AB stack-builder
Design, audit, and evolve a personalized AI skill stack ("your own gstack") for any user. Observes the user's real context and interviews for gaps, identifies both signature strengths worth replicating and workflow breakdowns worth compensating, recruits existing skills, then designs a complementary role portfolio that mirrors, operates, challenges, verifies, and amplifies the user — and generates the new roles as installable SKILL.md files. Triggers on "build my stack", "create my own gstack", "personal AI stack", "design my skill stack", "audit my stack", "帮我建我自己的stack", "打造我的专属skill组合", "复制我的能力做成AI团队", "定制我的AI团队", or when a user shares gstack/E-Stack-style projects and asks for their own version.
Design, audit, and evolve a personalized AI skill stack ("your own gstack") for any user.
As a process B 65/100 · Nearly there — 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 · 0
✓ No critical or high findings
Files scanned: 2. 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 65/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 9 mutating operations with no state check
- 65Failures and branches. 3 branches
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3489 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 top-level sections: this looks like several domains in one skill
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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +5Description quotes 10 example trigger phrases
- +3Description length 708: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 20 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.