AD memory-graph
Agent-agnostic personal knowledge graph stored as markdown files with YAML frontmatter. Use when you need persistent context about the user's life, projects, tools, people, concepts, or decisions — especially across agent resets or switches. Also use when logging significant activity, searching for context before starting work, creating knowledge nodes for new topics, discovering connections between existing nodes (backfill), or rebuilding indexes. Triggers include needing user context, logging work, "remember this", "what do I know about X", creating/updating knowledge entries, and periodic memory maintenance.
As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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: 18. 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 48/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (memory-graph) differs from the folder (agent-memory-graph)
- 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
- 70Failures and branches. 5 branches
- 85Steps. 40 steps, 1 vague phrases
- 100Execution cost. Instruction body is 2335 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low The response is described with custom markup (3 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 618: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 40 items
- +4Has examples (10 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 8 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.