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self-evolving-heuristics

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Analyze the codebase to find hard-coded thresholds, defaults, and timing constants, then convert them into self-evolving heuristics backed by a persistent registry that updates on each execution. Use when asked to replace fixed decision logic with adaptive parameters, build a heuristic registry, or generate a patch plan for heuristic rewrites.

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How to use this skill

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Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/development/self-evolving-heuristics/SKILL.md

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Self Evolving Heuristics

Overview

Identify static decision points in the codebase and rewrite them as registry-backed heuristics that evolve via runtime observations.

Workflow

  1. Scan for bottlenecks
    • python scripts/scan_bottlenecks.py --root . --output runs/heuristics_bottlenecks.json
  2. Build the heuristic registry
    • python scripts/build_registry.py --input runs/heuristics_bottlenecks.json --output memory/agent_state_v1/heuristics.json
  3. Install the registry module for runtime use
    • python scripts/install_registry.py --repo .
  4. Emit a patch plan
    • python scripts/emit_patch_plan.py --input runs/heuristics_bottlenecks.json --output runs/heuristics_patch_plan.md
  5. Apply the plan manually
    • Replace constants with heuristics_registry.get_value("key", default=<literal>).
    • Add heuristics_registry.observe("key", observed_value) after each execution to evolve the value.

Update Pattern

from core.sovereignty_v2 import heuristics_registry

threshold = heuristics_registry.get_value("core_worker.cpu_threshold", default=0.85)
if cpu_usage > threshold:
    ...
heuristics_registry.observe("core_worker.cpu_threshold", cpu_usage)

Notes

  • Keep bounds conservative and only update from reliable signals.
  • Re-run the scan after patching to confirm remaining constants are intentional.