CC pr-artifact-explorer
Open, share, and inspect GitHub PRs, issues, workflow runs (including scheduled, manual and other runs with no PR), Actions artifacts, and local or HTTP(S) web previews in the Artifact Explorer canvas, with viewport presets, zoom, screenshots, and recoverable download jobs.
The skill promises to open and preview GitHub PRs, issues, workflow runs, and artifacts directly in canvas with zoom and screenshots. In the files: one instruction at 1662 tokens, no scripts. Checks show 72/100 quality score with process at 61%; no critical errors, but medium/low findings exist. Supported across all major platforms from Claude to DeepSeek.
In practice, this is a reference guide for artifact viewing without ready-made code to execute it. Grade C means it works with caveats. Install if you need GitHub API integration guidance and can wire up request logic yourself.
Open, share, and inspect GitHub PRs, issues, workflow runs (including scheduled, manual and other runs with no PR), Actions artifacts, and local or HTTP(S)…
As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 61/100
- 0Result and completion. Does not say what the result is
- 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. 2 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 20 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1662 tokens
- low The response is described with custom markup (11 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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 274: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (0 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.