SKILLEMALL.ai
GitHub ↗ RU Sign in

BA fleece

Credit card research and redemption CLI. Looks up rewards rates, annual fees, welcome bonuses, statement credits, transfer partners, point valuations, application rules, lounge access, and travel protections for Chase, Amex, Citi, Capital One, Bilt, and all major US issuers. Compare cards, analyze wallet gaps, estimate ROI, get recommendations, look up merchant category codes, and search award flights and hotels. Install with pip install fleece-cli.

ClawHub Agent Skills author: Yuan Chen v1.6.1 MIT-0 80 files · 2 scripts body ≈ 2 070 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process A 83/100 · Runs to the end — weak spots: running it twice, progress reporting

IntegrationSoftware developmentData and analyticstype 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%
99
Quality 40%
72
Run on models
none yet
Process rating
A
83/100
Runs to the end
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
60
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token docs/index.html:13
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    <meta name="google-site-verification" content="9DkA…rGA" />
    quoted

Files scanned: 49. 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 83/100

  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 1 mutating operations with no state check
  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 14 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2070 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)
  • -44 reference files, but SKILL.md never points to them: the model will not open them
  • +2Single-language instructions
  • +3Description length 453: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 14 items
  • +3Output format is stated explicitly
  • +4Has examples (20 code blocks)
  • +1License stated

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

External checks

ClawHub: suspicious
The core CLI is mostly coherent, but the published artifact also contains broader iOS, wallet, location, Gmail, and safety-filter-bypass behavior that users should review before installing or building.
LLM: suspicious (high) · VirusTotal: · 4 Aug 2026