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dead-code-sweep skill

/skills/dead-code-sweep

This skill helps identify and safely remove dead code, orphaned artifacts, and redundant implementations in agent-maintained codebases.

npx playbooks add skill petekp/agent-skills --skill dead-code-sweep

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: dead-code-sweep
description: |
  This skill should be used when cleaning up codebases that have accumulated dead code, redundant implementations, and orphaned artifacts — especially codebases maintained by coding agents. Triggers on "find dead code", "clean up unused code", "remove redundant code", "prune this codebase", "dead code sweep", "code cleanup", or when a codebase has gone through multiple agent-driven refactors and likely contains overlooked remnants. Systematically identifies cruft, categorizes findings, and removes confirmed dead code with user approval.
license: MIT
metadata:
  author: petekp
  version: "0.1.0"
---

# Dead Code Sweep

Systematic identification and removal of dead code, redundant implementations, and orphaned artifacts in codebases — particularly those maintained by coding agents with limited context windows.

## Why Agent-Maintained Codebases Accumulate Cruft

Coding agents operate within narrow context windows. When refactoring, they often:

- Re-implement functionality without finding and removing the original
- Leave compatibility shims for interfaces that no longer exist
- Abandon helper functions after inlining their logic
- Create new files without deleting the ones they replace
- Duplicate type definitions across module boundaries
- Leave imports for symbols they stopped using mid-refactor

This cruft compounds. Each orphaned artifact misleads the next agent (or human), who wastes context budget reading code that does nothing.

## Workflow

### Phase 1: Scope and Inventory

Determine the analysis boundary.

**If the user provides a scope** (directory, file pattern, module), constrain all analysis to that scope.

**If no scope is provided**, analyze the full codebase. Start by reading the project structure:

1. Identify the primary language(s) and framework(s)
2. Map entry points (main files, route definitions, exported modules, test runners)
3. Note the build system and dependency configuration
4. Check for monorepo structure — analyze each package as a unit

Produce a brief inventory:

```
Language(s): TypeScript, Python
Entry points: src/index.ts, src/cli.ts
Build: esbuild via package.json scripts
Packages: 1 (single package)
Estimated LOC: ~4,200
```

### Phase 2: Detection

Launch parallel sub-agents to scan for different categories of dead code. Read `references/cruft-patterns.md` for the full catalog of detection patterns.

Organize detection into these parallel tracks:

| Track | What It Finds |
|-------|--------------|
| **Orphaned files** | Files not imported, required, or referenced by any other file |
| **Unused exports** | Exported symbols (functions, classes, types, constants) never imported elsewhere |
| **Redundant implementations** | Multiple functions/classes doing the same thing under different names |
| **Stale compatibility code** | Shims, adapters, wrappers, and re-exports that bridge interfaces that no longer differ |
| **Dead branches** | Conditional paths that can never execute (always-true/false guards, unreachable returns) |
| **Orphaned tests** | Test files testing functions or modules that no longer exist |
| **Orphaned dependencies** | Packages in dependency manifests not imported anywhere in source |

For each track, the sub-agent should:

1. Read `references/cruft-patterns.md` for detection strategies specific to that track
2. Search the codebase using Grep and Glob
3. Verify each candidate by tracing references — a symbol is only dead if **zero live code paths** reach it
4. Record findings with file path, line range, and evidence

**Verification is critical.** Common false positives to watch for:

- Symbols used via dynamic dispatch, reflection, or string-based lookups
- Framework-magic exports (e.g., Next.js page components, pytest fixtures, Rails conventions)
- Public API surface intended for external consumers
- Conditional imports behind feature flags or environment checks
- Decorator-registered or plugin-registered handlers
- CLI entry points referenced in package.json `bin` fields
- CSS class names referenced in templates or JSX as dynamic strings

When uncertain, mark as "needs review" rather than "confirmed dead."

### Phase 3: Report

Consolidate all findings into a structured report at `.claude/dead-code-report.md`.

