AC triskill
Use this skill whenever the agent needs to (1) verify a factual claim against live sources before stating it with confidence, (2) recover from a failed shell command or code execution by diagnosing the error and proposing a bounded, human-approved fix, or (3) read/write small pieces of state that must be shared safely across multiple agent sessions or sub-agents working on the same task. Trigger this skill for fact-checking requests, "it failed, fix it" debugging loops, or any multi-agent workflow where two or more agents need to coordinate through shared variables without overwriting each other's work.
Use this skill whenever the agent needs to (1) verify a factual claim against live sources before stating it with confidence, (2) recover from a failed shell…
As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- 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
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low Risky intent
intent-offensive-securitySKILL.md:61Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- Never runs as root and does not attempt privilege escalation.
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
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
- 30Running it twice. 3 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 28 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1372 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (15 tags): a typed call is more reliable
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
- +4No input/output examples
- +2Single-language instructions
- +3Description length 610: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 28 items
- +3All 3 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.