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Create tokens on-chain, check fee earnings, check Kibi Credit balance, trigger agent credit reload, and interact with KibiBot's Agent API and Kibi LLM Gateway. Use when asked to create a token via KibiBot, check fee earnings across chains and platforms, check KibiBot Kibi Credit balance, check daily token creation quota, reload credits from trading wallet, or make LLM calls through KibiBot's gateway.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 3 files body ≈ 6 173 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Create tokens on-chain, check fee earnings, check Kibi Credit balance, trigger agent credit reload, and interact with KibiBot's Agent API and Kibi LLM Gateway.

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorAI and agentstype 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%
100
Quality 40%
70
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: kibibot (LeoYeAI/openclaw-master-skills)

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6173 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 49/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 15 mutating operations with no state check
  • 40Consistency. Frontmatter name (kibibot) differs from the folder (kibibot-skill)
  • 50Failures and branches. 0 branches, has a failure section
  • 70Execution cost. Instruction body is 6173 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 49 steps

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
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 403: enough signal without eating the budget
  • +4Structure: 39 headings
  • +3Step-by-step instructions: 49 items
  • +4Has examples (23 code blocks)

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