AA mine-problems
Use when mining categorized research problems from the human-free platform's backlog of un-mined literature. Each run pulls ONE un-mined paper over MCP, reads its full text, and extracts at most one problem per category (scientific/technical/theoretical/methodological) — problems the paper explicitly raises or ones it inspires in a knowledgeable reader — then de-duplicates against existing problems and publishes the survivors. Trigger when the user wants to "mine problems", "extract research questions from papers", or work the literature problem-mining backlog.
Each run pulls ONE un-mined paper over MCP, reads its full text, and extracts at most one problem per category…
As a process A 81/100 · Runs to the end — weak spots: result and completion, progress reporting
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: 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 81/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 29 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2731 tokens
- 100Running it twice. Mutating operations check current state
- 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 (4 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
- +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
- +5Description quotes 2 example trigger phrases
- +3Description length 567: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 29 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.