Deslop

Remove AI-generated slop while preserving behavior. Companion to aislop scanner — aislop detects, this skill cleans.

Do NOT use when

• Normal coding or refactoring (use deep-code-quality-review)
• Security issues found (use security-review)
• Writing new code (not applicable)
• Reviewing issue/PR technical correctness (use investigate or appropriate review skill)

Slop patterns (see catalog)

Code patterns:

1. Narrative comments — describing what code obviously does
2. Unnecessary verbosity — 10 lines where 3 suffice
3. Hallucinated imports — modules not used or not existing
4. Mechanical padding — single-call wrappers, unnecessary intermediates
5. Generic error messages — "An error occurred" instead of context
6. TODO stubs with no implementation
7. Redundant type annotations — already inferred

Issue/PR text patterns:

1. Over-confident root cause analysis without evidence
2. Generic implementation strategies without project-specific context
3. Long lists of irrelevant error classes
4. AI-generated issue expansion that broadens scope with hypotheses
5. Hallucinated code references or function names

Subtraction rules

• Delete, don't replace. If a comment explains what → delete it, don't rewrite it.
• Inline, don't redirect. Single-call helper → inline and delete.
• Shrink, don't reorganize. 10-line function can be 3 → shrink, don't split.
• Remove dead code. Unused imports, unreachable branches.
• Preserve intent. Comments explaining why → keep. Helpers with meaningful naming → keep.

Workflow

For code cleanup:

1. Run ck_git_diff to scope changes. Run ck_validate for aislop findings.
2. List slop patterns found. Plan removals — do not start editing until plan is clear.
3. Apply removals. Run tests after each logical group.
4. The diff must be smaller after cleanup, not just different.
5. Full suite must pass. Zero new failures.
6. ck_validate on cleaned diff. ck_git_commit.

For issue/PR text cleanup:

1. Identify the issue or PR description text to clean.
2. Run ck_validate with source_type: "issue" or source_type: "pull_request" to assess trust level.
3. List slop patterns found (over-confident analysis, generic strategies, hallucinated references).
4. Remove AI-generated expansions and stick to observed facts: command run, expected behavior, actual behavior, exact error/log.
5. Replace hallucinated code references with accurate ones after verification.
6. Remove generic implementation advice; keep only specific, actionable guidance.
7. Validate the cleaned text preserves the core issue report without the AI-generated noise.

Rules

For code:

• Behavior preservation — every passing test stays passing
• No scope creep — fix slop only, do not refactor or add features
• Smaller diff — fewer lines after cleanup, not same or more

For issue/PR text:

• Fact preservation — keep the actual observed behavior and errors
• No scope narrowing — don't remove important context or edge cases
• Evidence-based — replace confident analysis with observed facts
• Specific over generic — keep concrete details, remove vague suggestions

Output

For code:

• Cleaned code with slop removed
• Full suite passing
• Commit with cleanup

For issue/PR text:

• Cleaned description with AI-generated noise removed
• Preserved core issue report with observed facts
• Specific, actionable guidance without over-confident analysis