Review Llm Artifacts logo

Review Llm Artifacts

Organization
existential-birds
review-llm-artifacts

Detects common LLM coding agent artifacts across four categories (tests, dead code, abstraction, style) over the project or changed files — using parallel subagents when the agent supports them, otherwise four sequential passes. Scans files changed since main by default; use --all for full-project scan. Triggers on LLM cruft cleanup, agent-generated code review, dead code sweeps, test-quality passes, or when the user asks to scan the whole repo.

Overview

Publisherexistential-birds
Repositorybeagle
Skill namereview-llm-artifacts
Stars
82
Forks
8
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by existential-birds on GitHub. Read the source before you install it.

Installation

Install the Review Llm Artifacts AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/existential-birds/beagle.git /tmp/beagle
mkdir -p .claude/skills
cp -r /tmp/beagle/plugins/beagle-core/skills/review-llm-artifacts .claude/skills/review-llm-artifacts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Llm Artifacts in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Review Llm Artifacts on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Review Llm Artifacts is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

LLM Artifacts Review

Detect common artifacts left behind by LLM coding agents: over-abstraction, dead code, DRY violations in tests, verbose comments, and defensive overkill.

Hard gates (sequence)

Advance only when each pass condition is objectively true (prevents “review complete” without artifacts):

GatePass condition
G1 — ScopeFile list is non-empty or you exit with exactly the Step 1 message; scope is set to all or changed.
G2 — Four categoriesTests, dead code, abstraction, and style are each reviewed (four parallel subagent runs when supported, or four sequential passes covering the same categories). Stop if any category did not complete; do not write JSON or a summary that implies a full pass.
G3 — JSON before summary.beagle/llm-artifacts-review.json exists and is valid JSON before Step 6 markdown.
G4 — IntegrityStep 7 checks pass before treating the run as complete.

Arguments

Parse $ARGUMENTS for flags and optional path:

FlagEffect
(default)Changed-files scope — only files changed since git merge-base HEAD main (PR-style scope)
--allFull project scan — all matching source files under the target path
--parallelForce parallel execution where subagents are supported (default when 4+ files in scope)
PathRoot directory to scan (default: current working directory)

Step 1: Determine Scope

A. Changed files only (default):

Resolve the base ref explicitly and fail loudly if none exists — do not wrap the git merge-base call in || true, which would silently swallow a missing main/master ref and report "no files to scan" on repos that only have origin/main or use master. If no base ref is found, suggest the user pass --all instead of silently falling back.

bash
BASE=$(for ref in main origin/main master origin/master; do
         git rev-parse --verify "$ref" >/dev/null 2>&1 && { echo "$ref"; break; }
       done)
if [ -z "$BASE" ]; then
  echo "error: no main/master ref found (checked main, origin/main, master, origin/master). Pass --all for a full-project scan." >&2
  exit 1
fi
MERGE_BASE=$(git merge-base HEAD "$BASE") || {
  echo "error: git merge-base HEAD $BASE failed." >&2
  exit 1
}
git diff --name-only "$MERGE_BASE..HEAD" | grep -E '\.(py|ts|tsx|js|jsx|go|rs|java|rb|swift|kt)$' || true

(The trailing || true on the grep is intentional — zero source-file matches is a legitimate empty-scope result, distinct from a failed base-ref resolution.)

B. Full project (--all):

From TARGET (default .), list source files and prune excluded dependency/build trees so find never descends into them. ! -path "*/foo/*" only filters the output; find still walks the tree (minutes of wasted I/O on large node_modules, target, etc.). Use -prune instead:

bash
find "$TARGET" \
  \( -type d \( \
       -name node_modules -o -name .git -o -name vendor -o -name __pycache__ \
    -o -name .venv        -o -name venv -o -name dist   -o -name build \
    -o -name target       -o -name .next -o -name coverage -o -name .turbo \
  \) -prune \) -o \
  \( -type f \( \
       -name "*.py" -o -name "*.ts" -o -name "*.tsx" -o -name "*.js" -o -name "*.jsx" \
    -o -name "*.go" -o -name "*.rs" -o -name "*.java" -o -name "*.rb" \
    -o -name "*.swift" -o -name "*.kt" \
  \) -print \)

Large repos: The --all path can produce huge file lists. If file count exceeds 400, warn and suggest narrowing: pass a subdirectory as TARGET, or drop --all to fall back to the default changed-files scope. Still proceed unless the user explicitly cancels. (This warning does not fire on the default changed-files scope, which is already bounded by the PR diff.)

