AC AetherLang V3 — Claude Code Integration Skill
Use this skill to execute AetherLang V3 AI workflows from Claude Code. AetherLang provides 9 specialized AI engines for culinary consulting, business strategy, scientific research, and more.
Use this skill to execute AetherLang V3 AI workflows from Claude Code.
As a process C 53/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency
IntegrationSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
How to improve
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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Risky intent
intent-offensive-securitySKILL.md:54Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)| `oracle` | Forecasting | Bayesian updating, black swan scanner, adversarial red team, Kelly criterion |
detector
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens)
Process rating: all ten parameters 53/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 4 mutating operations with no state check
- 40Consistency. Frontmatter name (AetherLang V3 — Claude Code Integration Skill) differs from the folder (aetherlang-claude-code)
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 100Steps. 16 steps
- 100Execution cost. Instruction body is 1523 tokens
- low 10 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)
- +1No license
- +2Single-language instructions
- +3Description length 190: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 16 items
- +3Output format is stated explicitly
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.