Skill Smell Checker

Skill Smell Checker

Audit all 26 smells from Hong, Imani, and Ahmed's From Anatomy to Smells. Treat the five static results as script-derived facts and the other 21 as evidence-backed semantic judgments.

Workflow

1. Locate the target SKILL.md. For a repository-wide audit, discover files with rg --files -g '**/SKILL.md'.

2. Run the deterministic harness:

   python SKILL_DIR/scripts/check_static_smells.py path/to/SKILL.md --pretty
   

   Replace SKILL_DIR with this skill's directory. Do not manually override its five results. Use --strict only when a nonzero exit status should fail CI.

3. Read references/smell-catalog.md completely.

4. Review every semantic smell against the full target skill and any bundled scripts/, references/, or assets/ needed to judge delegation, validation, templates, or utility-script availability.

5. For each semantic smell, record present, absent, or uncertain, cite concrete file evidence, and give confidence. Never infer a clean result from a missing section title alone.

6. Produce the report using the template below. Rank fixes by security, execution correctness, discoverability, then context efficiency.

7. If remediation was requested, make the smallest behavior-preserving edits, rerun the static harness, and repeat the semantic review for changed smells.

Track multi-file audits as pending, checked, or blocked; do not silently drop files or smells.

Decision Rules

• If the harness reports a smell, mark it present.
• If the harness cannot parse frontmatter, report its warning and do not claim LSN, LSD, or XID are absent.
• If a semantic decision depends on the skill's intended users, required output, or acceptable autonomy and the repository does not answer it, ask one focused human question. Mark the smell uncertain until answered.
• If a smell is condition-dependent, explain why the condition does or does not apply; do not treat "not applicable" as missing evidence.
• Do not invent thresholds for semantic smells. The paper specifies numeric thresholds only for LSB, LSN, and LSD.
• Do not skip validation because the file looks simple or because another smell appears more important. A complete audit covers 5 static and 21 semantic smells.

Report Template

# Skill Smell Audit: <path>

## Summary
- Static: <present>/5 present
- Semantic: <present>/21 present, <uncertain> uncertain
- Highest-priority issue: <id and reason>

## Findings
| ID | Smell | Mode | Verdict | Confidence | Evidence | Minimal remediation |
|---|---|---|---|---|---|---|
| LSB | Lengthy Skill Body | static | absent | high | 812 / 5,000 words | — |
| RL | Rationalization Loophole | semantic | present | high | No instruction prevents skipping required validation | Add one explicit completion guard |

## Harness Warnings
<warnings or "None">

## Scope
<files and bundled resources inspected>

Gotchas

• The paper defines 26 smells: 5 static and 21 semantic.
• The paper reports weighted F1 0.78 for its semantic LLM detector. Treat semantic output as review evidence, not ground truth.
• BP detects path-shaped backslashes, not every backslash in code or prose.
• XID detects tag-shaped XML in the frontmatter description. Comparisons such as x < y are not tags.
• The paper's supplementary repository was unavailable during this skill's construction. The harness implements the published Table III definitions and documents its parser assumptions in --help.

Verification

Run:

python SKILL_DIR/scripts/test_check_static_smells.py
python SKILL_DIR/scripts/check_static_smells.py SKILL_DIR/SKILL.md --pretty

Do not declare an audit complete unless the harness ran successfully and the report accounts for all 26 smell IDs.

For the converted paper text, read references/paper.md only when the user asks for the research basis, methodology, limitations, or exact surrounding discussion.