AC smart-memory-plus
Complete memory system for OpenClaw agents. Combines enhanced memory management (WAL protocol, type classification, temporal decay, session cache) with context compression (conversation digests, auto-injection). Zero external dependencies. Use when: managing MEMORY.md, extracting insights from conversations, compressing long sessions, searching memories, tracking task state, or when user says "remember this", "compact", "what do you know about X", "clean up memories".
As a process C 63/100 · Has gaps — weak spots: result and completion, running it twice
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.
The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Secrets in code
secret-private-keySKILL.md:53Private key material (key header without key body; quoted — discussed, not commanded)- Private keys (`-----BEGIN PRIVATE KEY----- …
header onlyquoted
Files scanned: 15. 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 63/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 7 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 62 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2427 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 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
- +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
- +5Description quotes 4 example trigger phrases
- +3Description length 472: enough signal without eating the budget
- +4Structure: 25 headings
- +3Step-by-step instructions: 62 items
- +4Reference files are cited in the instructions (4 of 4)
- +3All 9 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.