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BC free-scaling

$0 test-time scaling with online learning. Classify, generate, and verify using free model ensembles. Models self-select via ELO scoring + A/B testing from deployment data. 13 NIM models + optional Copilot backend.

ClawHub Agent Skills author: isotrivial v3.3.1 MIT-0 19 files body ≈ 1 632 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

AnalyzerGitHubInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
98
Quality 40%
72
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Dangerous commands cmd-eval-dynamic nim_ensemble/benchmark.py:193
    Dynamic code execution from decoded/untrusted input (quoted — discussed, not commanded)
    eval(input("Enter command: "))
    quoted
  • low Dangerous commands cmd-eval-dynamic README.md:48
    Dynamic code execution from decoded/untrusted input (quoted — discussed, not commanded)
    python3 -m nim_ensemble.cli scale "Is eval(input()) safe?" -k 3 --answers "SAFE,VULNERABLE"
    quoted

Files scanned: 18. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 56/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 24 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1632 tokens

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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 214: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 24 items
  • +4Has examples (9 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
This skill largely does what it claims, but it can reuse local OpenClaw/GitHub credentials for Copilot and stores inference history, so users should review it carefully before installing.
LLM: suspicious (high) · VirusTotal: · 29 May 2026