Deslop

SkillDev tools

Run a bounded final deslop pass on a nearly-finished change using slop-scan deltas, focused review lenses, and post-cleanup re-verification

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Deslop skill

What this skill tells your AI

The instructions your AI receives, as published by udecode/kitcn in .agents/skills/deslop/SKILL.md and read by ahel’s review.

Use this after the implementation already works and before commit or push. This is not a planning workflow and not a rescue mission for broken code. It is the last pressure pass that strips noise out of an otherwise-correct diff.

Core Contract

  • Deslop is a bounded helper inside the normal execution lane, not a competing end-to-end workflow.
  • Default scope is changed files only. Do not widen to the whole repo unless the user asks.
  • Prefer deleting or inlining over introducing fresh abstraction.
  • Treat slop-scan as signal, not law. Fix the good hits. Ignore the fake ones.

What Counts As Slop

Focus on concrete smell families, not vague "AI code" vibes:

  • needless abstraction: pass-through wrappers, barrel-only indirection, async noise, single-use helper layers
  • duplication: repeated signatures, repeated mock setup, local copy-paste helpers
  • defensive noise: empty catches, error swallowing, error obscuring where the real fix is to rethrow or narrow once
  • structural fragmentation: over-fragmentation and fan-out hotspots when they land in the changed area
  • placeholders and leftovers: TODO comments, dead helpers, debug scraps, fake scaffolding text

Inputs To Gather

Before cleanup, collect:

  • repo root AGENTS.md
  • any nested AGENTS.md or design docs that govern the changed area
  • the active plan file when one exists
  • the changed-files list or explicit file scope
  • fresh verification evidence from the implementation pass
  • the repo slop delta command output

Review Lenses

When the user explicitly asked for deslop / cleanup / refactor, run these 3 review lenses in parallel on the same context bundle:

  1. Repo rules and documentation conformance
  2. Type safety and source of truth
  3. Simplification and overengineering

If parallel reviewers are unavailable, do the same 3 lenses locally before editing.

Procedure

  1. Lock behavior first

    • If current behavior is not already protected, add or run the narrowest regression proof before cleanup edits.
  2. Bound the scope

    • Build a changed-files list or explicit file scope.
    • Stay inside that scope for both review and cleanup.
  3. Launch the review lenses

    • Give every reviewer the same context bundle and one assigned lens.
    • Ask for findings first, ordered by severity, with file references.
  4. Run slop delta while reviewers work

    • Prefer the repo script: bun run lint:slop:delta
    • If it does not exist, fall back to slop-scan delta ...
    • Use delta, not raw repo-wide scan, to focus on added and worsened noise.
  5. Synthesize before editing

    • Merge the reviewer output and delta output under:
      • How did we do?
      • Feedback to keep
      • Feedback to ignore
      • Plan of attack
  6. Apply only the worthwhile fixes

    • type drift, casts, or duplicated type definitions
    • pass-through wrappers, async noise, fake helper seams
    • dead helpers, placeholder comments, debug leftovers
    • duplicate mock setup or local copy-paste helpers
    • empty or error-obscuring internal catches when the right fix is simpler
  7. Re-run the narrowest affected verification

    • rerun the same tests, lint, and typecheck lanes that protect the touched area
    • if a stronger final gate already existed, rerun it after cleanup
  8. Update the story

    • Make sure the active plan, commit text, and PR-facing text describe the post-deslop state, not the earlier draft.

Stop Rules

  • Do not widen scope because the scan found juicy unrelated cleanup.
  • Do not start product work during deslop.
  • Do not add abstraction to fix abstraction.
  • If generated, vendored, or fixture noise dominates the scan, filter it and move on.
  • If behavior is not locked and cleanup would be risky, stop and lock behavior first.

Expected Output

Return an evidence-dense cleanup report:

  • scope
  • behavior lock / verification used
  • top slop deltas
  • feedback kept vs ignored
  • simplifications applied
  • post-deslop verification
  • remaining risks or explicitly deferred cleanup

Signals

GitHub stars
447
Forks
34
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
deslop-udecode
Source
github.com/udecode/kitcn