Continuous Learning Skill

Extract reusable knowledge from work sessions and save it as memories via Serena MCP.

Memory Categories

Learnings (learning_<topic>_<specific>)

Knowledge discovered through debugging, investigation, or problem-solving that wasn't obvious beforehand.

Extract when:

• •Solution required significant investigation (not a documentation lookup)
• •Error message was misleading — root cause was non-obvious
• •Discovered a workaround for a tool/framework limitation
• •Found a workflow optimization through experimentation

Examples: learning_swiftui_task_cancellation, learning_xcode_build_cache_corruption, learning_combine_nested_sink_retain_cycle

Decisions (decision_<domain>_<topic>)

Deliberate choices about how the project should work.

Extract when:

• •Architectural choice made (patterns, structures, dependencies)
• •Convention or style preference established
• •Tool/library selected over alternatives with reasoning
• •User says "let's use X", "I prefer Y", "from now on..."
• •Trade-off resolved between competing concerns

Domain prefixes:

Extraction Workflow

Step 1: Evaluate the Current Task

After completing any task, ask:

• •Did this require non-obvious investigation or debugging?
• •Was a choice made about architecture, patterns, or approach?
• •Did the user express a preference or convention?
• •Would future sessions benefit from having this documented?

If NO to all → skip. If YES to any → continue.

Step 2: Search Existing Knowledge

Always search docs-mcp-server first (semantic search across documentation and Serena memories):

mcp__docs-mcp-server__search_docs(library: "<project>", query: "<topic>")

Fall back to Serena if search_docs returns no results:

mcp__serena__list_memories()

Determine if: update an existing memory, cross-reference related memories, or knowledge is already captured.

Step 3: Research (When Appropriate)

For Apple/iOS/Swift topics — use Sosumi first:

mcp__sosumi__searchAppleDocumentation(query: "<topic>")

For general topics — use OmniSearch:

mcp__mcp-omnisearch__ai_search(query: "<topic> best practices 2026", provider: "perplexity")

Skip research for: project-specific conventions, personal preferences, time-sensitive captures.

Step 4: Structure and Save

Read references/templates.md for the full template structures.

For learnings: Use the Learning Memory Template (Problem → Trigger Conditions → Solution → Verification → Example → Notes → References).

For decisions: Use the ADR-Inspired Template for complex trade-offs, or the Simplified Template for straightforward preferences.

Save:

mcp__serena__write_memory(name: "<category>_<topic>_<specific>", content: "<structured markdown>")

Update existing:

mcp__serena__edit_memory(name: "<existing_name>", content: "<updated markdown>")

Quality Gates

Before saving any memory, verify:

• • Name follows the correct pattern (learning_ or decision_<domain>_)
• • Content uses the appropriate template from references/templates.md
• • Solution is verified to work (not theoretical)
• • Content is specific enough to be actionable
• • Content is general enough to be reusable
• • No sensitive information (credentials, internal URLs)
• • Does not duplicate existing memories
• • References included if external sources were consulted

Retrospective Mode

When /retrospective is invoked:

1. •Review conversation history for extractable knowledge
2. •Search existing memories via search_docs then list_memories
3. •List candidates with brief justifications
4. •Extract top 1-3 highest-value memories
5. •Report what was created and why

Tool Reference