AB ai-tool-research
Researches how people are using an AI tool (Claude Desktop, Cursor, OpenAI Codex, Google Gemini, or OpenClaw) and generates a Productivity Playbook plus a Skills Catalog in a consistent, rated, month-over-month format. Use when the user asks for a monthly research update on one of these tools, a productivity playbook, a skills catalog, a "what's new this month" report on an AI coding/agent tool, or wants to regenerate any of the files named `<Tool>-Productivity-Playbook.md` or `<Tool>-Skills-Catalog.md`. Also use if the user wants to run the same research cycle across all five tools.
As a process B 72/100 · Nearly there — weak spots: 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: 8. 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
- 30Running it twice. 20 mutating operations with no state check
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Failures and branches. 10 branches
- 85Steps. 112 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3835 tokens
- 100Progress reporting. Reports progress
- low 18 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (22 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)
- -234 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 590: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 112 items
- +3Output format is stated explicitly
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.