anti-slop

name	anti-slop
description	· Audit AI-generated code slop: hallucinated APIs, over-abstraction, duplicate code, test theater, noisy comments. Triggers: 'slop', 'AI-generated code', 'cleanup', 'overengineered'. Not for prose (use anti-ai-prose).
license	MIT
compatibility	None - works on any codebase
metadata	{"source":"iuliandita/skills","date_added":"2026-03-25","effort":"medium","argument_hint":"<file-or-pattern>"}

Anti-Slop: Polyglot Code Quality Audit

Detect and fix patterns that make code look machine-generated, over-abstracted, unnecessarily verbose, or fluently wrong. The goal is code that reads like a competent human wrote it - minimal, intentional, grounded, and clear.

This skill covers: TypeScript/JavaScript, Python, Bash/Shell, Rust, Docker/Containers, and Infrastructure as Code (Terraform, Ansible, Helm, Kubernetes manifests). The universal patterns apply everywhere; language-specific sections add targeted checks.

When to use

• Reviewing code that feels machine-generated, bloated, or oddly generic
• Simplifying code after an AI-heavy implementation pass
• Auditing comment noise, naming quality, over-abstraction, and dependency creep
• Looking for "ugly but technically works" code that still hurts readability or maintainability
• Looking for AI-native tells: hallucinated APIs, schema drift, fallback laundering, and tests that only ratify the implementation

The Three Axes of Slop

Every finding falls into one of three categories:

1. Noise - bulk without value (redundant comments, boilerplate, unnecessary types/annotations)
2. Lies - subtly wrong or outdated (hallucinated APIs, deprecated patterns, stale deps)
3. Soul - lacks taste (over-abstraction, generic names, defensive overkill)

When NOT to use

• Correctness, logic, or race-condition bugs - use code-review
• Security vulnerabilities, secret scanning, or auth review - use security-audit
• One-off prompt authoring or prompt templates - use prompt-generator
• Session-end documentation maintenance - use update-docs
• Prose audit of docs, READMEs, wikis, emails, or creative writing - use anti-ai-prose
• Safe-deletion or LOC-slimming requests (removing dead/superseded code and leftover files, collapsing inert try/catch or per-element function copies) - use code-slimming

AI Self-Check

Before returning any anti-slop audit, verify:

• Rewrites compile/parse: every "after" code snippet is syntactically valid in the target language
• Security patterns not flagged: auth, CORS, input validation, rate limiting, TLS - these are correct even if verbose (check the "What NOT to Flag" list)
• Framework idioms respected: what looks like over-abstraction might be the framework's expected pattern (e.g., Next.js layouts, Django class-based views, Terraform module structure)
• Existing project conventions preserved: the repo's naming style, comment density, and abstraction level take precedence over generic "clean code" preferences
• Severity is honest: don't inflate P3 findings to P2 to pad the report
• No hallucinated replacements: verify that suggested modern alternatives actually exist in the target language version (e.g., match requires Python 3.10+, LazyLock requires Rust 1.80+)
• Test theater distinguished from correctness: implementation-mirroring tests, mock-heavy ceremony, and snapshots with no semantic assertions belong here; actual failing behavior still belongs to code-review
• Structural duplication sweep done: compare same-role modules/classes across sibling dirs (providers, targets, sources, clients, registry) and either report near-twins or note why the duplication is intentional
• API/grounding verified: suspicious helpers, flags, imports, config keys, and schema claims are checked against local types, generated schema, lockfiles, --help output, or official docs before being called hallucinations
• Cross-cutting agent hygiene applied - see references/agent-hygiene.md

---

Performance

• Focus review on changed files and shared abstractions before scanning unrelated code.
• Collapse repeated slop patterns into one finding with examples, not one finding per occurrence.
• Use cheap static checks first, then run expensive tests only where they can confirm a real risk.

---

Best Practices

• Prefer deleting unnecessary abstraction over adding a new abstraction to hide it.
• Treat duplicate code as a finding only when it creates real divergence or maintenance risk.
• Require concrete failure modes; style dislike is not slop.

