AC kibibot
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.
As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
The same skill appears in 1 more place: ClawHub
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
body-longSKILL.md body ≈ 6173 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 54/100
- 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 (kibi)
- 70Execution cost. Instruction body is 6173 tokens
- 100Tools and files. No external tools needed
- 100Steps. 49 steps
- 100Failures and branches. 2 branches, has a failure section
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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 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: 74.