recursive-improvement

L1 Improvement

• Reworked the skill into Skill Forge required sections with explicit triggers, stop conditions, and evidence logging.
• Added prompt-architect ceiling discipline and contract-style IO to keep iterations auditable.

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LIBRARY-FIRST PROTOCOL (MANDATORY)

Before writing ANY code, you MUST check:

Step 1: Library Catalog

• Location: .claude/library/catalog.json
• If match >70%: REUSE or ADAPT

Step 2: Patterns Guide

• Location: .claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md
• If pattern exists: FOLLOW documented approach

Step 3: Existing Projects

• Location: D:\Projects\*
• If found: EXTRACT and adapt

Decision Matrix

Match	Action
Library >90%	REUSE directly
Library 70-90%	ADAPT minimally
Pattern exists	FOLLOW pattern
In project	EXTRACT
No match	BUILD (add to library after)

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STANDARD OPERATING PROCEDURE

Purpose

Manage recursive improvement cycles that collect failures, propose targeted changes, validate them, and stop when marginal gains level off.

Trigger Conditions

• Positive: iterative hardening of skills/prompts/agents, regression triage, A/B comparisons, or postmortem action items.
• Negative/reroute: net-new prompt design (prompt-architect/prompt-forge) or new skill scaffolding (skill-forge/skill-builder).

Guardrails

• Define stop criteria up front (delta threshold, timebox, risk acceptance) to avoid infinite loops.
• Capture evidence for each iteration: inputs, changes, tests, and results with confidence ceilings.
• Keep outputs in English; avoid hidden reasoning.
• Do not silently discard failed experiments—record them for future avoidance.

Execution Phases

1. Intake: Identify artifact to improve, goals, constraints, baseline metrics, and stop criteria; classify constraints as HARD/SOFT/INFERRED.
2. Hypothesis: Propose targeted changes addressing failures or goals; prioritize by impact.
3. Apply & Test: Implement changes, run validation (happy/edge/adversarial), and log results with ceilings.
4. Assess: Compare metrics vs baseline; decide to continue, pivot, or stop based on delta.
5. Package: Summarize iterations, residual risks, and recommended next steps.

Pattern Recognition

• Quality drift → focus on regression tests and schema tightening.
• Safety issues → add guardrails, refusals, and escalation rules.
• Performance/latency complaints → simplify prompts and reduce tool calls.

Advanced Techniques

• Use multi-armed bandit style sampling for competing variants with limited budget.
• Apply self-consistency or debate to stress-test high-risk changes.
• Snapshot checkpoints so reverting is easy when metrics regress.

Common Anti-Patterns

• Iterating without baseline metrics.
• Changing multiple variables simultaneously, making results ambiguous.
• Ignoring ceiling discipline or failing to log evidence.

Practical Guidelines

• Limit each iteration to one or two focused hypotheses.
• Keep a changelog with timestamps, metrics, and confidence statements.
• Escalate to domain specialists when improvements stall.

Cross-Skill Coordination

• Upstream: prompt-architect/prompt-forge to clarify artifacts under test.
• Parallel: cognitive-lensing to unlock new hypotheses.
• Downstream: skill-forge/agent-creator to bake improvements into canonical docs.

MCP Requirements

• Optional memory/vector MCP to store iteration history; tag WHO=recursive-improvement-{session}, WHY=skill-execution.

Input/Output Contracts

inputs:
  target: string  # required artifact to improve
  goals: list[string]  # required goals/metrics
  constraints: list[string]  # optional constraints
  stop_conditions: object  # required thresholds/timebox
outputs:
  iterations: list[object]  # steps taken, tests, outcomes, ceilings
  recommendation: summary  # continue/stop with rationale
  artifacts: list[file]  # updated files if applicable

Recursive Improvement

• Meta: apply this SOP to itself; stop when improvement delta < 2% or risks documented.

Examples

• Harden a prompt that occasionally hallucinates by tightening schema and adding refusals.
• Improve a code-generation skill by adding edge-case tests and measuring deltas.

Troubleshooting

• No improvement after multiple cycles → revisit hypotheses or broaden search (new lenses/tools).
• Metrics regressing → revert to last good checkpoint and reassess constraints.
• Timebox exceeded → summarize current best variant and open risks.

Completion Verification

• Stop conditions defined and respected.
• Iteration logs include tests, results, and ceilings.
• Recommendation provided with residual risks.
• Artifacts updated or explicitly unchanged.

Confidence: 0.70 (ceiling: inference 0.70) - Recursive Improvement SOP rewritten with Skill Forge structure and prompt-architect ceilings.