Organize findings by confidence level, then by category:

```markdown
# Dead Code Sweep Report

**Scope:** [full codebase | specific path]
**Date:** [date]
**Estimated removable lines:** [count]

## Confirmed Dead (high confidence)

### Orphaned Files
- `src/utils/old-parser.ts` — Not imported anywhere. Superseded by `src/parser/index.ts`.
- ...

### Unused Exports
- `formatDate()` in `src/helpers.ts:42-58` — Exported but zero imports across codebase.
- ...

[...other categories...]

## Needs Review (uncertain)

### Possibly Dynamic
- `handleLegacyEvent()` in `src/events.ts:91` — No static imports, but may be registered dynamically.
- ...
```

For each finding, include:

- **File path and line range**
- **What it is** (function, class, file, type, constant, dependency)
- **Why it appears dead** (no imports, no references, superseded by X)
- **Confidence** (confirmed / needs review)

### Phase 4: Cleanup with Approval

Present the report summary to the user and ask for approval before removing anything.

Use AskUserQuestion to present findings by category:

```
Found 12 confirmed dead items and 3 needing review.

Confirmed dead by category:
- 3 orphaned files (~280 lines)
- 5 unused exports (~120 lines)
- 2 redundant implementations (~90 lines)
- 2 orphaned dependencies

Which categories should I clean up?
```

Options: Remove all confirmed / Select categories / Review each item / Skip cleanup

For each approved category:

1. Remove the dead code
2. Clean up any imports that referenced the removed code
3. Remove empty files left after cleanup
4. Remove orphaned dependencies from the manifest
5. Run the project's lint/typecheck/build commands to verify nothing broke

If any removal causes a build or type error, immediately revert that specific removal and move the item to "needs review."

After cleanup, update the report with what was removed and what was kept.

## Detection Principles

### Trace from entry points, not from suspects

Start from known entry points and trace what's reachable, rather than starting from a suspect symbol and trying to prove it's used. The reachability approach has fewer false negatives.

### Respect the module boundary

A symbol exported from a package's public API may be consumed by external code not visible in this repository. When analyzing libraries or packages with external consumers, only flag internal (non-public-API) dead code as "confirmed." Flag public API dead code as "needs review."

### Look for clusters, not just individuals

Agent-generated cruft tends to cluster. When one dead function is found, examine its neighbors — the agent likely abandoned the entire section during a refactor. A dead file often has sibling dead files created in the same commit.

### Use git history as a signal

When available, check when suspect code was last meaningfully modified. Code untouched across several refactor commits is more likely dead. Use `git log --follow` to trace renames and detect superseded files.

Overview

This skill automates systematic identification and safe removal of dead code, redundant implementations, and orphaned artifacts in codebases. It is tuned for repositories maintained or refactored by coding agents, where overlooked remnants commonly accumulate. The workflow inventories the project, detects likely cruft, generates a confidence-ranked report, and removes confirmed dead code only after user approval.

How this skill works

The skill first scopes the analysis to a provided directory or the whole repo, maps entry points, and estimates language and build structure. It runs parallel detectors for orphaned files, unused exports, redundant implementations, stale compatibility shims, dead branches, orphaned tests, and unused dependencies. Each candidate is verified by tracing references, checking git history, and respecting dynamic usage patterns before being marked confirmed or needs review.

When to use it

  • After multiple automated refactors or agent-driven merges
  • When the codebase feels cluttered or builds include many unused files
  • Before major upgrades to reduce regression risk from hidden cruft
  • When preparing a library/package for public release to shrink surface area
  • During maintenance sprints to lower technical debt and improve readability

Best practices

  • Provide an explicit scope (directory or package) to limit analysis and speed results
  • Review the generated report and approve only safe categories for automatic removal
  • Watch for framework conventions and dynamic registration; mark uncertain items as needs review
  • Run tests, typechecks, and builds after each approved cleanup and revert any breaking removals
  • Prefer removing clusters of nearby dead code together to reduce follow-up churn

Example use cases

  • Prune an agent-refactored frontend repo that has duplicate component implementations
  • Clean a Python package with orphaned helper modules and unused exports before publishing
  • Audit a monorepo to find packages with unused dependencies and orphaned test suites
  • Reduce noise in CI by removing dead branches and unreachable conditional paths
  • Prepare a repository for new contributors by shrinking codebase and clarifying entry points

FAQ

How do you avoid false positives for framework magic or dynamic imports?

The skill treats such patterns cautiously: it detects likely dynamic usage, marks those items as needs review, and only confirms removal after explicit verification or user approval.

Will you modify public APIs used by external consumers?

No. Public API symbols are flagged as needs review rather than auto-removed. Only internal, non-public items are eligible for confirmed cleanup.