Skip to main content

find-dead-code

Find unused functions, classes, imports, and unreachable code paths. Use when the user asks to find dead code, unused code, cleanup candidates, or wants to reduce codebase size.

Quellinformationen

Repository
openshift/lightspeed-service
Letzte Quellaktivität
18. März 2026 um 15:56
Erkannte Sprache von SKILL.md
Englisch
Sterne
70
Forks
94

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
find-dead-code
description
Find unused functions, classes, imports, and unreachable code paths. Use when the user asks to find dead code, unused code, cleanup candidates, or wants to reduce codebase size.
disable-model-invocation
true
# Find Dead Code Detect unused code that can be safely removed. ## Rules - Report findings, do not delete. Removal is a separate task. - Focus on production code (`ols/`). Skip tests unless explicitly asked. - Vulture has false positives — classify each finding before recommending removal. - Code used only via dynamic dispatch (e.g. Pydantic validators, FastAPI dependencies) is not dead. ## Step 1: Determine Scope Ask the user: - **Branch mode**: only files changed in the current branch vs main. - **Full mode**: scan the entire `ols/` directory. For branch mode: ```bash git diff --name-only origin/main -- 'ols/' | grep '\.py$' ``` ## Step 2: Run Vulture ```bash uvx vulture <target> --min-confidence 80 ``` `--min-confidence 80` reduces noise. Lower confidence findings are more likely false positives. ## Step 3: Run Pylint Unused Checks ```bash uv run pylint --disable=all --enable=unused-import,unused-variable,unused-argument,unreachable <target> ``` Cross-reference with vulture findings to increase confidence. ## Step 4: Filter False Positives Common false positives in this codebase: | Pattern | Why it's not dead | |---------|-------------------| | Pydantic `model_validator`, `field_validator` | Called by Pydantic, not directly | | FastAPI dependency functions | Injected via `Depends()` | | `__eq__`, `__hash__`, `__str__` | Called implicitly by Python | | Constants used in config YAML | Referenced by config loader | | Abstract method implementations | Called via base class interface | | Imports re-exported from `__init__.py` | Used by external consumers | ## Step 5: Classify Findings For each finding, classify: | Category | Criteria | Action | |----------|----------|--------| | **Remove** | Clearly unused, no dynamic references | Safe to delete | | **Verify** | Possibly used dynamically or externally | Search for string references before removing | | **False positive** | Pydantic/FastAPI/magic method | Skip | For "Verify" findings, search for string-based references: ```bash rg "<function_or_class_name>" ols/ tests/ ``` ## Step 6: Report For each finding: 1. File and line number 2. What is unused (function, class, import, variable) 3. Confidence level (vulture %) 4. Classification (remove / verify / false positive) Summary: total findings, how many safe to remove, estimated lines saved.
Auf GitHub ansehen