AC second-brain-growth
Evaluate the user's second-brain/第二大脑 effective knowledge growth speed and second-brain health, with Hbrain defaults plus Codex interactive use, Hermes cron/reporting, and OpenClaw multi-agent handoff compatibility. Use when the user asks about 知识增长速度, 第二大脑增长, Hbrain growth, knowledge compounding, recall rate, connection density, transformation rate, weekly/monthly second-brain scorecards, OpenClaw/Hermes second-brain automation, or whether notes are becoming usable 人脑 judgment/action/output rather than merely accumulating files.
Evaluate the user's second-brain/第二大脑 effective knowledge growth speed and second-brain health, with Hbrain defaults plus Codex interactive use, Hermes…
As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions
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: 0. 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 60/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 76 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2520 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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)
- +3Output format is not stated: the model decides each time
- -4Absolute local paths (C:\Users, /home/…): not portable
- +1No license
- +2Single-language instructions
- +3Description length 535: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 76 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.