AB self-learning-coach-deep
Deep self-learning coach for AI agents. Use when the user wants guided self-learning, mode selection for quick/standard/deep study, deeper business learning, training-material style lessons, detailed source grounding, business-context analysis, Feishu/internal-document learning, web/video-supported research, case diagnosis, or "讲深一点/深入学习/结合业务/多引用资料/做培训材料". Produces user-selected learning paths, source-grounded HTML lessons with inline citations, business scenario mapping, case analysis, practice tasks, progress tracking, and source records. Works best for Feishu Miaoda/OpenClaw, while remaining usable in other agents that can create files.
Deep self-learning coach for AI agents.
As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice
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: 3. 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 65/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 7 mutating operations with no state check
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 100Steps. 79 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 9 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3957 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
- +1No license
- +2Single-language instructions
- +3Description length 647: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 79 items
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.