AB mem-skill
Self-evolving memory and knowledge accumulation system for AI agents. Acts as a persistent 'second brain' that automatically retrieves past experiences, captures best practices, and proactively records successful solutions to a private knowledge base. Use this skill whenever starting any task, opening a new conversation, or triggering any other skill. Supports memory engines (default: built-in JSON/Markdown index; optional: QMD semantic search). Initialize with: /mem-skill init [--mem-engine=qmd].
As a process B 76/100 · Nearly there — no weak spots found
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 · 0
✓ No critical or high findings
Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6131 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 76/100
- 60Tools and files. Uses tools (bash, read, web, node) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Failures and branches. 19 branches
- 70Execution cost. Instruction body is 6131 tokens
- 100Steps. 176 steps
- 100Consistency. Name and required fields are in place
- 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 12 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (41 tags): a typed call is more reliable
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)
- -31 of 1 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 502: enough signal without eating the budget
- +4Structure: 31 headings
- +3Step-by-step instructions: 176 items
- +3Output format is stated explicitly
- +4Has examples (13 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.