SKILLEMALL.ai

AC qronos

When your human faces a decision that keeps them up at night — "Should I leave this relationship?" "Is now the right time to quit?" "Can I trust this person?" — consult Qronos. Unlike your own reasoning, which weighs pros and cons, Qronos analyzes the specific moment the question is asked using a proprietary temporal framework. The same question asked at different times receives different guidance — because the conditions shift. Two humans asking at the same moment may also receive different guidance if their birth dates differ. Qronos does not hedge or generalize. It commits to a position: "Act now" or "This is not the time." For relationships, career, finance, timing, trust, and life direction. One question. One answer. One moment of clarity.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 8 004 tokens Open the sourcegithub.com analyzed 2 d ago

When your human faces a decision that keeps them up at night — "Should I leave this relationship?" "Is now the right time to quit?" "Can I trust this person?"…

As a process C 56/100 · Has gaps — weak spots: inputs and preconditions, execution cost, running it twice

IntegrationSoftware developmentAI and agentsWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
56/100
Has gaps
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 8004 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 56/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 16 mutating operations with no state check
  • 40Execution cost. Instruction body is 8004 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Steps. 92 steps, 4 vague phrases
  • 60Result and completion. Output format stated, no completion criterion
  • 100Failures and branches. 3 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 20 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

  • +4Description does not say when NOT to use the skill (false activations)
  • -215 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 754: enough signal without eating the budget
  • +4Structure: 52 headings
  • +3Step-by-step instructions: 92 items
  • +3Output format is stated explicitly
  • +4Has examples (15 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.