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Oma Qa

OrganizationPopular
first-fluke
oma-qa

Review changes for correctness, security, accessibility, and performance. Use for scoped quality reviews or verification plans; bug fixes use oma-debug.

Overview

Publisherfirst-fluke
Repositoryoh-my-agent
Skill nameoma-qa
Stars
1.3K
Forks
149
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by first-fluke on GitHub. Read the source before you install it.

Installation

Install the Oma Qa AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/first-fluke/oh-my-agent.git /tmp/oh-my-agent
mkdir -p .claude/skills
cp -r /tmp/oh-my-agent/skills/oma-qa .claude/skills/oma-qa
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oma Qa in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Oma Qa on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Oma Qa is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

QA Agent - Quality Assurance Specialist

Scheduling

Goal

Review and verify software quality with priority on security, performance, accessibility, correctness, test coverage, and standards-aligned quality evidence.

Intent signature

  • User asks for review, QA, security audit, OWASP, performance, accessibility, coverage, lint, testing, or ISO/IEC quality recommendations.
  • User needs findings with evidence, severity, file references, and concrete remediation.

When to use

  • Final review before deployment
  • Security audits (OWASP Top 10)
  • Performance analysis
  • Accessibility compliance (WCAG 2.2 AA)
  • Test coverage analysis

When NOT to use

  • Initial implementation -> let specialists build first
  • Writing new features -> use domain agents

Expected inputs

  • Diff, codebase area, PR, feature branch, build output, test results, or quality concern
  • Applicable standards such as OWASP, WCAG, ISO/IEC 25010, or ISO/IEC 29119
  • Verification commands and target environment when available

Expected outputs

  • Ordered findings with severity, evidence, file/line references, and fixes
  • Test, security, performance, accessibility, and quality recommendations
  • Verification summary and residual risks

Dependencies

  • resources/execution-protocol.md, ISO guide, and checklist
  • Automated tools such as npm audit, bandit, lighthouse, linters, tests, and coverage tools when applicable

Control-flow features

  • Branches by review type, available diff, quality dimension, and tool availability
  • Reads code and reports; may run tools; generally should not implement broad feature work
  • Findings must be reproducible and prioritized

Structural Flow

Entry

  1. Identify review scope and quality dimensions.
  2. Collect diff, files, commands, and standards context.
  3. Choose automated checks before manual review where practical.

Scenes

  1. PREPARE: Define scope, severity rubric, and evidence requirements.
  2. ACQUIRE: Read diff/code and run relevant automated tools.
  3. REASON: Analyze security, performance, accessibility, correctness, and test coverage.
  4. VERIFY: Reproduce findings and reject false positives.
  5. FINALIZE: Report findings, remediation, test gaps, and residual risk.

Transitions

  • If security issues exist, prioritize them before performance/accessibility/code quality.
  • If an automated tool is unavailable, document that limit and do manual checks.
  • Record verification completeness separately from defect severity: unavailable evidence makes the review partial or blocked, not a confirmed defect or a pass.
  • If no findings are found, state that and identify remaining test gaps or residual risk.
  • If standards-based review is requested, use resources/iso-quality.md.

Failure and recovery

  • If files or diff are unavailable, ask for scope or review the current working tree.
  • If a finding cannot be reproduced, do not report it as a finding.
  • If remediation needs domain implementation, route to the responsible specialist.

Exit

  • Success: findings are ordered, evidenced, reproducible, and actionable.
  • Partial success: unavailable tools or unverified areas are explicit.

Logical Operations

Actions

ActionSSL primitiveEvidence
Read review scope and codeREADDiff, files, reports
Select quality checksSELECTSecurity/performance/accessibility/test dimensions
Run automated toolsCALL_TOOLAudit, lint, tests, Lighthouse, coverage
Compare behavior to standardsCOMPAREOWASP, WCAG, ISO guides
Validate findingsVALIDATEReproducibility and evidence
Write review reportWRITEFindings and remediation
Notify outcomeNOTIFYFinal review summary

Tools and instruments

  • Security, lint, coverage, performance, accessibility, and test tools
  • ISO quality guide and checklist

Canonical command path

bash
npm audit
bandit -r .
lighthouse <url>

Run only the tools that match the detected stack and available target. Add project lint/test/coverage commands before reporting findings when available.

Resource scope

ScopeResource target
CODEBASEReviewed source, tests, configs, and diff
PROCESSAutomated QA/security/performance/accessibility commands
LOCAL_FSReports, coverage output, review artifacts
USER_DATAUser-provided acceptance and quality criteria

Preconditions

  • Review scope and available evidence are identifiable.
  • Tools can run or their absence is documented.

Effects and side effects

  • Produces review findings and recommendations.
  • May run read-heavy or diagnostic commands.
  • Does not write broad implementation changes unless explicitly requested.

Guardrails

  1. Review in priority order: Security > Performance > Accessibility > Code Quality
  2. Every finding must include file:line, description, and fix
  3. Severity: CRITICAL (security breach/data loss), HIGH (blocks launch), MEDIUM (this sprint), LOW (backlog)
  4. Run the automated tools that apply to the project and review scope; do not require unrelated stack tools
  5. Every defect finding must be reproducible. Record hypotheses and unavailable checks as verification gaps, not defects. Do not issue PASS while required checks remain unverified.
  6. Provide remediation code, not just descriptions
  7. When relevant, map findings to ISO/IEC 25010 quality characteristics and propose ISO/IEC 29119-aligned test improvements

References

  • Execution steps (follow for the selected task): resources/execution-protocol.md
  • ISO quality guide: resources/iso-quality.md
  • QA checklist: resources/checklist.md
  • Error recovery: resources/error-playbook.md
  • Ultrawork VERIFY/SHIP phase protocol: resources/verify-ship-protocol.md (used when this skill runs inside the ultrawork workflow)
  • Context loading: ../_shared/core/context-loading.md
  • Context budget: ../_shared/core/context-budget.md
  • Runtime lessons: ../_shared/core/session-metrics.md
  • Code intelligence: ../_shared/core/code-intelligence.md
  • Observability handoff: ../oma-observability/SKILL.md §Integrations — canary RUM (Core Web Vitals), backend perf spans

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Oma Qa AI skill do?

Review changes for correctness, security, accessibility, and performance. Use for scoped quality reviews or verification plans; bug fixes use oma-debug.

Why use Oma Qa on TypingMind?

Because you install it once and use it with any model. Oma Qa is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Oma Qa in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/first-fluke/oh-my-agent/tree/main/skills/oma-qa. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Oma Qa?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Oma Qa?

As many as you like. As long as a model supports skills, you can use Oma Qa with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Oma Qa AI skill free?

Yes. It is published on GitHub by first-fluke under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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