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 skills add swDomass/AI_orchestrator --skill research-qagit clone --depth 1 https://github.com/swDomass/AI_orchestratorWrote 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/skills/swdomass/ai_orchestrator/research-qa)<a href="https://agentmods.dev/skills/swdomass/ai_orchestrator/research-qa"><img src="https://agentmods.dev/badge/skills/swdomass/ai_orchestrator/research-qa/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/skills/swdomass/ai_orchestrator/research-qa"><img src="https://agentmods.dev/badge/skills/swdomass/ai_orchestrator/research-qa.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 | $0.00014 | $0.00244 |
| Opus 5 | $0.00007 | $0.00122 |
| Sonnet 5 | $0.00003 | $0.00049 |
| Haiku 4.5 | $0.00001 | $0.00024 |
Grade A, and why
research-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 10d 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.
What it actually says
System Prompt Addition
Perform a structured pre-implementation research workflow:
-
Discovery: Explore the codebase thoroughly — read docs, directory structure, relevant source files, tests, configs, git history. Understand the project's architecture, patterns, and conventions. Do NOT modify any files.
-
Analysis: Think deeply about implementation approaches (2-3 alternatives), required changes, data/API impact, security, performance, testing strategy, risks, and edge cases. Do NOT modify any files.
-
Question Generation: Produce a prioritized list of questions the developer must answer before starting. Mark critical blockers with [BLOCKING]. Reference concrete code where relevant. Suggest options where possible.
Output written to .research-qa/. This is a read-only tool — no code changes.
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.
- 10d ago First seen · 31 lines · 14 tokens per session scan A c119b1458c03
research-qa is a skill published in the GitHub repository swDomass/AI_orchestrator (5 stars, last pushed 4d ago), licensed MIT. It adds 14 tokens to every session and 244 once invoked, about $0.0001 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 2026-08-31.
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