AGENT SKILL

fabric-heal

Self-healing skill that improves signal mapper keyword coverage and capability coverage (ADR-0002) through iterative problem generation and keyword/capability patching. Use when user says "heal signal mapper", "improve keyword coverage", "improve capability coverage", "generate problem statements", "run healing loop", "patch signal mapper", or asks about "signal mapper gaps" or "capability gaps". Do NOT use for project architecture or deployment.

License 许可
MIT
最近更新
2026-05-22
Stars
★ 50
Forks
16

01 前言 Front Matter

name
fabric-heal
description
Self-healing skill that improves signal mapper keyword coverage and capability coverage (ADR-0002) through iterative problem generation and keyword/capability patching. Use when user says "heal signal mapper", "improve keyword coverage", "improve capability coverage", "generate problem statements", "run healing loop", "patch signal mapper", or asks about "signal mapper gaps" or "capability gaps". Do NOT use for project architecture or deployment.

02 资源文件 10

03 SKILL.md 正文

Fabric Signal Mapper Healer

Mode 1: Generate Problem Statements

Orchestrator passes --mode generate --batch-num N. Write 25 novel problem statements to problem-statements.md.

Requirements

Realistic (enterprise user), specific (volumes/tools/deadlines), multi-signal (2-3 categories), domain-diverse, varying complexity.

Output Format (parser-dependent)

# Problem Statements for Stress Testing

> Auto-generated batch N — 25 problems for self-healing loop.

## Category Name

1. "Problem statement text in quotes."

2. "Another problem in same category."

Use 5-8 categories per batch, 3-5 problems per category.

Mode 2: Analyze Gaps and Patch Keywords

Orchestrator passes --mode heal with benchmark results (coverage %, zero-candidate count, uncovered terms).

  1. Map uncovered terms to signal categories (1-11)
  2. Update _shared/registry/signal-categories.json using signal-categories-cli.py (max 15 new keywords per category per iteration)

Orchestrator Reporting (Verbose Distribution)

heal-orchestrator.py prints a verbose, deterministic distribution report with ASCII bar charts:

  • Task flow suggestion rate
  • Top candidate distribution
  • Pattern-category distribution (batch/streaming/hybrid/governance/app-api)
  • Skillset distribution (LC/CF/mixed tendencies)
  • Item type frequency across suggested flows
  • Recommendations derived from benchmark thresholds and recurring uncovered terms

Detailed analytics are persisted to:

  • .github/skills/fabric-heal/scripts/_heal-loop-results.json

Constraints

  • Use signal-mapper.py for all signal lookups.
  • For keyword maintenance, use _shared/scripts/signal-categories-cli.py (add/remove/move/list) instead of manual raw JSON edits.
  • Never remove existing keywords or modify the matching algorithm
  • Clean up generated files: delete problem-statements-batch*.md after each loop completes
  • Prefer specific terms over generic ones

References

  • .github/skills/fabric-discover/scripts/signal-mapper.py — current keyword categories
  • _shared/scripts/signal-categories-cli.py — safe keyword maintenance helper for signal categories registry
  • problem-statements.md — existing format (in this skill's folder)
  • task-flows.md — task flow descriptions

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