BF keep-learning-agent
持续学习 Agent - 知识沉淀和经验固化框架。支持学习记录、快速索引、自我修复、经验→模型转化。包含完整模板、索引系统、SOP 流程。让 AI Agent 持续进化，每天进步一点点。
As a process F 35/100 · Will not run — References files that are not bundled: url
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
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: 4. 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") - warning
missing-refreference to a missing file: url - note
frontmatter-keyunknown frontmatter key "created"
Process rating: all ten parameters 35/100
Will not run. References files that are not bundled: url
- 0Tools and files. 1 referenced file(s) missing: url
- 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
- 100Steps. 22 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1463 tokens
- 100Running it twice. No mutating operations
- low 12 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)
- +3Description length 93: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -223 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +4Structure: 26 headings
- +3Step-by-step instructions: 22 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.
External checks
ClawHub: suspicious
This is a coherent learning and memory framework, but it asks agents to automatically run unreviewed local PowerShell scripts and persist changes into agent configuration without enough scoping or user control.
LLM: suspicious (high) · VirusTotal: · 29 May 2026