AB url-manager
Cross-platform URL collection & knowledge management with agent-first auto-registration. Use when users say "save/bookmark/collect/remember this", need to organize links into categories, share curated collections, or build a structured knowledge base from web resources. Supports collaborative shared categories, full-text search, and magic-link delivery to users.
Cross-platform URL collection & knowledge management with agent-first auto-registration.
As a process B 65/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, running it twice
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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5274 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 65/100
- 20When it triggers. No condition that starts the skill
- 30Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 32 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Execution cost. Instruction body is 5274 tokens
- 100Steps. 40 steps
- 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 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (19 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)
- -224 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 364: enough signal without eating the budget
- +4Structure: 47 headings
- +3Step-by-step instructions: 40 items
- +3Output format is stated explicitly
- +4Has examples (17 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.