SKILLEMALL.ai

BF peer-review

Multi-model peer review layer using local LLMs via Ollama to catch errors in cloud model output. Fan-out critiques to 2-3 local models, aggregate flags, synthesize consensus. Use when: validating trade analyses, reviewing agent output quality, testing local model accuracy, checking any high-stakes Claude output before publishing or acting on it. Don't use when: simple fact-checking (just search the web), tasks that don't benefit from multi-model consensus, time-critical decisions where 60s latency is unacceptable, reviewing trivial or low-stakes content. Negative examples: - "Check if this date is correct" → No. Just web search it. - "Review my grocery list" → No. Not worth multi-model inference. - "I need this answer in 5 seconds" → No. Peer review adds 30-60s latency. Edge cases: - Short text (<50 words) → Models may not find meaningful issues. Consider skipping. - Highly technical domain → Local models may lack domain knowledge. Weight flags lower. - Creative writing → Factual review doesn't apply well. Use only for logical consistency.

ClawHub Agent Skills author: staybased v1.0.0 2 files body ≈ 966 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process F 49/100 · Will not run — References files that are not bundled: scripts/peer-review.sh, scripts/peer-review-batch.sh, scripts/seed-test-corpus.sh

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
81/100
safety, quality, tests
Safety 60%
100
Quality 40%
53
Run on models
none yet
Process rating
F
49/100
Will not run
References files that are not bundled: scripts/peer-review.sh, scripts/peer-review-batch.sh, scripts/seed-test-corpus.sh
Tools and files w 18
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Shorten the description to 1024 characters.
  2. 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

  • error description-long description is 1060 chars, limit 1024
  • warning missing-ref reference to a missing file: scripts/peer-review.sh
  • warning missing-ref reference to a missing file: scripts/peer-review-batch.sh
  • warning missing-ref reference to a missing file: scripts/seed-test-corpus.sh

Process rating: all ten parameters 49/100

Will not run. References files that are not bundled: scripts/peer-review.sh, scripts/peer-review-batch.sh, scripts/seed-test-corpus.sh
  • 0Tools and files. 3 referenced file(s) missing: scripts/peer-review.sh, scripts/peer-review-batch.sh, scripts/seed-test-corpus.sh
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 3 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 15 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 966 tokens
  • 100Progress reporting. Reports progress

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

  • +3Description length 1059: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +4Description says when NOT to use the skill
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 15 items
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.

External checks

ClawHub: suspicious
The skill’s peer-review purpose is plausible, but it relies on unbundled shell scripts and encourages external sharing and logging of review content without clear safeguards.
LLM: suspicious (medium) · VirusTotal: suspicious · 28 May 2026