Testing & Quality skills

Browse reusable Agent Skills, each with a clear purpose and practical guidance.

opsmill-dev-analyzing-bugs

Performs root-cause analysis of a bug — from a GitHub issue, an issue URL, or a free-text description — before any reproduction or fix is written. TRIGGER when: triaging or diagnosing a bug, investigating why something misbehaves, an issue number/URL handed over for analysis, needing the root cause before touching code. DO NOT TRIGGER when: a failing reproduction test already exists and you are ready to fix → opsmill-dev-fixing-bugs; writing that reproduction test → opsmill-dev-test-driving-bugs; capturing a new bug as a ticket → opsmill-dev-creating-issues.

494 repo starsObserved in 2 repos
Testing & Quality
GitHub

opsmill-dev-fixing-bugs

Implements and validates the fix for a bug once a failing reproduction test exists. TRIGGER when: a bug has a failing reproduction test and you are ready to make it pass, implementing the root-cause fix, the final step of the bug-fixing pipeline. DO NOT TRIGGER when: no reproduction test exists yet → opsmill-dev-test-driving-bugs; still diagnosing, or asked to fix a bug with no analysis or reproduction test yet → opsmill-dev-analyzing-bugs.

494 repo starsObserved in 2 repos
Testing & Quality
GitHub

opsmill-dev-test-driving-bugs

Writes a single failing test that reproduces a bug after its root-cause analysis is complete, before any fix is written. TRIGGER when: a bug has a completed root-cause analysis and you need the failing reproduction test, writing a test that proves a bug exists, the second step of the bug-fixing pipeline. DO NOT TRIGGER when: still triaging or diagnosing the bug → opsmill-dev-analyzing-bugs; implementing the fix once the test exists → opsmill-dev-fixing-bugs; general feature test-first work → superpowers test-driven-development.

494 repo starsObserved in 2 repos
Testing & Quality
GitHub

dg-skill

Adversarial code review with two sub-agents. Use for code review.

494 repo starsObserved in 1 repos
Testing & Quality
GitHub

script-skill

A skill with scripts. Use when testing script discovery.

494 repo starsObserved in 1 repos
Testing & Quality
GitHub

benchmark-on-visionanalysis

Signpost for benchmarking LibreYOLO models for visionanalysis.org. Use when someone wants to "benchmark for visionanalysis", produce a submission for the site, measure a model on COCO for publication, or add a hardware/runtime row to the site. The actual work lives in two OTHER repos; this skill orients you and hands off. It does not run benchmarks itself.

493 repo starsObserved in 1 repos
Testing & Quality
GitHub

libreyolo-checkpoint-metadata

Inspect, validate, produce, and debug LibreYOLO .pt checkpoint metadata. Use when a checkpoint won't load ("not a LibreYOLO checkpoint", wrong family/task/class-count errors), when writing or reviewing a conversion script, when deciding what a converter must emit, when preparing weights for HF upload, or when a schema change is proposed. Covers the inspection commands, the serialization helpers that are the only sanctioned writer/reader, the lean-vs-training checkpoint distinction, the legacy/foreign-weights paths and auto-conversion, and the contract-change protocol. The schema itself lives in docs/checkpoint_schema.md; this skill is how to work with it.

493 repo starsObserved in 1 repos
Testing & Quality
GitHub

libreyolo-profiling

Diagnose and fix slow LibreYOLO with the `libreyolo profile` CLI — both TRAINING throughput (`profile run`) and INFERENCE latency (`profile infer`). Use whenever training feels slow, GPU utilization is low, images/sec is disappointing, inference/predict latency is too high, a run is dataloader- / host-launch- / NMS- / preprocess-bound, or someone wants to optimize step time, batch size, throughput, or p50/p90/p99 latency. Teaches the profile → diagnose → change → compare loop an agent runs to push speed to the max. This is for SPEED, not accuracy (mAP).

493 repo starsObserved in 1 repos
Testing & Quality
GitHub

libreyolo-review-pr

Review a LibreYOLO pull request the way this repo expects: contract-first (REVIEW.md axioms + /docs schemas), evidence-based, and verified live in a worktree rather than by reading the diff alone. Use whenever the user asks to review a PR, assess an external contribution, second-opinion a branch, or "deep review" something before merge. Covers the reading order, the worktree + live-verification method, the finding taxonomy and severity bar, and the delivery rules (findings go to the user; agents never post PR comments or reviews themselves).

493 repo starsObserved in 1 repos
Testing & Quality
GitHub

libreyolo-run-e2e-tests

Launch LibreYOLO's end-to-end (e2e) test suite the right way — the heavy GPU tests under tests/e2e/ that load real weights, export to ONNX/TensorRT/OpenVINO/ ncnn/TorchScript/CoreML, train on real datasets, and check inference parity. Use whenever someone wants to run e2e tests, the nightly suite, a single e2e file, a model family's e2e coverage, an export-backend check, or an RF1/RF5 training test — or is confused about why e2e tests "don't run" or all skip. Covers the Makefile targets, the direct-pytest fallback (Windows / no make+uv), the marker taxonomy, weight/dataset provisioning, and how to read the results. This is for *running* the e2e suite, not for writing new e2e tests.

493 repo starsObserved in 1 repos
Testing & Quality
GitHub