本文へ移動
cccskills
無料GitHub で公開

llm-artifacts-detection

Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.

インストール方法を見る

含まれるファイル(5)

  • SKILL.md6.5 KB
  • references/abstraction-criteria.md7.5 KB
  • references/dead-code-criteria.md5.3 KB
  • references/style-criteria.md6.4 KB
  • references/tests-criteria.md5.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

LLM Artifacts Detection

Detect and flag common patterns introduced by LLM coding agents that reduce code quality.

Detection Categories

CategoryReferenceKey Issues
Testsreferences/tests-criteria.mdDRY violations, library testing, mock boundaries
Dead Codereferences/dead-code-criteria.mdUnused code, TODO/FIXME, backwards compat cruft
Abstractionreferences/abstraction-criteria.mdOver-abstraction, copy-paste drift, over-configuration
Stylereferences/style-criteria.mdObvious comments, defensive overkill, unnecessary types

Agent Prompts

Use these prompts to spawn focused detection agents:

Tests Agent

Analyze the test files for LLM-introduced test quality issues:

1. **DRY Violations**: Look for setup/teardown code repeated across multiple test functions instead of using fixtures or shared helpers. Flag patterns like:
   - Identical object creation in multiple tests
   - Repeated mock configurations
   - Copy-pasted database setup

2. **Library Testing**: Identify tests that validate standard library or framework behavior rather than application code. Signs:
   - No imports from the application codebase
   - Testing built-in functions or third-party library methods
   - Assertions about stdlib behavior

3. **Mock Boundaries**: Flag mocking that's too deep or too shallow:
   - Too deep: Mocking internal implementation details, private methods
   - Too shallow: Mocking at the wrong layer, missing integration points
   - Wrong level: Unit test mocks in integration tests or vice versa

For each issue found, report: [FILE:LINE] ISSUE_TITLE

Dead Code Agent

Scan the codebase for dead code and cleanup opportunities:

1. **Unused Code**: Find functions, classes, and variables with no references:
   - Functions never called
   - Classes never instantiated
   - Module-level variables never read
   - Unreachable code after returns

2. **TODO/FIXME Comments**: Flag all TODO, FIXME, HACK, XXX comments that indicate incomplete work

3. **Backwards Compat Cruft**: Look for patterns suggesting removed features:
   - Variables renamed with _unused, _old, _deprecated suffixes
   - Re-exports only for backwards compatibility
   - Comments like "# removed", "# legacy", "# deprecated"
   - Empty functions/classes kept "for compatibility"

4. **Orphaned Tests**: Tests for code that no longer exists:
   - Test files with no corresponding source
   - Test functions testing deleted features

For each issue found, report: [FILE:LINE] ISSUE_TITLE

Abstraction Agent

Review the codebase for over-engineering introduced by LLM agents:

1. **Over-Abstraction**: Identify unnecessary abstraction layers:
   - Wrapper classes that just delegate to one method
   - Interfaces/protocols with only one implementation
   - Abstract base classes with single concrete class
   - Factory functions that always return the same type

2. **Copy-Paste Drift**: Find 3+ similar code blocks that should be parameterized:
   - Nearly identical functions with minor variations
   - Repeated patterns that could be a single function with parameters
   - Similar class methods across multiple classes

3. **Over-Configuration**: Flag configuration for non-configurable things:
   - Feature flags that are never toggled
   - Environment variables always set to one value
   - Config options with no production variation
   - Overly generic code for single use case

For each issue found, report: [FILE:LINE] ISSUE_TITLE

Style Agent

Check for verbose LLM-style patterns that reduce code clarity:

1. **Obvious Comments**: Comments that restate what the code clearly does:
   - "# increment counter" above counter += 1
   - "# return the result" above return result
   - Docstrings that repeat the function name

2. **Over-Documentation**: Excessive documentation on trivial code:
   - Full docstrings on simple getters/setters
   - Parameter descriptions for obvious args
   - Return value docs for self-evident returns

3. **Defensive Overkill**: Unnecessary defensive programming:
   - try/except around code that cannot fail
   - Null checks on values that can't be null
   - Type checks after type hints guarantee the type
   - Validation of already-validated inputs

4. **Unnecessary Type Hints**: Type hints that add no value:
   - Type hints on obvious literal assignments
   - Redundant hints on variables immediately clear from context
   - Over-annotated internal/local variables

For each issue found, report: [FILE:LINE] ISSUE_TITLE

Gates (reporting)

Run these in order so findings are evidence-bound, not inferred. This is the detection-side instance of the Anti-confabulation gate in the review-verification-protocol skill: every [FILE:LINE] must be echoed from a freshly read buffer in this turn, never inferred from the branch name, directory, or memory.

  1. Anchor — Set FILE and LINE from an opened buffer, read_file, or equivalent; do not rely only on stale search snippets. Pass: LINE is in range for FILE, and the described issue is visible on that line or its immediate neighbors.
  2. Title — ISSUE_TITLE states the defect in plain language (about one short sentence), not a proposed fix. Pass: someone opening FILE at LINE can see why the title applies.
  3. Dedup — Before final output, merge rows that share the same FILE:LINE and root cause. Pass: at most one [FILE:LINE] ISSUE_TITLE per distinct defect at that anchor.

Usage

  1. Load this skill when reviewing AI-generated code
  2. If the agent supports subagents, dispatch one per detection category in parallel; otherwise work through the categories sequentially yourself, producing the same [FILE:LINE] ISSUE_TITLE findings.
  3. Use reference files for detailed criteria and examples
  4. Apply Gates (reporting) above, then emit findings as [FILE:LINE] ISSUE_TITLE

When to Apply

  • Cleaning up code written by AI coding agents
  • Post-generation code review
  • Reducing code bloat from iterative AI generation
  • Identifying patterns that reduce maintainability

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use when you need to mine a conversation, session transcript, or design discussion for architectural decisions before writing ADRs. Identifies problem-solution pairs, trade-off debates, technology choices, and explicit "[ADR]" tags. Triggers on "what decisions did we make", "extract decisions from this chat", "find the choices in our discussion", or "summarize architectural decisions". Also useful after long planning sessions to capture decisions that were made implicitly. Does NOT write ADR documents — use adr-writing or write-adr for that.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when writing or formatting an ADR document using the MADR template, applying Definition of Done (E.C.A.D.R.) criteria, or verifying ADR completeness. Triggers on "write the ADR", "format as MADR", "check ADR quality", "mark gaps in ADR". Also triggers when a decision has been extracted and needs to become a document. Does NOT extract decisions from conversations (use adr-decision-extraction) or orchestrate the full extract-confirm-write workflow (use write-adr).

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Vercel AI Elements for workflow UI components. Use when building chat interfaces, displaying tool execution, showing reasoning/thinking, or creating job queues. Triggers on ai-elements, Queue, Confirmation, Tool, Reasoning, Shimmer, Loader, Message, Conversation, PromptInput.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Reviews App Intents code for intent structure, entities, shortcuts, and parameters. Use when reviewing code with import AppIntents, @AppIntent, AppEntity, AppShortcutsProvider, or @Parameter.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when the user wants a cited, structured read of local documents and project knowledge. Triggers on: "analyze these docs", "scan my project for context", "read the docs folder", "summarize what's in .beagle/concepts/", "extract context from docs/", "what's in this folder", "go read everything in X and tell me what's there". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), web research (use web-research), LLM-as-judge evaluation (use llm-judge), or document editing (use humanize-beagle). Produces a written scan plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

existential-birds のスキルをすべて見る

このスキルの問題を報告する