Continuous Learning Skill

Overview

The Continuous Learning Skill enables agents to autonomously extract reusable patterns from debugging discoveries. Rather than losing hard-won knowledge at session end, this skill captures high-value insights as structured documents that inform future work.

Research Foundation

This implementation draws from established agent learning research:

• Voyager (Wang et al., 2023): Open-ended skill library discovery
• CASCADE (2024): Meta-skills for compound learning
• Reflexion (Shinn et al., 2023): Verbal reinforcement learning
• SEAgent (2025): Trial-and-error in software environments

Problem Addressed

Agents routinely discover non-obvious solutions through debugging, but this knowledge is lost when:

• Context windows compact or clear
• Sessions end without explicit knowledge capture
• Similar problems are re-investigated from scratch

The Continuous Learning Skill transforms ephemeral discoveries into persistent, retrievable knowledge.

Activation Triggers

The skill activates when ANY of these conditions are detected:

Trigger 1: Non-Obvious Solution Discovery

Agent completed debugging where the solution wasn't immediately apparent from the error message or documentation.

Signals:

• Multiple investigation steps before resolution
• Solution differs from first hypothesis
• Required reading source code or experimentation

Trigger 2: Workaround Through Investigation

Agent found a workaround through trial-and-error or systematic investigation rather than known solution.

Signals:

• Tested multiple approaches before success
• Solution involved undocumented behavior
• Required combining information from multiple sources

Trigger 3: Non-Apparent Root Cause

Agent resolved an error where the root cause wasn't clear from initial symptoms.

Signals:

• Error message was misleading or generic
• Actual cause was upstream of reported location
• Required tracing through multiple layers

Trigger 4: Project-Specific Patterns

Agent learned patterns specific to this codebase through experimentation.

Signals:

• Pattern doesn't exist in general documentation
• Specific to this project's architecture or conventions
• Would be valuable for future agents in this codebase

Integration with Loa Architecture

Three-Zone Model Compliance

.claude/

grimoires/loa/

src/

CRITICAL: Extracted skills MUST write to State Zone only:

• Pending:

  grimoires/loa/skills-pending/{skill-name}/SKILL.md

• Active:

  grimoires/loa/skills/

• Archived:

  grimoires/loa/skills-archived/

NOTES.md Integration

Cross-reference extracted skills with NOTES.md to prevent duplicates:

1. Before extraction, check

   ## Learnings

   section
2. If similar pattern exists, UPDATE rather than create new skill
3. Add reference to NOTES.md:

   ## Learnings

   entry pointing to skill

NOTES.md Entry Format:

## Learnings
- [NATS JetStream] Use durable consumers for persistent state → See `skills/nats-jetstream-consumer-durable`

Agent Tagging

Each extracted skill must include the extracting agent:

loa-agent: implementing-tasks  # or reviewing-code, auditing-security, etc.

This enables filtering skills by agent context for more relevant retrieval.

Quality Gates

All four gates must PASS before skill extraction proceeds. See

.claude/protocols/continuous-learning.md

Gate 1: Discovery Depth

Question: Did the agent actually discover something through investigation?

Gate 2: Reusability

Question: Will this help future sessions with similar problems?

Gate 3: Trigger Clarity

Question: Can the skill be reliably retrieved when needed?

Gate 4: Verification

Question: Is the solution proven to work?

Workflow

Automatic Mode (During Implementation)

During

/implement

/review-sprint

/audit-sprint

/deploy-production

/ride

1. Monitor: Watch for activation triggers during work
2. Flag: When trigger detected, note in trajectory log
3. Queue: Add to extraction queue (don't interrupt flow)
4. Prompt: At natural break, evaluate quality gates
5. Extract: If all gates pass, write to

   skills-pending/

Manual Mode (/retrospective Command)

At session end or milestone:

1. Review: Scan conversation for discovery signals
2. Candidates: Present potential extractions with gate assessment
3. Approve: User selects which to extract
4. Write: Extract approved skills to

   skills-pending/

Skill Format

Use the template at

resources/skill-template.md

Required Sections

1. YAML Frontmatter: Metadata for retrieval
2. Problem: Clear statement of the issue
3. Trigger Conditions: When to apply this skill
4. Root Cause: Why the problem occurs
5. Solution: Step-by-step resolution
6. Verification: How to confirm success
7. Anti-Patterns: Common mistakes to avoid
8. Related Memory: NOTES.md cross-references

Example

See

resources/examples/nats-jetstream-consumer-durable.md

Phase Gating

/implement sprint-N

/review-sprint sprint-N

/audit-sprint sprint-N

/deploy-production

/ride

/plan-and-analyze

/architect

/sprint-plan

Skill Lifecycle

[Discovery] → [Extraction] → [Pending] → [Active] → [Archived]
                                 ↓
                            [Rejected]

States

grimoires/loa/skills-pending/

grimoires/loa/skills/

grimoires/loa/skills-archived/

Transitions

• Pending → Active:

  /skill-audit --approve {skill-name}

• Pending → Archived:

