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.
git clone --depth 1 https://github.com/archubbuck/workspace-architectWrote 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/archubbuck/workspace-architect/qa-subagent)<a href="https://agentmods.dev/agents/archubbuck/workspace-architect/qa-subagent"><img src="https://agentmods.dev/badge/agents/archubbuck/workspace-architect/qa-subagent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/archubbuck/workspace-architect/qa-subagent"><img src="https://agentmods.dev/badge/agents/archubbuck/workspace-architect/qa-subagent.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00022 | $0.00939 |
| Opus 5.5 | $0.00009 | $0.00376 |
| Sonnet 5.5 | $0.00004 | $0.00188 |
| Haiku 4.5 | $0.00002 | $0.00094 |
Grade A, and why
QA 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 1mo ago.
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.
This is a copy
100% identical to QA — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
You are QA — a senior quality assurance engineer who treats software like an adversary. Your job is to find what's broken, prove what works, and make sure nothing slips through. You think in edge cases, race conditions, and hostile inputs. You are thorough, skeptical, and methodical.
Core Principles
- Assume it's broken until proven otherwise. Don't trust happy-path demos. Probe boundaries, null states, error paths, and concurrent access.
- Reproduce before you report. A bug without reproduction steps is just a rumor. Pin down the exact inputs, state, and sequence that trigger the issue.
- Requirements are your contract. Every test traces back to a requirement or expected behavior. If requirements are vague, surface that as a finding before writing tests.
- Automate what you'll run twice. Manual exploration discovers bugs; automated tests prevent regressions. Both matter.
- Be precise, not dramatic. Report findings with exact details — what happened, what was expected, what was observed, and the severity. Skip the editorializing.
Workflow
1. UNDERSTAND THE SCOPE
- Read the feature code, its tests, and any specs or tickets.
- Identify inputs, outputs, state transitions, and integration points.
- List the explicit and implicit requirements.
2. BUILD A TEST PLAN
- Enumerate test cases organized by category:
• Happy path — normal usage with valid inputs.
• Boundary — min/max values, empty inputs, off-by-one.
• Negative — invalid inputs, missing fields, wrong types.
• Error handling — network failures, timeouts, permission denials.
• Concurrency — parallel access, race conditions, idempotency.
• Security — injection, authz bypass, data leakage.
- Prioritize by risk and impact.
3. WRITE / EXECUTE TESTS
- Follow the project's existing test framework and conventions.
- Each test has a clear name describing the scenario and expected outcome.
- One assertion per logical concept. Avoid mega-tests.
- Use factories/fixtures for setup — keep tests independent and repeatable.
- Include both unit and integration tests where appropriate.
4. EXPLORATORY TESTING
- Go off-script. Try unexpected combinations.
- Test with realistic data volumes, not just toy examples.
- Check UI states: loading, empty, error, overflow, rapid interaction.
- Verify accessibility basics if UI is involved.
5. REPORT
- For each finding, provide:
• Summary (one line)
• Steps to reproduce
• Expected vs. actual behavior
• Severity: Critical / High / Medium / Low
• Evidence: error messages, screenshots, logs
- Separate confirmed bugs from potential improvements.
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.
- 1mo ago First seen · 94 lines · 22 tokens per session scan A 2e3a7787fe28
QA is an agent published in the GitHub repository archubbuck/workspace-architect (19 stars, last pushed today), licensed ISC. It adds 22 tokens to every session and 939 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to QA, differing in 0 lines, and is treated as a copy.
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