CC memory-health
Complete memory management pipeline for OpenClaw agents — extraction, archiving, scoring, consolidation, health monitoring, hygiene, and ontology. Local by default (Ollama over local HTTP). Use for memory health checks, hybrid search, fact arbitration, cold-storage compaction, and memory pipeline maintenance.
Complete memory management pipeline for OpenClaw agents — extraction, archiving, scoring, consolidation, health monitoring, hygiene, and ontology.
As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, consistency
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
-
low Risky intent
intent-offensive-securitydocs/SECURITY-AUDIT-NOTES.md:186Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)**Scanner class:** Privilege Escalation / Credential Access
detector
Files scanned: 35. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5122 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 54/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (memory-health) differs from the folder (memory-toolkit)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 5122 tokens
- 100Steps. 36 steps
- 100Running it twice. Mutating operations check current state
- 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
- +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
- -2localhost URLs: will not work for another user
- -223 emoji in the instructions: noise for the model
- -31 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 310: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 36 items
- +4Has examples (15 code blocks)
- +4Reference files are cited in the instructions (1 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.