BC memory-orchestrator
Layered memory orchestration for OpenClaw conversations. Use when implementing or maintaining a memory system that must classify user input by domain, capture preferences/decisions during chat, organize long-term knowledge objects, do summary-first recall, and periodically reflect/compress memory for better future retrieval. Triggers on requests about memory architecture, recall strategy, progressive disclosure, long-term context, cross-topic association, user preference capture, or building an OpenClaw skill/plugin for persistent memory.
Layered memory orchestration for OpenClaw conversations.
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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: 23. 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 55/100
- 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. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (memory-orchestrator) differs from the folder (mem-orchestrator)
- 85Steps. 69 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 2061 tokens
- 100Progress reporting. Reports progress
- low 11 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
- -35 of 12 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 544: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 69 items
- +4Has examples (5 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.