CC astronomy-inference
Carries astronomical data from file to posterior with the conventions the field expects, covering astropy units, coordinates, FITS, WCS and Time with barycentric corrections, photometric systems and magnitude arithmetic, period finding with the astropy Lomb-Scargle periodogram and its false-alarm levels, Bayesian parameter estimation with emcee and nested sampling with dynesty, convergence diagnostics such as autocorrelation time and R-hat, uncertainty propagation through samples, and explicit reporting of priors. Use for light curves, photometry, spectroscopy, orbit or transit fits and any astronomical parameter estimation; use astropy for plain library usage and bayesian-inference for non-astronomical models.
Carries astronomical data from file to posterior with the conventions the field expects, covering astropy units, coordinates, FITS, WCS and Time with…
As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- 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
-
medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Read Bash python
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "summary"
Process rating: all ten parameters 61/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 40Result and completion. Does not say what the result is
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 22 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1662 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
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
- +4No input/output examples
- +2Single-language instructions
- +3Description length 720: enough signal without eating the budget
- +4Structure: 6 headings
- +3Step-by-step instructions: 22 items
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.