continuous-learning

Continuous Learning

Overview

Learn from sessions in two complementary ways:

1. Instinct-based learning — extract reusable patterns, score confidence, promote high-confidence instincts into skills.
2. Workspace memory — delegate durable preferences and facts to the agents-memory-updater subagent (orchestration only in this skill).

When to Use

• After completing a significant feature or fix (instincts)
• When a session revealed a non-obvious solution worth remembering
• Before starting similar work to apply learned instincts
• When transcript deltas may update AGENTS.md (workspace memory)

Instinct Lifecycle

1. Discovery

When a pattern is encountered during work:

Pattern: [What was discovered]
Context: [When this applies]
Confidence: [0.0-1.0 based on evidence]
Evidence: [What proves this works]

2. Validation

Before storing an instinct:

• Pattern has been verified in at least one successful outcome
• Context is specific enough to avoid false positives
• Confidence score is justified with evidence
• No conflicting instincts exist

3. Storage

Instincts are stored with:

instinct:
  name: "descriptive-name"
  pattern: "what to do"
  context: "when to apply"
  confidence: 0.85
  evidence: "why this works"
  created: "2026-01-15"
  usage_count: 3
  success_rate: 0.95

4. Evolution

When an instinct reaches thresholds:

• Confidence > 0.9 AND usage_count > 5 AND success_rate > 0.9 → Promote to skill
• Confidence < 0.5 AND usage_count > 3 → Deprecate
• Confidence unchanged after 30 days → Review

Confidence Scoring

Score	Meaning	Action
0.9-1.0	Proven pattern, multiple successes	Promote to skill candidate
0.7-0.9	Strong pattern, some evidence	Use with confidence
0.5-0.7	Plausible pattern, limited evidence	Use cautiously, verify
0.3-0.5	Weak pattern, speculative	Note but don't rely on
0.0-0.3	Unproven, likely incorrect	Discard

Import and Export

Export Instincts

Export all instincts to JSON: [{name, pattern, context, confidence, evidence}]

Import Instincts

Import instincts from JSON. Merge with existing records and update confidence on duplicates.

Workspace memory (AGENTS.md)

When transcript mining may produce durable updates—not one-off task context:

1. Call agents-memory-updater; return its result unchanged.
2. Do not mine transcripts or edit files in the parent flow.

The updater owns AGENTS.md sections (## Learned User Preferences, ## Learned Workspace Facts), incremental transcript indexes under ~/.cursor/projects/<workspace-slug>/agent-transcripts/, and deduplication (max 12 bullets per learned section).

If no meaningful updates: respond exactly No high-signal memory updates.

Anti-Rationalization Table

Excuse	Counter
"I'll remember this pattern"	Human memory is unreliable. Document it now with context and evidence.
"This is too specific to be useful"	Specific patterns become general skills through evolution. Start specific, generalize later.
"I don't have time to document"	Two minutes now saves hours of rediscovery later. Use the instinct template.
"The confidence score is subjective"	Confidence is a starting point. Usage and success rates provide objective data over time.