BC vibo-memory
Use when the agent needs persistent memory (L1/L2/L3), a living document archive (.vibo: pack documents, search by meaning, answer questions), web-search savings (compress articles up to 96%), thread memory (compress long conversations, restore details), live handoff (resume/save-state), or a privacy layer (mask secrets before they reach any LLM). Requires a valid ViBo license.
vibo: pack documents, search by meaning, answer questions), web-search savings (compress articles up to 96%), thread memory (compress long conversations…
As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6538 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "tools"
Process rating: all ten parameters 59/100
- 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
- 30Running it twice. 22 mutating operations with no state check
- 70Execution cost. Instruction body is 6538 tokens
- 85Steps. 82 steps, 1 vague phrases
- 100Tools and files. Tools declared in frontmatter
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 16 top-level sections: this looks like several domains in one skill
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
- -2localhost URLs: will not work for another user
- -241 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 380: enough signal without eating the budget
- +4Structure: 26 headings
- +3Step-by-step instructions: 82 items
- +4Has examples (16 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.