BC aios-self-improving-agent
在 AIOS/OpenClaw 运行环境中记录当前 agent 的错误、纠正、经验、知识缺口和可复用改进。适用于命令失败、用户纠正回答、发现过时知识、外部工具/API 异常、同类问题反复出现、完成复杂任务后需要沉淀经验、或开始重要任务前需要回顾当前 agent workspace 内历史 learnings 的场景。若环境中有 QMD，优先使用 per-workspace QMD 索引检索和去重。该技能必须保持 per-agent 逻辑隔离，只在当前 agent workspace 内读写 `.learnings/`，不得默认写全局 workspace、其他 agent workspace、共享 skill 目录或管理面配置。
在 AIOS/OpenClaw 运行环境中记录当前 agent 的错误、纠正、经验、知识缺口和可复用改进。适用于命令失败、用户纠正回答、发现过时知识、外部工具/API 异常、同类问题反复出现、完成复杂任务后需要沉淀经验、或开始重要任务前需要回顾当前 agent workspace 内历史 learnings…
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 51/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 9 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 40 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1762 tokens
- 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
- +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
- +1No license
- +2Single-language instructions
- +3Description length 317: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 40 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.