BD enhanced-memory
An enhanced memory system for OpenClaw agents that replaces the default single-file MEMORY.md with a complete memory architecture: hierarchical directory organization by category, [category:value] tag indexing with multi-tag AND search, automatic lifecycle management (active → archive, never delete), and intelligent cross-category retrieval that auto-routes queries to the right memory module. Gives your agent structured, searchable, long-lived memory out of the box.
An enhanced memory system for OpenClaw agents that replaces the default single-file MEMORY.md with a complete memory architecture: hierarchical directory…
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 49/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (enhanced-memory) differs from the folder (openclaw-enhanced-memory)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 10 steps
- 100Execution cost. Instruction body is 1359 tokens
- 100Running it twice. No mutating operations
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
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 470: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (8 code blocks)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.