Workflow

Step 1: Scope the audit

Default scope based on context:

• If invoked right after writing code in this session -> self-check (review what you just wrote)
• If there are uncommitted changes (git diff --name-only) -> recent changes
• Otherwise -> ask the user

Available scopes:

• Full codebase audit - scan everything, report by category
• Recent changes - check git diff or recent commits
• Specific files/dirs - targeted review
• Self-check - review code you just wrote in this session

Step 2: Detect languages

Scan the project to determine which languages are present. Apply the universal patterns to everything, then layer on language-specific checks. Don't apply TS checks to Python code or vice versa.

Step 3: Run mechanical linters first (if available)

Before scanning for slop, run standard linters to clear the low-hanging fruit:

• Shell: shellcheck
• Python: ruff check / mypy
• TypeScript: eslint / tsc --noEmit
• Terraform: terraform validate / tflint
• IaC schema tools: ansible-lint, helm lint, kubectl apply --dry-run=client, kubeconform when available
• Structural patterns: ast-grep (tree-sitter powered AST search - write rules to catch restating comments, dead code, hallucinated imports across languages. Install: npm i -g @ast-grep/cli. If your tool ecosystem provides ast-grep helper skills or templates, use them.)

Linters handle syntax issues, unused imports, and known anti-patterns mechanically. This skill focuses on what linters can't catch: taste, over-abstraction, naming quality, unnecessary complexity, and stale idioms. Don't duplicate what a linter already covers.

Per-project tools (eslint, tsc, vitest, prettier) are project devDeps, not system tools. If they're not in the project's package.json/devDependencies, mention to the user what's missing and let them decide whether to install. Don't run linters that aren't set up.

Step 4: Scan for patterns

Use Grep, Glob, and Read. Read files before flagging - context matters. A pattern that looks like slop in isolation might be justified.

Before finalizing, do one explicit structural pass:

• Compare same-role files across sibling directories (foo/emby.ts vs foo/jellyfin.ts, multiple registry.ts files, provider adapters, target wrappers)
• Look for near-twin modules/classes with the same method set and only renamed nouns, IDs, or client types
• If you find a repeated pattern across many files, report one representative example and mention the spread instead of silently dropping it

Classify each finding by axis (Noise/Lies/Soul - see above), action, and severity:

Action:

• Fix - clearly wrong or wasteful, should change
• Consider - judgment call, present it and let the user decide
• Fine - looks like slop but is justified (note why and move on)

Severity (determines report ordering - high first):

• P1 - strongly suggests fabricated or ungrounded code (hallucinated APIs, schema drift, silent swallowing used to hide uncertainty, fallback laundering)
• P2 - maintainability (over-abstraction, generic naming, missing error context, logic duplication)
• P3 - style (verbose patterns, comment noise, redundant annotations, barrel files)

Step 5: Report and fix

Present findings grouped by category. For each Fix-level item, show the concrete replacement. Don't just point at problems - show the better version.

---

Universal Patterns (All Languages)

1. Comment Slop (Noise)

The single biggest tell. Comments that narrate what code does instead of why.

Detect:

• Comments restating the next line ("Initialize array", "Loop through items", "Return result", "Set variable")
• Hedging ("This should work", "This might need updating", "TODO: review this")
• Overconfident assertions ("This is the most efficient approach", "This handles all edge cases")
• Section dividers adding no information (# --- Helper Functions --- above obvious helpers)
• Docstrings/JSDoc on every function when signatures are self-documenting
• Inline comments on every block in a shell script or Terraform file

AI agent tells (rarely appear in human code):

• Docstrings on every function regardless of complexity (add(a, b) -> "Returns the sum")
• Restating the next line in English on every block; section headers in short functions
• @param descriptions repeating the parameter name; comments on obvious ops (// increment counter)
• Convention blindness: camelCase in a snake_case repo, JSDoc in a no-JSDoc project

Fix: Delete obvious comments. Keep only why comments - business logic, workarounds, gotchas, non-obvious decisions. If code needs a what comment, rewrite the code. A 20-line function needs zero comments if the names are good. A 200-line module might need 3-4.

Exception: Comments explaining workarounds for bugs, API quirks, or platform limitations are valuable. Also: shell scripts benefit from more comments than typical code because the syntax is less self-documenting.

2. Defensive Overkill (Soul)

Error handling or validation that protects against impossible scenarios.

