trim oldest)

4. Update strategy_effectiveness:
   FOR each strategy in reflection.patterns.effective_strategies:
     IF strategy exists in aggregate:
       → Increment uses and successes
       → Recalculate effectiveness_score
     ELSE:
       → Add new entry with uses=1, successes=1

   FOR each strategy in reflection.patterns.ineffective_strategies:
     IF strategy exists in aggregate:
       → Increment uses and failures
       → Recalculate effectiveness_score
     ELSE:
       → Add new entry with uses=1, failures=1

5. Update failure_patterns:
   FOR each issue in reflection.process_analysis.phases[].issues:
     IF similar pattern exists (fuzzy match):
       → Increment occurrences
       → Update last_seen
     ELSE:
       → Add new pattern

6. Update bottleneck_hotspots:
   FOR each bottleneck in reflection.process_analysis.bottlenecks:
     IF location exists:
       → Increment frequency
       → Add cause if new
     ELSE:
       → Add new hotspot

7. Update skill_candidates:
   FOR each sequence in reflection.patterns.repeated_sequences:
     IF sequence.skill_candidate == true:
       IF similar pattern exists:
         → Increment frequency
       ELSE:
         → Add new candidate

8. Update recurring_discoveries:
   FOR each finding in reflection.knowledge_discovered:
     IF similar finding exists:
       → Increment discovery_count
       → Set should_be_documented = true if count >= 3
     ELSE:
       → Add new entry

9. Update recurring_proposals:
   FOR each proposal in reflection.evolution_proposals:
     IF similar proposal exists:
       → Increment occurrence_count
     ELSE:
       → Add new entry

10. Update retry_analysis:
    FOR each retry in reflection.process_analysis.retries:
      → Increment total_retries
      → Update by_strategy counts

11. Increment tasks_analyzed
12. Update last_updated
13. Write updated _aggregate.json (compact JSON)

Similarity Matching

When checking if patterns are "similar":

Normalize both strings:
  - Lowercase
  - Remove punctuation
  - Remove common words (the, a, an, is, are)

Compare using:
  - Exact match after normalization
  - OR: >70% word overlap
  - OR: Same key terms present

Thresholds for Action

Metric Threshold Action
Strategy effectiveness < 0.3 After 5 uses Flag as anti-pattern
Strategy effectiveness > 0.8 After 5 uses Flag as best practice
Failure pattern occurrences >= 3 Flag for urgent fix
Skill candidate frequency >= 5 Propose as new skill
Recurring discovery count >= 3 Add to knowledge base
Recurring proposal count >= 3 Prioritize for evolution

Query Patterns (for Evolver)

Get top issues to fix:

failure_patterns
  WHERE status == "active"
  ORDER BY occurrences * severity_weight DESC
  LIMIT 5

Get best practices to document:

strategy_effectiveness
  WHERE effectiveness_score > 0.8
  AND uses >= 5

Get skill candidates ready for implementation:

skill_candidates
  WHERE frequency >= 5
  AND effectiveness == "high"
  AND status == "candidate"

Get knowledge gaps:

recurring_discoveries
  WHERE should_be_documented == true
  AND NOT in knowledge_base

Integration with Evolver

The evolver should:

  1. Read _aggregate.json FIRST (not individual reflections)
  2. Use aggregated data for proposal prioritization:
    • High-occurrence failure patterns → High priority
    • High-frequency skill candidates → Medium priority
    • Recurring proposals → Already validated ideas
  3. Reference individual reflections only for details
  4. Update recurring_proposals[].status after evolution

Principles

  1. Aggregate, don't duplicate - Summary stats, not copies
  2. Track trends - First seen, last seen, frequency
  3. Enable queries - Structure for easy filtering
  4. Threshold-based actions - Clear criteria for when to act
  5. Fuzzy matching - Similar patterns should merge, not duplicate

Expand your agent's capabilities with these related and highly-rated skills.

aiskillstore/marketplace

perigon-backend

Perigon ASP.NET Core + EF Core + Aspire conventions

232 15
Explore
aiskillstore/marketplace

perigon-agent

Pointers for Copilot/agents to apply Perigon conventions

232 15
Explore
aiskillstore/marketplace

perigon-angular

Angular 21+ standalone/Material/signal conventions for Perigon WebApp

232 15
Explore
aiskillstore/marketplace

fastapi-mastery

Comprehensive FastAPI development skill covering REST API creation, routing, request/response handling, validation, authentication, database integration, middleware, and deployment. Use when working with FastAPI projects, building APIs, implementing CRUD operations, setting up authentication/authorization, integrating databases (SQL/NoSQL), adding middleware, handling WebSockets, or deploying FastAPI applications. Triggered by requests involving .py files with FastAPI code, API endpoint creation, Pydantic models, or FastAPI-specific features.

232 15
Explore
aiskillstore/marketplace

context7-efficient

Token-efficient library documentation fetcher using Context7 MCP with 86.8% token savings through intelligent shell pipeline filtering. Fetches code examples, API references, and best practices for JavaScript, Python, Go, Rust, and other libraries. Use when users ask about library documentation, need code examples, want API usage patterns, are learning a new framework, need syntax reference, or troubleshooting with library-specific information. Triggers include questions like "Show me React hooks", "How do I use Prisma", "What's the Next.js routing syntax", or any request for library/framework documentation.

232 15
Explore
aiskillstore/marketplace

browser-use

Browser automation using Playwright MCP. Navigate websites, fill forms, click elements, take screenshots, and extract data. Use when tasks require web browsing, form submission, web scraping, UI testing, or any browser interaction.

232 15
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results