BD Memory_Skill_Manager
Responsible for maintaining SKILLMEMORY.md in the target skill directory, recording the three most recent execution pipeline JSONs, and modifying the description file of the target SKILL.md to achieve progressive experience awakening upon each skill invocation.
As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "trigger_keywords"
Process rating: all ten parameters 47/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (Memory_Skill_Manager) differs from the folder (memory-skill-manager)
- 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 100Steps. 7 steps
- 100Execution cost. Instruction body is 792 tokens
- 100Running it twice. No mutating operations
- low The response is described with custom markup (8 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)
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 261: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 7 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.