Detect:

• Try/catch (or try/except) that catches, logs, and re-raises without adding context
• Null/nil/None checks after the type system or prior logic already guarantees non-null
• Input validation deep inside internal functions (validate at boundaries, trust internally)
• Fallback values for things that can't be missing
• Shell scripts wrapping every command in if ... then ... fi instead of using set -e
• Terraform try() / can() wrapping expressions that can't fail

AI agent tells (guardrails humans would never write):

• Try/catch wrapping every function body, catching Error with a generic log message
• Null checks on values from functions you control that never return null
• Input validation on internal helpers that only receive pre-validated data
• Fallback defaults for required config that should crash loudly if missing
• if (!response.ok) after every internal call, not just HTTP boundaries

Fix: Remove pointless error handling. Validate at system boundaries (user input, API responses, env vars, external data), not on every internal call. If a catch block doesn't add context, retry, or recover - delete it.

Exception: Defensive checks on external data (network responses, deserialized JSON, user input, third-party libraries) are correct. Security patterns (auth, CORS, SSRF protection, input sanitization) should never be flagged.

3. Over-Abstraction (Soul)

Creating abstraction layers for problems that need 10 lines of direct code.

Detect:

• Wrappers that just forward to one thing with no added logic
• Abstract/base classes with a single concrete implementation
• Factory functions that always produce the same type
• Separate files for types/interfaces only used in one place
• "Service" or "Manager" classes wrapping a single operation
• Terraform modules wrapping a single resource with no added logic
• Ansible roles with one task file
• Shell functions called exactly once

Fix: Inline small abstractions. Delete wrappers that add no logic. A little repetition beats a premature abstraction.

Exception: Abstractions for testing (dependency injection), multiple implementations, or isolating external deps are fine even with one current impl.

4. Verbose Patterns (Noise)

Using 10 lines where 3 would do.

Detect (language-agnostic):

• Unnecessary intermediate variables: result = foo(); return result
• Ternaries wrapping boolean returns: return x ? true : false
• Manual iteration that the language has built-ins for
• Copy-pasting code blocks with tiny variations instead of parameterizing

Fix: Use the idiomatic short form for the language. See language-specific sections for details.

5. Generic Naming (Soul)

Names that could mean anything: data, result, response, item, temp, value, info, handler, manager, utils, helpers, common, misc.

Detect:

• Variables with generic names outside tiny scopes (2-3 line lambdas are fine)
• Files named utils.*, helpers.*, common.*, misc.* (junk drawers)
• Functions named handle_x, process_x, manage_x without domain specificity
• Terraform resources named this or main when there are multiple of the same type
• Ansible variables named item or result in complex plays

Fix: Use domain-specific names. data -> active_subscriptions. utils.sh -> split by concern or inline.

Exception: Generic names in genuinely generic code (a map() callback, a type parameter T, a Terraform module's this when it's the only resource of that type).

Module-level smells

God modules are a structural form of generic naming: the file itself has no clear identity.

Detect:

• Files named utils.*, helpers.*, common.*, misc.* with 30+ functions
• A single module mixing unrelated domains (auth + formatting + DB queries + file I/O)
• utils.py with functions spanning 5+ distinct concerns

Fix: Split by domain concern, not by arbitrary grouping. A 47-function utils.py typically contains 3-5 coherent modules (auth_utils.py, format_helpers.py, db_queries.py). Steps: (1) cluster functions by what they operate on or what domain they serve, (2) create a module per cluster, (3) update imports. Do not create a new junk-drawer module - each split module should have a name that describes its single concern.

Exception: Genuinely cross-cutting helpers (e.g., a retry() decorator used by 8 modules) can stay in a small, tightly scoped core.py or retry.py.

6. Logic Duplication (Lies)

Same logic written slightly differently in multiple places - a telltale sign of context-free generation.

Detect:

• Near-identical helper functions in different files
• Near-twin modules for adjacent integrations/providers where only names, IDs, or injected client types change
• Registry classes with the same Map + register/get/all|list/clear shape repeated across domains
• Thin wrappers/adapters that repeat the same mapping code for multiple backends
• Same validation logic in multiple handlers/routes
• Repeated inline formatting/parsing across modules
• Terraform locals blocks computing the same thing in different modules
• Ansible tasks doing the same thing with different variable names

Fix: Consolidate into a shared helper/factory/base module or keep one representative implementation and parameterize the differences. But only if truly the same - slight variations might be intentional.

Exception: Sometimes duplication is clearer than a contorted abstraction, especially when integrations are likely to diverge. Still surface it as a Consider finding if the files/classes are near-twins today.