  /skill-audit --reject {skill-name}

• Active → Archived:

  /skill-audit --prune

  (age + no matches)

Configuration

In

.loa.config.yaml

continuous_learning:
  enabled: true                    # Master toggle
  auto_extract: false              # Require user confirmation (recommended)
  quality_gate_threshold: 4        # All 4 gates must pass
  prune_after_days: 90             # Archive unused skills after N days
  min_match_count: 0               # Minimum retrievals to avoid pruning
  trajectory_logging: true         # Log extraction events

Integration with Trajectory Evaluation

All skill extraction events are logged to trajectory:

Location:

grimoires/loa/a2a/trajectory/continuous-learning-{date}.jsonl

Event Types:

• extraction

  : Skill extracted to pending

• approval

  : Skill approved to active

• rejection

  : Skill rejected to archived

• prune

  : Skill pruned due to age/non-use

• match

  : Skill retrieved for a problem

Example Entry:

{
  "timestamp": "2026-01-18T10:30:00Z",
  "event": "extraction",
  "skill": "nats-jetstream-consumer-durable",
  "agent": "implementing-tasks",
  "gates": {"depth": true, "reusability": true, "trigger": true, "verification": true},
  "source": "sprint-7-task-3"
}

Upstream Learning Flow (v1.16.0+)

High-value project learnings can be proposed for upstream contribution to the Loa framework. This enables proven patterns to benefit all Loa users.

Workflow Overview

┌──────────────────────────────────────────────────────────────────┐
│              Upstream Learning Flow                               │
├──────────────────────────────────────────────────────────────────┤
│                                                                   │
│  1. Learning Discovery (existing /retrospective workflow)         │
│     └─→ Extract learnings to grimoires/loa/a2a/compound/         │
│                                                                   │
│  2. Effectiveness Tracking                                        │
│     └─→ Track applications and success rates over time           │
│                                                                   │
│  3. Upstream Detection (automatic)                                │
│     └─→ post-retrospective-hook.sh evaluates eligibility         │
│     └─→ Silent unless learning meets thresholds                  │
│                                                                   │
│  4. User Opt-In                                                   │
│     └─→ User decides whether to propose                          │
│     └─→ /propose-learning <ID> [--dry-run]                       │
│                                                                   │
│  5. Anonymization & Submission                                    │
│     └─→ PII automatically redacted                               │
│     └─→ GitHub Issue created with learning-proposal label        │
│                                                                   │
│  6. Maintainer Review                                             │
│     └─→ Accept: merged to framework learnings                    │
│     └─→ Reject: 90-day cooldown before resubmit                  │
│                                                                   │
└──────────────────────────────────────────────────────────────────┘

Eligibility Thresholds

A learning qualifies for upstream proposal when:

.upstream_detection.min_upstream_score

.upstream_detection.min_occurrences

.upstream_detection.min_success_rate

Upstream Score Calculation

The upstream score is a weighted combination of four components:

Script:

.claude/scripts/upstream-score-calculator.sh

Proposing a Learning

# Preview proposal (recommended first step)
/propose-learning L-0001 --dry-run

# Submit proposal
/propose-learning L-0001

# Force submission (skip eligibility check)
/propose-learning L-0001 --force

Anonymization

Before submission, the following PII is automatically redacted:

sk-*

ghp_*

AKIA*

[REDACTED_API_KEY]

/home/user/*

/Users/*

[REDACTED_PATH]

[REDACTED_DOMAIN]

@mentions

[REDACTED_USER]

*@*.com

[REDACTED_EMAIL]

192.168.*.*

[REDACTED_IP]

Script:

.claude/scripts/anonymize-proposal.sh

Proposal Lifecycle

none

draft

submitted

under_review

accepted

rejected

Checking Status

# Check specific proposal
.claude/scripts/check-proposal-status.sh --learning L-0001

# Check all submitted proposals and sync
.claude/scripts/check-proposal-status.sh --all --sync

Handling Rejection

When a proposal is rejected:

1. rejection.reason

   captured from maintainer feedback

2. rejection.reason_code

   categorized (duplicate, too_specific, etc.)

3. rejection.resubmit_blocked_until

   set to 90 days from rejection
4. Learning can be resubmitted after cooldown expires

Configuration

# .loa.config.yaml
upstream_detection:
  enabled: true
  min_occurrences: 3
  min_success_rate: 0.8
  min_upstream_score: 70
  novelty_threshold: 0.7

upstream_proposals:
  target_repo: "0xHoneyJar/loa"
  label: "learning-proposal"
  anonymization:
    enabled: true
  rejection_cooldown_days: 90

Related Commands

/retrospective

/propose-learning

/compound

Protocol Reference

See

.claude/protocols/continuous-learning.md

• Detailed quality gate criteria with examples
• Zone compliance enforcement
• Pre-commit hook for validation
• Complete trajectory schema

See

grimoires/loa/prd.md

• Full feature requirements
• Maintainer acceptance workflow
• Non-functional requirements