BD ultra-memory
ultra-memory 给 AI Agent 提供跨会话记忆。每次操作后自动记录，会话结束后可恢复，支持按关键词检索历史操作。 【必须触发-中文】用户说以下任意词：记住、别忘了、记录一下、不要忘记、上次我们做了什么、帮我回忆、继续上次的、从上次继续、记忆、帮我记、追踪进度 【必须触发-英文】用户说以下任意词：remember、don't forget、recall、what did we do、pick up where we left off、continue from last time、memory、keep track、track progress、log this 【隐式触发-A+B】同时满足以下两条时触发：(A)消息含持续性任务动词：开发、实现、处理、完成、构建、develop、implement、create、fix；(B)消息中包含项目名词（专有名词/文件名/系统名） 【不触发】用户只问一个问题无后续；单次咨询无操作步骤；说"随便聊聊"、"just chatting"；明确说"不用记录"
As a process D 43/100 · 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.
- 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 · 2
✓ No critical or high findings
Medium and low: 2
-
low Secrets in code
secret-high-entropy-tokenreferences/advanced-config.md:97High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"model": "para…-v2",
quoted -
low Secrets in code
secret-high-entropy-tokenreferences/advanced-config.md:106High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)- `para…-v2`（小，快，多语言）
quoted
Files scanned: 40. 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 43/100
- 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. 1 mutating operations with no state check
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 100Steps. 35 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3353 tokens
- low 18 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
- -216 emoji in the instructions: noise for the model
- -311 of 18 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 459: enough signal without eating the budget
- +4Structure: 50 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (26 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.