7. Stale Patterns (Lies)

Code that was fine 5 years ago but has better alternatives now.

Fix: Use the modern equivalent. Check compatibility (language version, runtime, provider versions) before changing. See language-specific sections for concrete examples.

8. Error Handling Anti-Patterns (Noise + Lies)

Detect:

• Catch/except blocks that log a generic message and continue (error is lost)
• Every function wrapped in its own error handler (handle at boundaries instead)
• Errors caught and re-raised as new exceptions, losing the original stack
• Silent swallowing: .catch(() => {}), except: pass, 2>/dev/null on critical commands
• Shell scripts without set -e that check $? after every command manually

Fix: Handle errors at boundaries. Let errors propagate through internal code. When catching, either recover or add context and re-raise.

Exception: Fire-and-forget operations legitimately swallow errors. Comment why.

9. Cross-Language Leakage (Lies)

AI models trained on multiple languages bleed idioms across boundaries. A reliable tell for AI-generated code.

Detect:

• JavaScript patterns in Python: .push(), .length, .forEach(), ===, console.log()
• Java patterns in non-Java: .equals(), .toString(), System.out.println(), public static void
• Python patterns in JavaScript: len(), print(), elif, def , using # comments in JS
• Shell patterns in Python: backtick usage, $VARIABLE syntax
• Go patterns elsewhere: fmt.Println(), := assignment, func in non-Go

Fix: Replace with the target language's idiom. This is almost always an AI generation artifact - humans don't accidentally write .push() in Python.

10. Plausible Hallucinations / Schema Drift (Lies)

Code that looks locally plausible because the names sound right, but it is not grounded in the actual API, schema, CLI, provider, or framework version in use.

Detect:

• Functions, methods, imports, CLI flags, config keys, or resource arguments that look real but are not in local types, tool help, or docs
• Mixing adjacent ecosystems: provider arguments from the wrong Terraform resource, Helm values keys that the chart never reads, Kubernetes fields from a different API version, Ansible params from the wrong module or collection
• Compatibility blind spots: using a modern API without checking the repo's runtime/tool version
• "Fixes" that paper over missing understanding with try(), optional chaining, default fallbacks, or broad catches instead of verifying the contract

Fix: Check the actual contract first - local types, generated schema, --help, provider docs, chart values, API version docs. Delete invented surface area. Prefer loud failure for required config over fantasy defaults.

Exception: Compatibility shims for multi-version support are fine when the codebase clearly supports multiple runtimes, provider versions, or API levels.

11. Test Theater / Self-Confirming Tests (Lies + Soul)

Tests can be slop too. A green test suite is not evidence if the tests were generated from the implementation and only mirror what already exists.

Detect:

• Tests written after implementation that assert the exact control flow, fixture data, or internal call graph of the current code
• Mock-heavy tests where every dependency is stubbed and the only assertions are call counts, method names, or log messages
• Snapshots or golden files used as a substitute for semantic assertions
• "Happy path only" tests paired with broad catches, default fallbacks, or defensive code that never gets exercised
• Generated tests with high coverage but no clear link to the spec, acceptance criteria, or boundary behavior

Fix: Prefer spec-driven tests, behavior-level assertions, and a real RED phase. Keep mocks at the edges. If the test would still pass when the implementation is wrong in the same way, it is ceremony, not protection.

Exception: Adapter tests, logging/metrics assertions, and contract tests may legitimately assert call shapes when that contract is the behavior under test.

---

Language: TypeScript / JavaScript

Read references/typescript.md for the full TS/JS pattern catalog. Key highlights:

• Type abuse: redundant annotations where inference works, any instead of unknown, enums instead of const objects/unions, missing satisfies / as const
• Stale patterns: require() in ESM, var, React.FC, class components, PropTypes alongside TS, .then() chains, namespace
• Verbose: for loops that should be .filter().map(), Object.keys().forEach() instead of for...of, classes for stateless logic
• Dependency creep: node-fetch when fetch is global, uuid when crypto.randomUUID() exists, two libs for the same concern
• AI-native tells: try/catch around deterministic local code, new Promise(async ...), fallback defaults for required env/config, tests that only assert mocks or snapshots
• Barrel files: index.ts re-exporting everything in small directories

Language: Python

Read references/python.md for the full Python pattern catalog. Key highlights:

