BF memory-graph
基于 SQLite 的实体关系图谱存储，提供 add_entity / add_relation / query / traverse / get_path 等原子操作，供 skill-compounding 沉淀时调用。
基于 SQLite 的实体关系图谱存储，提供 addentity / addrelation / query / traverse / getpath 等原子操作，供 skill-compounding 沉淀时调用。
As a process F 34/100 · Will not run — weak spots: steps, result and completion, when it triggers
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
- 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: 4. 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") - note
frontmatter-keyunknown frontmatter key "owner"
Process rating: all ten parameters 34/100
- 0Steps. Prose only: no discrete steps
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 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 (memory-graph) differs from the folder (sipoon-memory-graph)
- 100Tools and files. No external tools needed
- 100Execution cost. Instruction body is 736 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)
- +3Description length 111: 120–800 characters recommended
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 7 headings
- +4Has examples (3 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.
External checks
ClawHub: suspicious
This is a local memory-graph skill, but its persistent database location and memory-use controls are under-scoped enough that users should review it before installing.
LLM: suspicious (medium) · VirusTotal: · 28 May 2026