AC sleep-consolidation
Use this skill to consolidate an AI agent's daily experiences and learnings into structured long-term memory, mimicking human sleep-based memory consolidation. Trigger this skill whenever: an agent session ends and there are new learnings to store, a user asks the agent to "sleep", "consolidate memory", or "process today's learnings", the agent's working memory is getting full and needs compression, the context window is approaching its limit and memories need flushing, or you want to extract insights from accumulated interaction logs. The skill runs three modes: micro-rest (waking replay for quick within-session notes), NREM (deep structured consolidation with dual fast/slow tracks), and REM (creative cross-domain synthesis). Memory is stored as plain Markdown files following OpenClaw's two-layer architecture. Always use this skill at the end of long agent sessions, before context compaction, and whenever explicitly asked to "remember".
As a process C 64/100 · Has gaps — 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: 7. 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
- 30Running it twice. 4 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 8 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1954 tokens
- 100Progress reporting. Reports progress
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)
- +3Description length 951: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -31 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +4Structure: 12 headings
- +3Step-by-step instructions: 8 items
- +4Has examples (10 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.