• Class-for-everything disease: stateless classes that should be plain functions/modules
• Exception anti-patterns: bare except: catching KeyboardInterrupt/SystemExit, except Exception as e: logger.error(e); raise (adds nothing), type/None checks on typed parameters (e.g., if user_id is None when the signature says int), broad try/except wrapping its own explicit raise statements
• Stale patterns: os.path instead of pathlib, .format() instead of f-strings, % formatting, if/elif chains instead of match (3.10+), typing.Optional[X] instead of X | None (3.10+)
• Type hints: Any used to bypass type errors, redundant hints on obvious assignments, overly complex TypeVar gymnastics
• Verbose: manual dict/list building instead of comprehensions, nested if instead of early returns, lambda assigned to a variable (just use def), redundant docstrings restating the function signature
• Dependency creep: requests for a single GET when urllib works, python-dotenv when os.environ is fine
• AI-native tells: dict.get(..., {}) chains laundering missing invariants, catch-log-reraise noise, mock-heavy tests with no behavioral assertion

Language: Bash / Shell

Read references/shell.md for the full Shell pattern catalog. Key highlights:

• Missing safety: no set -euo pipefail, unquoted variables, no shellcheck compliance
• Useless use of cat: cat file | grep instead of grep file
• Stale patterns: backticks instead of $(), expr instead of $(()), [ ] instead of [[ ]] in bash/zsh, parsing ls output
• Over-defensive: if command; then ... fi on every line instead of set -e, manual $? checks
• Verbose: echo "$var" | grep instead of [[ "$var" == *pattern* ]], external tools for built-in operations
• AI-native tells: hallucinated flags/subcommands copied from adjacent CLIs, 2>/dev/null || true used to hide uncertainty, heredoc-heavy automation instead of checked files/templates

Language: Infrastructure as Code

Read references/iac.md for the full IaC pattern catalog covering Terraform, Ansible, Helm, and Kubernetes manifests. Key highlights:

• Terraform: over-modularizing, redundant depends_on, not using locals, unpinned provider versions, invented resource arguments, provider/version hallucinations
• Ansible: command/shell when a module exists, ignore_errors: true everywhere, registering variables never used, not using handlers, invented module params or wrong collections
• Helm: hardcoded values in templates, tpl for static strings, .Values spaghetti without defaults, chart version not pinned, values keys that the chart never consumes
• Kubernetes: no resource requests/limits, latest tags, no namespace, imperative kubectl run/create in automation, no probes, mismatched apiVersion/field combinations

Language: Rust

Read references/rust.md for the Rust pattern catalog. Key highlights:

• Clone abuse: .clone() to dodge the borrow checker - the #1 AI-Rust tell
• Error type proliferation: custom error enums for every module instead of anyhow/thiserror
• Overly generic trait bounds: T: Display + Debug + Clone + Send + Sync when only Display is used
• Verbose: match with two arms instead of if let, explicit return at function end, manual Option/Result matching instead of combinators
• Unsafe overuse: unsafe blocks without // SAFETY: comments, transmute when as works
• Stale: extern crate, #[macro_use], try!(), lazy_static instead of std::sync::LazyLock (1.80+)

Language: Docker / Containers

Read references/docker.md for Dockerfile and Compose pattern catalog. Key highlights:

• Fat images: no multi-stage build, build tools in final stage, COPY . . before dep install (cache busting)
• Layer waste: separate RUN per package, ADD for local files, RUN cd instead of WORKDIR
• Security: running as root, chmod 777, secrets in build args/env, missing .dockerignore
• Compose bloat: container_name everywhere, restart: always without healthchecks, depends_on without conditions, hardcoded ports
• Stale: old base image versions, MAINTAINER directive, ENTRYPOINT+CMD confusion

Other Languages

For Go and other languages without dedicated reference files: apply the universal patterns (sections 1-11) only. Note in the report that language-specific checks were skipped. Common cross-language tells still apply - over-abstraction, comment noise, cross-language leakage, schema drift, and error handling anti-patterns look similar everywhere.

Research & Citations

Read references/research-sources.md for statistics, source citations, and deeper context on the "no soul" problem. Use when the user wants data to back up findings.

