BD feedback-loop
当用户对输出给出学习或行为反馈(称赞/批评/达成共识/改主意)时,按反馈学习循环处理写入 feedback-log,重要反馈走完整落盘链(feedback-log→conclusions→MEMORY→验证可检索),保持立场一致不横跳。触发词:做得好/记住了/有问题/怎么又这样/记住/以后都这样(仅在指向本输出/行为的评价时生效)。
As a process D 45/100 · Unfinished process — 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.
For the model run — optional
- 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 45/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 8 mutating operations with no state check
- 60Tools and files. Uses tools (git) that frontmatter does not declare
- 100Steps. 58 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1172 tokens
- 100Progress reporting. Reports progress
- low 14 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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 167: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 58 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.
External checks
ClawHub: suspicious
This skill is transparent about creating long-term feedback memory, but it also persists user feedback across several agent-control files and can modify governance files, so it deserves manual review before installation.
LLM: suspicious (high) · 20 Aug 2026