AB bounded-researcher
Bounded evidence-first research workflow for software agents. Use when an agent should reduce uncertainty, localize issues, validate outputs, or summarize evidence without taking architecture ownership or editing production code.
As a process B 72/100 · Nearly there — weak spots: inputs and preconditions, progress reporting
ProcedureAI and agentsWriting and documentsInfrastructuretype and topics are labelled automatically from the skill text
How to improve
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: 4. 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 72/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 50When it triggers. No condition that starts the skill
- 60Result and completion. Output format stated, no completion criterion
- 70Failures and branches. 5 branches
- 100Tools and files. No external tools needed
- 100Steps. 59 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 630 tokens
- 100Running it twice. No mutating operations
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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 229: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 59 items
- +3Output format is stated explicitly
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.
External checks
ClawHub: clean
This is a narrow instruction-only research skill that asks agents to gather limited evidence and report bounded next steps, with no executable code or hidden high-risk behavior found.
LLM: benign (high) · VirusTotal: · 29 May 2026