SKILLEMALL.ai

BB intent-router

Converges an underspecified request into an IntentSpec: looks up what sources answer, asks only what they cannot, halts instead of guessing, checks delivery against the spec. Use when a request to implement, add, change, fix or configure leaves a decision open — scope, approach, failure behaviour, what done means — contradicts itself, reads two ways, or rests on a premise sources rule out. Silent on a fully stated request its sources do not contradict; not for explaining existing state.

angel291592/Intent-Router Agent Skills author: angel291592 12 files body ≈ 7 525 tokens Open the sourcegithub.com↗ analyzed 9 h ago

Converges an underspecified request into an IntentSpec: looks up what sources answer, asks only what they cannot, halts instead of guessing, checks delivery…

As a process B 69/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

AnalyzerSoftware developmentData and analyticsAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
B
69/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 12. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7525 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 69/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 12 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Execution cost. Instruction body is 7525 tokens
  • 100Steps. 63 steps
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 5 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (9 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
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 491: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 63 items
  • +3Output format is stated explicitly
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.