---

What NOT to Flag

These look like slop but aren't:

• Security patterns: auth, CORS, SSRF protection, input validation at boundaries, rate limiting, TLS configuration, RBAC policies. Correct even if "defensive".
• Explicit types/annotations on public interfaces: function signatures, exported types, API contracts benefit from explicitness.
• Abstractions that enable testing: interfaces with one implementation for dependency injection.
• Workaround comments: "works around X bug", "API requires this", "platform-specific" - institutional knowledge.
• Defensive checks on external data: network responses, user input, deserialized data, third-party library output.
• Shell verbosity for clarity: complex pipelines benefit from intermediate variables and comments.
• Terraform count/for_each on single resources: might be conditional (count = var.enabled ? 1 : 0).
• Ansible when conditions that seem obvious: often guarding against cross-platform differences.
• Compatibility shims: version- or provider-specific branches may look repetitive because they support multiple real targets.
• Contract-level tests: asserting exact API payloads, SQL, CLI args, or emitted metrics is fine when that contract is the thing being tested.

---

Output Contract

See references/output-contract.md for the full contract.

• Skill name: ANTI-SLOP
• Deliverable bucket: audits
• Mode: always-on. Every invocation emits the full contract - boxed inline header, body summary inline plus per-finding detail in the deliverable file, boxed conclusion, conclusion table.
• Deliverable path: docs/local/audits/anti-slop/<YYYY-MM-DD>-<slug>.md
• Severity scale: reduced subset of the shared scale - P1 | P2 | P3 with slop-specific meanings (P1 fabricated/ungrounded, P2 maintainability, P3 style; see the Workflow classification). No P0: slop is never a build or security breaker - route those to code-review / security-audit.

Related Skills

• code-review - finds bugs and correctness issues. Anti-slop finds quality and style issues. If it would cause incorrect behavior, it's a code-review finding. If it's ugly but correct, it's anti-slop.
• security-audit - finds vulnerabilities. Defensive code that looks like "overkill" may be correct security practice. Check the "What NOT to Flag" list before flagging security patterns.
• full-review - orchestrates code-review, anti-slop, security-audit, and update-docs in parallel. Anti-slop is one of the four passes.
• update-docs - handles documentation maintenance. Anti-slop focuses on code quality; update-docs focuses on keeping docs accurate and trimmed.
• anti-ai-prose - audits prose for AI writing tells (vocabulary, syntax, tone, formatting). Anti-slop audits code. Together they cover "does this repo read as machine-generated" across both code and documentation.
• code-slimming - handles safe-deletion and LOC-slimming passes (dead/superseded code, leftover files, inert try/catch, per-element function copies). Use it when the goal is reducing volume; use anti-slop when the goal is quality. AI-bloat shapes overlap: code-slimming proposes the behavior-preserving collapse; anti-slop judges them as style/quality smells.

---

Reference Files

• references/typescript.md - TypeScript and JavaScript anti-slop patterns
• references/python.md - Python anti-slop patterns
• references/shell.md - shell and script anti-slop patterns
• references/iac.md - Terraform, Ansible, Helm, and Kubernetes anti-slop patterns
• references/rust.md - Rust anti-slop patterns
• references/docker.md - Dockerfile and Compose anti-slop patterns
• references/research-sources.md - supporting research, citations, and external context

---

Output Format

## Anti-Slop Audit: [scope]

### Findings

#### [Category Name] ([count] items)

**[action]** ([severity], [axis]) `path/to/file:line` - [description]
```[language]
// before
[code snippet]

// after
[improved code snippet]
```

### Summary
- X findings across Y files
- [top-level observations about codebase health]

Keep it concise. Show the diff, not a paragraph explaining it.

---

Rules

• Keep correctness out of scope. If it would actually break behavior, route it to code-review instead of padding this report.
• Flag AI-native lies even when they look polished. Hallucinated APIs, schema drift, and self-confirming tests belong here when the problem is lack of grounding or taste rather than a demonstrated failing behavior.
• Keep security out of scope. Defensive code often looks verbose on purpose. Do not flag it casually.
• Read before judging. A pattern that looks generic in isolation may be justified by framework or project constraints.
• Ground hallucination claims. Use local types, schema, lockfiles, generated docs, or tool help before saying a flag/resource/API is fake.
• Do not bury structural duplication. If near-twin modules or repeated registry/wrapper shapes appear, surface at least one representative finding even when higher-severity hallucination findings dominate the report.
• Prefer concrete rewrites. If you flag a pattern, show the simpler version.
• Run the AI Self-Check. Verify findings against the checklist before returning the audit.