AC xiaoyaoclaw-usage-report
OpenClaw usage and performance reporting. Parse session JSONL to answer how long each agent task took, which tools/skills/models were used, and how many tokens were consumed — zero dependency, local only, no cost dimension (token is the primary metric). Read-only: never modifies any file, aggregates statistics only, never leaks conversation content. Use when the user asks about token usage, task duration, slowest tools, skill usage, or per-agent consumption (今天花了多少 token/哪个工具最慢/ 任务耗时/用量报告), or scheduled via cron. 中文：OpenClaw 用量与性能查询。 解析 session JSONL，回答每次 agent 任务耗时、所用工具/技能/模型、token 消耗。零依赖纯本地，不提供成本维度（token 为主指标）。只读：不修改任何 文件，只输出聚合统计，不泄露会话内容。用户问 token 用量、任务耗时、 最慢工具、技能使用、按 agent 消耗时使用。cron 每日日报为可选项，用户 自行设置。
OpenClaw usage and performance reporting.
As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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: 9. 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 52/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 31 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1110 tokens
- 100Running it twice. No mutating operations
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
- -212 emoji in the instructions: noise for the model
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +2Single-language instructions
- +3Description length 713: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 31 items
- +4Has examples (3 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.