CC flaky-smoke-tests
Diagnose intermittent VS Code Electron smoke-test failures from the Azure DevOps Flaky Smoke Tests pipeline (definition 700). Covers finding failed iterations, downloading task logs and platform artifacts with Azure CLI, correlating cumulative runner logs, tracing the introducing commit, and queueing focused validation runs.
The skill promises to diagnose intermittent VS Code smoke-test failures in Azure DevOps: locate failed runs, fetch task logs and platform artifacts, correlate runner logs, trace the introducing commit, and queue focused validation. In reality, it's a text file with instructions and no scripts. Quality score is 72, process score 62. No critical errors, but no automation either—you'll manually run Azure CLI commands and search log patterns yourself.
For those familiar with the pipeline internals and what to hunt for in logs, it's a handy reference that saves time on flag lookups and action sequencing. For others, it's an instruction set you'll need to translate into actual commands. Install if you regularly chase flaky tests in VS Code and need a structured checklist.
Diagnose intermittent VS Code Electron smoke-test failures from the Azure DevOps Flaky Smoke Tests pipeline (definition 700).
As a process C 62/100 · Has gaps — weak spots: result and completion, running it twice
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") - note
edit-residuethe text marks something as outdated (lines 235): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 62/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 18 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 59 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3144 tokens
- 100Progress reporting. Reports progress
- low 12 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (4 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 326: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 59 items
- +4Has examples (13 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.