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Found 87 Skills
Reads documented bugs from bugs.md, analyzes root causes, implements fixes with regression tests, and validates the full test suite. Prioritizes fixes by severity (high to low). Updates bugs.md with correction status and generates a final bugfix report. Use when the user asks to fix bugs, resolve issues, or run the bugfix workflow for a feature. Do not use for new feature implementation, code review, or QA testing.
Runs Visual Regression Testing (VRT) locally to prevent disqualification in Web Speed Hackathon. Captures screenshots, compares against baselines, updates snapshots, and validates visual integrity after performance optimizations. Use when optimizing WSH apps, running VRT checks, updating VRT baselines, or investigating VRT failures.
[Hyper] Test Codex/agent skills for intended triggering and behavior with realistic positive, negative, boundary, and edge-case scenarios. Use when validating a skill folder, SKILL.md, rules/references/scripts/assets, trigger precision, workflow correctness, or regression coverage before shipping skill changes.
Map test coverage to GDD critical paths, identify fixed bugs without regression tests, flag coverage drift from new features, and maintain tests/regression-suite.md. Run after implementing a bug fix or before a release gate.
Drives a disciplined explore → plan → implement → verify loop for changing an AI agent's behavior with confidence — whether fixing a reported failure or introducing a new requirement, business rule, or policy. Grounds the diagnosis in MLflow traces, codifies the desired behavior as a regression test suite (`mlflow.genai.evaluate` assertions in `@mlflow.test` pytest tests), and iterates the agent — not the test — until green, resisting quick system-prompt patches when the real fix is upstream (missing tool, retrieval source, or capability). Use whenever the user wants to fix or change how an agent behaves — e.g. "fix this issue in my agent", "this answer is wrong", "the agent is hallucinating", "improve my agent based on this trace", "make the agent do X instead of Y", "I want the agent to lead with/prioritize/recommend X", "new business rule: the agent should X", "always/never do X", "change the agent's default behavior" — or shares a trace they want addressed.
Hard-bug DIAGNOSIS + FIX: reproduce the failure, name the cause, write a regression test, apply the minimal fix. Use whenever the user reports a bug, failure, flaky test, perf regression, error, or visible misbehaviour whose cause is not yet known — even if they only paste a symptom, stack trace, or failing test output, or say "why is X broken", "it stopped working", "it got slower", "this looks wrong". Do NOT start debugging inline without this skill: if your next step would be forming hypotheses about an unexplained failure, invoke /pasteurize first. Do NOT use for review-only diffs (/age), feature design (/mold), fixes where the cause is already known (/cook), or when the user opted out of writes (/culture).
Analyzes errors, searches past solutions in memory, provides immediate fixes with code examples, and saves solutions for future reference. Use when user says "debug this", "fix this error", "why is this failing", or when error messages appear like TypeError, ECONNREFUSED, CORS, 404, 500, etc.
Snapshot test email templates using Verify to catch regressions. Validates rendered HTML output matches approved baseline. Works with MJML templates and any email renderer.
Evaluate Omni AI query generation accuracy by running test prompts through the Omni CLI, comparing generated query JSON against expected results, and scoring accuracy. Use this skill whenever someone wants to evaluate Omni AI, benchmark Blobby, run regression tests, compare AI output across branches or configurations, test prompt variations, measure AI quality, run A/B tests on model changes, assess impact of context changes, or any variant of "run evals", "test Blobby", "benchmark query generation", "compare AI results", "regression test", "how accurate is the AI", or "measure the impact of my changes".
Debugs a failing production call, reproduces the bug with Cekura evaluators, implements a fix, verifies it, runs regression tests, then raises a PR with evidence. Use when the user wants to fix a production call bug, investigate a failing prod call, reproduce and fix a production issue, run regression tests before a PR, or says things like "fix this prod call issue", "debug and fix call ID", "test my fix against prod scenarios", "reproduce this production bug", or "regression test before raising PR".
Generate security fixes from detect-dev findings with regression tests. Use when remediating security vulnerabilities.
Workflow-first code review for bugs, regressions, risky behavior changes, and missing tests. USE when reviewing diffs, PRs, or proposed implementations.