AC lifeos
Read, query and edit a LifeOS / Obsidian PARA vault (notes, tasks, periodic notes, theme notes, tags, AI Wiki) from the command line via the `lifeos` CLI — headless, no Obsidian or Aino needed. Use when the user asks about their tasks or 待办; periodic notes — daily/weekly/monthly/quarterly/yearly review or 日记/周记/月记/季记/年记; theme notes / PARA — projects/areas/resources/archives or 项目/领域/资源/归档/主题; tags / theme tags or 标签/主题标签; the LifeOS AI Wiki / `.AI.md` topic synthesis pages or 整理主题 / 更新 AI Wiki; or wants to find/search notes, capture a thought, or check/update what's on their plate in their LifeOS vault.
Read, query and edit a LifeOS / Obsidian PARA vault (notes, tasks, periodic notes, theme notes, tags, AI Wiki) from the command line via the lifeos CLI —…
As a process C 62/100 · 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: 6. 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 62/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 60Tools and files. Uses tools (bash, web, git) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 85Steps. 32 steps, 2 vague phrases
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3064 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low 14 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (8 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)
- +3Output format is not stated: the model decides each time
- -5TODO / placeholder text left in the skill
- +1No license
- +2Single-language instructions
- +3Description length 611: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 32 items
- +4Has examples (8 code blocks)
- +4Reference files are cited in the instructions (2 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.