If no files are found, exit with:

No files to scan. Check the path, branch, or pass --all for a full-project scan.

Set scope in the report: "all" for --all, "changed" for the default changed-files scope.

Step 2: Detect Languages

Extract unique file extensions from the file list:

bash
echo "$FILES" | sed 's/.*\.//' | sort -u

Map extensions to language names for the report:

  • .py -> Python
  • .ts, .tsx -> TypeScript
  • .js, .jsx -> JavaScript
  • .go -> Go
  • .rs -> Rust
  • .java -> Java
  • .rb -> Ruby
  • .swift -> Swift
  • .kt -> Kotlin

Step 3: Review the Four Categories

Cover all four categories below. If the agent supports subagents and file count >= 4 (or --parallel is set), dispatch one subagent per category in parallel. Otherwise, run the four category reviews sequentially yourself, producing the same findings. Either way:

  1. Load the llm-artifacts-detection skill
  2. Review each category (one per subagent when parallel, one pass at a time when sequential)
  3. Collect findings in the structured format below

Category 1: Tests

Focus: Testing anti-patterns from LLM generation

  • DRY violations (repeated setup code, duplicate assertions)
  • Testing library/framework code instead of application logic
  • Wrong mock boundaries (mocking too much or too little)
  • Overly verbose test names that describe implementation
  • Tests that just mirror the implementation

Category 2: Dead Code

Focus: Unused or obsolete code

  • Unused imports, variables, functions, classes
  • TODO/FIXME comments that should have been resolved
  • Backwards compatibility code for removed features
  • Orphaned test files for deleted code
  • Commented-out code blocks
  • Feature flags that are always on/off

Category 3: Abstraction

Focus: Over-engineering patterns

  • Unnecessary abstraction layers (interfaces for single implementations)
  • Copy-paste drift (similar code that diverged slightly)
  • Over-configuration (configurable things that never change)
  • Premature generalization
  • Factory/Builder patterns for simple object creation
  • Deep inheritance hierarchies

Category 4: Style

Focus: Verbose or defensive patterns

  • Verbose comments explaining obvious code
  • Defensive overkill (null checks on non-nullable values)
  • Unnecessary type hints (dynamic languages with obvious types)
  • Overly explicit error messages
  • Redundant logging
  • Self-documenting code with documentation

Step 4: Consolidate Findings

Prerequisite: G2 satisfied (all four category reviews finished successfully).

Once all four category reviews have completed (parallel subagents or sequential passes), then:

  1. Merge all findings into a single list
  2. Assign unique IDs (1, 2, 3...)
  3. Group by category for display

Echo before write (anti-confabulation): Every finding written to JSON MUST come from a category review's [FILE:LINE] ISSUE_TITLE output, not from the branch name, directory, or your own inference. After assigning ids, echo the consolidated table — id | category | file:line | description — and confirm each row traces to a specific category result. Do not add findings that no category review reported.

ID lock: Ids are contiguous 1..N with no gaps or duplicates. This 1..N set is the locked id set that downstream skills (verify-llm-artifacts, fix-llm-artifacts) bind to 1:1. summary.total MUST equal N, and summary.by_category counts MUST sum to N. State the id set before writing JSON.

Step 5: Write JSON Report

Create .beagle directory if it doesn't exist:

bash
mkdir -p .beagle

Write findings to .beagle/llm-artifacts-review.json:

json
{
  "version": "1.0.0",
  "created_at": "2024-01-15T10:30:00Z",
  "git_head": "abc1234",
  "scope": "all" | "changed",
  "target": ".",
  "files_scanned": 42,
  "languages": ["Python", "TypeScript", "Go"],
  "findings": [
    {
      "id": 1,
      "category": "tests" | "dead_code" | "abstraction" | "style",
      "type": "dry_violation" | "unused_import" | "over_abstraction" | "verbose_comment" | "...",
      "file": "src/utils/helper.py",
      "line": 42,
      "description": "Repeated setup code in 5 test functions",
      "suggestion": "Extract to a pytest fixture",
      "risk": "Low" | "Medium" | "High",
      "fix_safety": "Safe" | "Needs review",
      "fix_action": "refactor" | "delete" | "simplify" | "extract"
    }
  ],
  "summary": {
    "total": 15,
    "by_category": {
      "tests": 4,
      "dead_code": 5,
      "abstraction": 3,
      "style": 3
    },
    "by_risk": {
      "High": 2,
      "Medium": 8,
      "Low": 5
    },
    "by_fix_safety": {
      "Safe": 10,
      "Needs review": 5
    }
  }
}

