AC memos-cloud-server
External long-term memory and knowledge base backed by the MemOS Cloud API. Capabilities — search prior memory, add conversation messages, delete or correct memories via feedback, retrieve a consolidated user profile (facts, preferences, tool history), and manage knowledge bases and their documents. Use proactively on every user turn (search memory before answering and persist the exchange after), and whenever the user references past context, their identity, preferences, or history, or asks to remember, recall, modify, forget, or correct something (e.g., "who am I", "what do I like", "remember that...", "forget X", "you got it wrong"). Also use when uploading, listing, or deleting knowledge base files.
As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
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: 11. 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 64/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 30 mutating operations with no state check
- 40Consistency. Frontmatter name (memos-cloud-server) differs from the folder (memos-cloud-skill)
- 70Execution cost. Instruction body is 4268 tokens
- 100Tools and files. No external tools needed
- 100Steps. 71 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 3 branches, has a failure section
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (9 tags): a typed call is more reliable
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
- +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
- +5Description quotes 5 example trigger phrases
- +3Description length 712: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 71 items
- +4Has examples (19 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.