AD memory-dreaming
Autonomous memory consolidation for OpenClaw agents — like REM sleep. Periodically gathers signal from daily logs, session transcripts, and learnings; consolidates into MEMORY.md; syncs structured knowledge to an Obsidian vault (or any markdown knowledge base); tracks plans; prunes stale entries. Use when: (1) setting up periodic memory maintenance, (2) manually triggering a dream cycle, (3) configuring Obsidian vault sync, (4) agent memory is getting noisy/contradictory and needs consolidation.
Autonomous memory consolidation for OpenClaw agents — like REM sleep.
As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
The same skill appears in 1 more place: ClawHub
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: 13. 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 48/100
- 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
- 30Running it twice. 22 mutating operations with no state check
- 40Consistency. Frontmatter name (memory-dreaming) differs from the folder (memory-dreaming-safe)
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 85Steps. 119 steps, 2 vague phrases
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 2970 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 15 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
- +3Output format is not stated: the model decides each time
- -31 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 500: enough signal without eating the budget
- +4Structure: 33 headings
- +3Step-by-step instructions: 119 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (2 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.