eval-run
Skill11 starsChanged 44 days agoApache-2.0View on GitHub
Run an AI application against reviewed cases with validated graders, reliable traces, usage accounting, and resumable results. Use for baselines and comparisons, not for inventing criteria without evidence.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- alexaiarchitect/google-adk-skillstheir own version, first seen Oct 20, 2026
- AaravChadha/acstacktheir own version, first seen Jul 30, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- AaravChadha/acstack★ 2same name
- alexaiarchitect/google-adk-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository9 other files it gives its agents.
- eval-auditSkill
- eval-buildSkill
- eval-datasetSkill
- eval-descentSkill
- eval-discoverSkill
- eval-error-analysisSkill
- eval-gradeSkill
- eval-maintainSkill
- eval-traceSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
Read the operating agreement, execution requirements, and portable artifacts.
- Distinctive: line 29
Use fresh app outputs for each application comparison.
- Distinctive: line 33
Use the host's background execution when supported, with a progress artifact.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Run a reproducible evaluation
Read the operating agreement, execution requirements, and portable artifacts. For hosted runs read managed guidance.
For bundled local tools and exact commands, read toolkit setup. Run the workspace installer for this skill; it preserves existing customized files. For the managed route, use concrete service operations after confirming the connected schema.
Prepare
Reuse the real application entrypoint and existing runner. Check reviewed cases, criteria, versions, split, fixtures, and intended claim. If those are missing, finish the corresponding discovery/curation/calibration stage first.
Choose case count, repetitions, app/judge settings, concurrency, timeout, and retry policy. Confirm paid scope before calling services; use existing authorized limits. A measured pilot supports duration and spend estimates. Include app, judge, and billed retry usage. No budget change is implicit authorization to continue.
Pilot
Run one authorized case, then inspect the actual saved output, trace, grade, model identity, timing, and available usage. Verify unknown fields are not silently zero. Check that graders consume the actual app result and that requested settings take effect. Test local error classification without spending a full pass.
Execute
Use fresh app outputs for each application comparison. Regrading stored output is valid for grader calibration, but label it as such. Write completed records incrementally. Run bounded concurrency, jittered retries, per-case wall-clock limits, and clean cancellation. Record errors and billable attempts separately.
Resume only against unchanged case, app, grader, and run fingerprints. Preserve at most one terminal record per case/repetition in a run; separate attempt history. If the app failed before grading, it is an execution error. An actual safety refusal or incorrect answer is an observed outcome whose grading follows the pre-agreed criteria.
Use the host's background execution when supported, with a progress artifact. Do not create subagents just to wait on a shell command. Verify completion from saved counts, not a notification alone.
Report
Recompute aggregates from raw records. Show scorable count, error count, missing metrics, refusals, truncations, and denominators alongside scores. Do not present a partial pass as a complete run. Estimate uncertainty at the independent case/group level.
Managed: link the verified hosted run and preserve export/version identifiers. Local: build/reuse a readable report alongside the review UI; escape all untrusted text. Summarize per-case failures, metric tradeoffs, costs where known, and evidence links.
Deliver the exact rerun command and configuration. The bundled artifact helper can validate portable records, inspect an export, and create group splits; it is not a replacement app runner.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

