AF alibabacloud-agentloop-experience
Proactively use AgentLoop Recall to retrieve prior Alibaba Cloud AgentLoop experience through the bundled SearchContext CLI whenever the user asks or implies that prior work may help. Trigger for requests to check, search, recall, retrieve, look up, review, consult, reference, or compare prior experience, historical troubleshooting cases, past fixes, comparable incidents, lessons learned, old runbooks, previous remediations, or successful workflows before or during work. Also trigger for Chinese requests such as 先查历史经验、回忆类似案例、召回过往排障记录、看看之前有没有处理过类似问题、 参考以前怎么处理、找找之前的踩坑记录、翻一下历史排障、有没有类似经验、先看看过去的案例.
Proactively use AgentLoop Recall to retrieve prior Alibaba Cloud AgentLoop experience through the bundled SearchContext CLI whenever the user asks or implies…
As a process F 68/100 · Will not run — References files that are not bundled: assets/recall.env.example
How to improve
- The text references files that are not there: add them or drop the references.
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: assets/recall.env.example
Process rating: all ten parameters 68/100
- 0Tools and files. 1 referenced file(s) missing: assets/recall.env.example
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 100Steps. 19 steps
- 100Inputs and preconditions. Inputs and preconditions are listed
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1355 tokens
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)
- +2Single-language instructions
- +3Description length 601: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 19 items
- +3Output format is stated explicitly
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.