SKILLEMALL.ai

BF 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.

ClawHub Claude Code author: Kevin Anderson v1.0.0 MIT-0 2 files body ≈ 3 055 tokens Open the sourceclawhub.ai analyzed 26 h ago

Detects common LLM coding agent artifacts across four categories (tests, dead code, abstraction, style) over the project or changed files — using parallel…

As a process F 65/100 · Will not run — References files that are not bundled: ../llm-artifacts-detection/SKILL.md, ../verify-llm-artifacts/SKILL.md, ../fix-llm-artifacts/SKILL.md

AnalyzerAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
F
65/100
Will not run
References files that are not bundled: ../llm-artifacts-detection/SKILL.md, ../verify-llm-artifacts/SKILL.md, ../fix-llm-artifacts/SKILL.md
Tools and files w 18
0
Progress reporting w 2
0
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: ../llm-artifacts-detection/SKILL.md
  • warning missing-ref reference to a missing file: ../verify-llm-artifacts/SKILL.md
  • warning missing-ref reference to a missing file: ../fix-llm-artifacts/SKILL.md
  • warning missing-ref reference to a missing file: ../review-verification-protocol/SKILL.md
  • note edit-residue the text marks something as outdated (lines 116): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 65/100

Will not run. References files that are not bundled: ../llm-artifacts-detection/SKILL.md, ../verify-llm-artifacts/SKILL.md, ../fix-llm-artifacts/SKILL.md
  • 0Tools and files. 4 referenced file(s) missing: ../llm-artifacts-detection/SKILL.md, ../verify-llm-artifacts/SKILL.md, ../fix-llm-artifacts/SKILL.md
  • 0Progress reporting. Says nothing while it works
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 53 steps, 2 vague phrases
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3055 tokens
  • 100Running it twice. Mutating operations check current state
  • low 11 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 449: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 53 items
  • +3Output format is stated explicitly
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.

External checks

ClawHub: clean
This skill reviews code for common AI-generated cleanup issues and only writes a local report, with no hidden or high-risk behavior found.
LLM: benign (high) · VirusTotal: · 1 Jun 2026