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    iamladi

    de-slop

    iamladi/de-slop
    Coding

    About

    SKILL.md

    Install

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    About

    This skill should be used to remove AI-generated artifacts and unnecessary code before committing...

    SKILL.md

    De-Slop Skill

    Why this skill exists. This skill enforces Simplicity First — karpathy-principles, principle 2 — applied after the fact. AI assistants tend to over-produce filler comments, vague TODOs, and excessive docstrings; this skill removes the overproduction before it ships.

    Remove AI-generated artifacts before committing or creating PRs. Uses desloppify for quantitative scoring and directed fixes when available; falls back to LLM-based pattern detection otherwise.

    See EXAMPLES.md for ❌/✅ code pairs illustrating each scan category below.

    When to Use

    This skill should be invoked when the user:

    • Says "de-slop", "clean up slop", "remove AI artifacts", or "clean before commit"
    • Is about to commit changes and mentions cleaning/reviewing code
    • Asks to check for unnecessary comments, TODOs, or files
    • Wants to prepare code for PR by removing AI-generated artifacts
    • Is at end of /sdlc:implement and the de-slop gate is triggered

    Primary Workflow (desloppify-powered)

    0. Check desloppify availability

    uvx desloppify --version
    

    If this fails, fall back to the LLM-based Workflow below.

    1. Install/update workflow guide (once per session)

    uvx desloppify update-skill claude
    

    Follow the output — it installs the workflow guide into context. Do not augment or override the instructions it provides.

    2. Scan the target path

    Determine the path to scan. Default is . unless the user specifies otherwise.

    uvx desloppify scan --path {path}
    

    Read the scan output carefully. It contains:

    • Strict score — quantitative measure of AI artifact density
    • Issue list — specific findings with file and line references
    • Agent instructions — follow these verbatim; do not augment or reinterpret

    Record the before score for the final report.

    3. Iterative fix loop

    Run next until there are no more issues:

    uvx desloppify next
    

    Each call to next tells you:

    • Which file to edit
    • What issue to fix
    • The resolve command to run after fixing

    For each issue:

    1. Fix it using the Edit tool (show before/after)
    2. Run the resolve command given by next
    3. Call next again

    Repeat until next reports no more issues.

    Safety rules during fix loop:

    • Never touch README.md, CONTRIBUTING.md, CHANGELOG.md
    • Never auto-delete test files — flag them for manual review instead
    • Show before/after for every code edit
    • When unsure whether a change is correct, flag it rather than auto-fix

    4. Final scan

    uvx desloppify scan --path {path}
    

    Report before score → after score.


    Fallback Workflow (LLM-based)

    Use this when uvx desloppify --version fails.

    1. Determine Comparison Base

    Ask user what to compare against (or use sensible default):

    • No args: Compare against main/master branch
    • Branch name provided: Compare against that branch
    # Get default branch
    git remote show origin | grep "HEAD branch" | cut -d ":" -f 2 | xargs
    
    # Get changed files
    git diff --name-only {BASE}...HEAD
    
    # Get change summary
    git diff --stat {BASE}...HEAD
    

    If no remote, fall back to:

    git diff --name-only main...HEAD
    # or
    git diff --name-only master...HEAD
    

    2. Scan for Slop Patterns (Dry Run Only)

    Scan all changed files for these patterns. DO NOT modify anything yet.

    A. Unnecessary Markdown Files

    Flag for deletion:

    • Filenames matching: NOTES.md, PLAN.md, ARCHITECTURE.md, THOUGHTS.md, IDEAS.md, SCRATCH.md, TEMP.md, TODO.md
    • Case-insensitive match
    • Only if they appear in changed files

    Never touch:

    • README.md, CONTRIBUTING.md, CHANGELOG.md
    • Anything in docs/** directory
    • Any markdown with specific project purpose

    B. Redundant Comments

    Comments that just restate what the next line obviously does.

    Detection:

    • Single-line comment immediately before code
    • Comment essentially restates the code
    • Adds no context, reasoning, or "why"

    See EXAMPLES.md for ❌/✅ pairs.

    C. AI TODO Comments

    Pattern: # TODO: (Add|Consider|Might|Should|Could|May|Probably)

    Flag the vague, generated-as-filler form. Keep specific, actionable TODOs that reference a real follow-up (ticket, decision, or external dependency).

    See EXAMPLES.md for ❌/✅ pairs.

    D. Excessive Docstrings

    Flag docstrings that are excessively long for trivial functions.

    Check for:

    • Function has ≤5 lines of actual code
    • Docstring has >3 lines
    • Docstring just restates what code obviously does

    See EXAMPLES.md for ❌/✅ pairs.

    E. Mock-Heavy Tests

    Flag tests with excessive mocking that test nothing real.

    Pattern:

    • Count @patch decorators per test function
    • Flag if >3 patches
    • Note: CLAUDE.md says "no mocking in tests"

    F. Fake Data in Comments/Docs

    Flag suspiciously specific claims without citation:

    Patterns:

    • "according to studies" (no link)
    • "research indicates" (no source)
    • "X% of users" (no citation)
    • Specific performance metrics without benchmark

    3. Present Findings with Numbered Selection

    Display all findings with clear numbering and actions:

    Scanned X files, found Y slop patterns
    
    [1] NOTES.md (45 lines)
        → DELETE: Unnecessary markdown file
    
    [2] src/user.py:23-28 (6 lines)
        → REMOVE redundant comments
    
    [3] src/api.py:15-25 (11 lines)
        → SIMPLIFY excessive docstring on get_name()
    
    [4] src/utils.py:42
        → REMOVE AI TODO: # TODO: Add error handling
    
    [5] tests/test_user.py:50-70 (test_create_with_mocks)
        → FLAG: Mock-heavy (5 @patch decorators)
        Review manually
    
    ---
    Select items to clean:
      • Enter numbers: 1 2 4
      • Range: 1-4
      • 'all' - clean items 1-4 (skips flags)
      • 'none' - cancel
    
    Selection: _
    
    • Show ±3 lines of context for code issues
    • Separate "actions" (delete/remove/simplify) from "flags" (review needed)
    • "all" only applies to action items, never flags

    4. Execute User Selection

    Parse user input (numbers, ranges, 'all', 'none').

    For file deletions: git rm {FILE}

    For code cleanup: Use Edit tool to remove redundant comments, simplify docstrings, remove AI TODOs. Show before/after for each edit.

    For flagged items: Display file path and line numbers, ask user to review manually.

    5. Summary Report

    Cleaned:
      • 2 files deleted
      • 12 redundant comments removed
      • 3 docstrings simplified
      • 8 AI TODOs removed
      • 67 total lines removed
    
    Flagged for manual review:
      • tests/test_user.py:50-70 (mock-heavy, 5 patches)
    
    Next steps:
      1. Review flagged items (if any)
      2. Run tests: bun test
      3. Verify changes: git diff
      4. Commit: /commit
    

    Safety Rules

    Always follow these regardless of workflow:

    1. Never touch: README.md, CONTRIBUTING.md, CHANGELOG.md, docs/**, test files (only flag)
    2. When unsure: Flag for review, don't auto-fix
    3. Show before/after for all code changes
    4. Confirm before deleting >5 files or removing >50 lines total
    5. Preserve formatting: Keep indentation when removing comments
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    Repository
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