Step 6: Display Summary

Prerequisite: G3 satisfied (JSON on disk and parseable).

markdown
## LLM Artifacts Review

**Scope:** Changed files since merge-base with main | Entire project under `<path>` (when `--all`)
**Files scanned:** 42
**Languages:** Python, TypeScript, Go

### Findings by Category
...
### Summary Table
...
### Next Steps

- Run the [verify-llm-artifacts](../verify-llm-artifacts/SKILL.md) skill to confirm findings and drop false positives before fixing.
- Run the [fix-llm-artifacts](../fix-llm-artifacts/SKILL.md) skill after verification (or to preview safe-only fixes).
- Review the JSON report at `.beagle/llm-artifacts-review.json`

Step 7: Verification (report integrity)

Before completing, verify the review executed correctly:

  1. JSON validity: Confirm .beagle/llm-artifacts-review.json exists and is parseable
  2. Category coverage: All 4 category reviews completed without errors (parallel subagents or sequential passes)
  3. Git HEAD captured: The git_head field is non-empty in the report
  4. Staleness check: If a previous report exists, compare stored git_head to current HEAD and warn if different
  5. ID + count integrity: Finding ids are contiguous 1..N; summary.total == N; summary.by_category sums to N. A mismatch means a finding was added, dropped, or duplicated — fix before completing.
bash
python3 -c "import json; json.load(open('.beagle/llm-artifacts-review.json'))" 2>/dev/null && echo "✓ Valid JSON" || echo "✗ Invalid JSON"

python3 - <<'PY'
import json
r = json.load(open('.beagle/llm-artifacts-review.json'))
ids = [x['id'] for x in r['findings']]
n = len(ids)
ok = ids == list(range(1, n + 1)) and r['summary']['total'] == n \
     and sum(r['summary']['by_category'].values()) == n
print("✓ ids 1..N and counts consistent" if ok else f"✗ id/count mismatch: ids={ids} total={r['summary']['total']}")
PY

STORED_HEAD=$(jq -r '.git_head' .beagle/llm-artifacts-review.json 2>/dev/null)
CURRENT_HEAD=$(git rev-parse --short HEAD)
if [ "$STORED_HEAD" != "$CURRENT_HEAD" ]; then
  echo "⚠️ Report was generated on $STORED_HEAD, current HEAD is $CURRENT_HEAD"
fi

If any verification fails, report the error and do not proceed.

Finding-level verification (precision, not JSON syntax) is a separate skill: verify-llm-artifacts — run it before mass deletes or --fix on risky items.

Output Format for Each Finding

text
[FILE:LINE] **ISSUE_TYPE** (Risk, Fix Safety)
- Description
- Suggestion: Specific fix recommendation

Rules

  • Follow Hard gates order; do not skip G3 (JSON before Step 6).
  • Anti-confabulation: every finding must trace to a category review's [FILE:LINE] output (Step 4 echo); never invent findings from the branch name, directory, or inference. See the review-verification-protocol skill → Anti-confabulation (gate 0).
  • Always load the llm-artifacts-detection skill first
  • Use parallel subagents (when the agent supports them) for the four category reviews when >= 4 files; otherwise run them sequentially
  • Every finding MUST have file:line reference
  • Categorize risk honestly (don't inflate or deflate)
  • Mark fix safety as "Safe" only if change is mechanical and reversible
  • Create .beagle directory if needed
  • Write JSON report before displaying summary
  • Default scope is changed files since merge-base with main; pass --all for a full-project scan

Frequently asked questions

What does the Review Llm Artifacts AI skill do?

Detects common LLM coding agent artifacts across four categories (tests, dead code, abstraction, style) over the project or changed files — using parallel subagents when the agent supports them, otherwise four sequential passes. Scans files changed since main by default; use --all for full-project scan. Triggers on LLM cruft cleanup, agent-generated code review, dead code sweeps, test-quality passes, or when the user asks to scan the whole repo.

Why use Review Llm Artifacts on TypingMind?

Because you install it once and use it with any model. Review Llm Artifacts is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Review Llm Artifacts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/existential-birds/beagle/tree/main/plugins/beagle-core/skills/review-llm-artifacts. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Review Llm Artifacts?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Review Llm Artifacts?

As many as you like. As long as a model supports skills, you can use Review Llm Artifacts with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Review Llm Artifacts AI skill free?

Yes. It is published on GitHub by existential-birds under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