AC defamation-response
Respond to false, damaging statements about you or your business — decide what actually counts as defamation, preserve evidence, and choose the right response from correction to takedown to legal action. Use when asked someone posted lies about me, respond to a false review/statement, is this defamation, or protect my reputation online. Produces a read on whether it likely crosses from opinion into actionable falsehood, evidence-preservation steps, a tiered response (platform report, correction/retraction request, cease-and-desist, legal), and a caution against reactions that make it worse. Not legal advice.
Respond to false, damaging statements about you or your business — decide what actually counts as defamation, preserve evidence, and choose the right response…
As a process C 64/100 · Has gaps — weak spots: when it triggers, failures and branches, 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 · 0
✓ No critical or high findings
Files scanned: 1. 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 64/100
- 0Failures and branches. Linear process with no failure handling
- 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
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 36 steps, 2 vague phrases
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1072 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)
- +1No license
- +2Single-language instructions
- +3Description length 615: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 36 items
- +3Output format is stated explicitly
- +4Has examples (0 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.