Knowledge Consolidation Framework

When This Activates

This skill activates when:

• User wants to remember something for future sessions

• A significant decision or pattern emerges

• A bug fix reveals a gotcha

• Session learnings should be captured

The RETRIEVE→JUDGE→DISTILL→CONSOLIDATE Cycle

1. RETRIEVE

Find similar past situations from corrections/decisions:

memory_sessions category=decision "authentication pattern" reasoning_query context="login flow architecture"

2. JUDGE

Assess if past solution applies to current context:

• Domain match? (same tech area)

• Context similarity > 30%?

• Confidence threshold met?

3. DISTILL

Extract generalizable patterns from specific instances:

• Find common trigger terms across examples

• Identify shared solution approaches

• Require 2+ examples before creating pattern

4. CONSOLIDATE

Update long-term memory with new patterns:

• Merge similar trajectories

• Update confidence scores

• Prune outdated patterns

Observation Categories

When capturing learnings, categorize them:

Category When to Use Example

decision

Technical choices made "Chose native Ollama over Docker for Metal GPU"

pattern

Patterns discovered/applied "Use host.docker.internal for Docker→host"

bugfix

Bugs found and fixed "Fixed Firebase error by mounting key"

gotcha

Tricky/unexpected things "Docker can't use Metal GPU on Mac"

feature

Features implemented "Added doc_query tool to gateway"

implementation

How something was built "Integrated AnythingLLM via REST"

Recording Workflow

When the user says "remember this" or similar:

1. Identify the Learning Type

"Remember: Docker containers can't use Metal GPU" → Category: gotcha → Domain: docker

2. Structure the Observation

{ "category": "gotcha", "observation": "Docker containers on macOS cannot use Metal GPU - must use native services", "context": "Trying to run Ollama in Docker for GPU acceleration", "files": [], "project_id": "claude-dash" }

3. Check for Related Past Learnings

reasoning_query context="Docker GPU Metal macOS"

4. Confirm and Store

"Recorded as a gotcha. This will surface next time Docker GPU topics come up."

Pattern Extraction

After multiple similar observations, patterns emerge:

Observations:

1. "Docker can't use Metal GPU"
2. "Ollama must run native for GPU"
3. "MLX requires native execution"

Distilled Pattern: { "id": "docker_metal_gpu", "domain": "docker", "trigger_terms": ["docker", "metal", "gpu", "macos"], "solution_terms": ["native", "host", "not container"], "description": "When [docker, metal, gpu], try [native, host execution]", "confidence": 0.8 }

Proactive Injection

The system auto-injects relevant memories via:

• <semantic-memory> tags on prompt submission

• <past-corrections> when similar topics arise

• Pattern matching on trigger terms

Manual Queries

Check what's been learned:

memory_sessions category=decision limit=5 memory_sessions category=gotcha query="docker" reasoning_query context="current problem description"

Consolidation Triggers

Consolidation runs:

• After 10+ new trajectories

• When explicitly requested

• During background worker runs

Output: Merged patterns, updated confidence scores, pruned stale entries.