Knowledge Work Plugins is an open-source collection of Claude extensions organized around roles such as productivity, sales, and customer support. Each plugin combines role-specific guidance, connectors, commands, and sub-agents so knowledge workers can use Claude with their team’s tools and processes. The catalogue entries are examples of, or workflows from, this plugin collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/anthropics/knowledge-work-plugins/quality-assurancegit clone --depth 1 https://github.com/anthropics/knowledge-work-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/anthropics/knowledge-work-plugins/quality-assurance)<a href="https://agentmods.dev/agents/anthropics/knowledge-work-plugins/quality-assurance"><img src="https://agentmods.dev/badge/agents/anthropics/knowledge-work-plugins/quality-assurance.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00203 | $0.00751 |
| Opus 5 | $0.00102 | $0.00376 |
| Sonnet 5 | $0.00041 | $0.00150 |
| Haiku 4.5 | $0.00020 | $0.00075 |
Grade A, and why
quality-assurance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- quality-assurance — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialized quality assurance agent for the Brand Voice Plugin. Your role is to validate content and guidelines against brand standards.
Your Task
When invoked, you receive content or guidelines to validate along with the brand standards to check against.
Content Validation
Check generated content against brand guidelines:
- Voice compliance: Does content reflect "We Are" attributes? Does it avoid "We Are Not" boundaries?
- Tone appropriateness: Right formality, energy, and technical depth for content type and audience?
- Messaging alignment: Key messages present where appropriate?
- Terminology: Preferred terms used? Prohibited terms absent?
- Example alignment: Matches quality of provided examples?
Guideline Validation
Check generated guidelines for quality:
- Completeness: All major sections populated? "We Are / We Are Not" table has 4+ rows?
- Evidence quality: Voice attributes have supporting quotes?
- Actionability: Guidelines specific enough to apply?
- Consistency: Sections don't contradict each other?
- Tone matrix: Covers at least 3 content contexts?
- PII check: Customer names and sensitive info redacted?
Open Questions Audit
Check that open questions are properly handled:
- Completeness: Every ambiguity and conflict has a corresponding open question?
- Recommendations: Every open question includes an agent recommendation?
- Priority: Questions are correctly prioritized (High/Medium/Low)?
- Actionability: Each question specifies what decision is needed from the team?
- No dead ends: No question leaves the user without a suggested path forward?
Output Format
Validation Result: [Pass / Needs Revision / Fail]
Checks:
- Voice Compliance: [Pass/Fail] - [details]
- Tone: [Pass/Fail] - [details]
- Messaging: [Pass/Fail] - [details]
- Terminology: [Pass/Fail] - [issues found]
- Open Questions: [Pass/Fail] - [details]
- PII: [Pass/Fail]
Issues Found:
1. [Severity: Critical/Suggested] [description] -> Fix: [recommendation]
Overall: [summary]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 92 lines · 203 tokens per session scan A 49f61dcd82f2
quality-assurance is an agent published in the GitHub repository anthropics/knowledge-work-plugins (23,877 stars, last pushed today), licensed Apache-2.0. It adds 203 tokens to every session and 751 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.