One repeatable, evidence-backed pass that retires a bounded batch of maturity debt worst-first across the system's dedicated debt lanes. Features rich queryability (--query, --health), cross-indexes related items (dual-repo companions, tests, runtime-proofs, benchmarks, inbound blast radius), targets high-carrying-cost or degraded hotspots, advances maturity with tests, integration, and benchmarks, re-measures with --compare to prove the denominator was level-set, and commits by explicit path...
Scanned 9/12/2026
Install to Claude Code
Skills Directorynpx -y skills add anthony-chaudhary/fak --skill debt-clean --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: debt-clean
description: One repeatable, evidence-backed pass that retires a bounded batch of maturity debt worst-first across the system's dedicated debt lanes. Features rich queryability (--query, --health), cross-indexes related items (dual-repo companions, tests, runtime-proofs, benchmarks, inbound blast radius), targets high-carrying-cost or degraded hotspots, advances maturity with tests, integration, and benchmarks, re-measures with --compare to prove the denominator was level-set, and commits by explicit path with (fak <leaf>). Use when cleaning or retiring maturity debt across units of work.
metadata:
canonical: ../../../.claude/skills/debt-clean/SKILL.md
---
# Canonical project skill adapter
Load and follow [`../../../.claude/skills/debt-clean/SKILL.md`](../../../.claude/skills/debt-clean/SKILL.md). This generated discovery adapter contains no maintained workflow body.
## Portability contract
- The linked canonical `SKILL.md` is the single semantic workflow body for Claude, Codex, and fak-native loaders.
- This adapter changes discovery only; it must not fork, summarize, or translate the workflow.
- Harness-native invocation, permissions, hooks, model routing, and worker launch remain typed adapters outside the semantic body.
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A comprehensive mathematical reasoning skill for AI assistants — handles arithmetic to research-level problems with rigorous step-by-step reasoning, systematic verification, and transparent uncertainty handling
