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 mpaarating/ai-workflow-kit --skill researchgit clone --depth 1 https://github.com/mpaarating/ai-workflow-kitWrote 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/mpaarating/ai-workflow-kit/research)<a href="https://agentmods.dev/skills/mpaarating/ai-workflow-kit/research"><img src="https://agentmods.dev/badge/skills/mpaarating/ai-workflow-kit/research/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/mpaarating/ai-workflow-kit/research"><img src="https://agentmods.dev/badge/skills/mpaarating/ai-workflow-kit/research.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 | $0.00015 | $0.01314 |
| Opus 5.5 | $0.00006 | $0.00526 |
| Sonnet 5.5 | $0.00003 | $0.00263 |
| Haiku 4.5 | $0.00002 | $0.00131 |
Grade A, and why
research 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research
Trigger Phrases
- "research [topic]"
- "deep dive on [topic]"
- "synthesize [topic]"
Inputs
The user provides a topic or question:
- A broad topic: "research React Server Components"
- A specific question: "research how other teams handle feature flag cleanup"
- A comparison: "research Zustand vs Jotai for our use case"
- A codebase question: "research how auth middleware works in this repo"
Workflow
Step 1: Clarify Scope
Assess the topic. If the request is clear and specific, proceed immediately. If ambiguous, ask one clarifying question — no more.
Good (proceed immediately):
- "research how to implement rate limiting in Express"
- "deep dive on React 19 cache API"
Needs clarification:
- "research databases" — too broad. Ask: "What aspect? Choosing one, optimizing queries, migration strategies?"
- "research the bug" — no context. Ask: "Which bug? Point me to a ticket or error message."
Step 2: Search Sources in Parallel
Search all available sources simultaneously. Use whichever sources are accessible:
Web Search
- Search for the topic using web search
- Look for: official documentation, well-regarded blog posts, conference talks, GitHub discussions
- Prefer primary sources (official docs, RFCs, author posts) over secondary summaries
- Skip SEO-farm results and outdated content (check publication dates)
Codebase Search
- Search the current repository for related code, patterns, and prior art
- Use filename search and content search
- Look for: existing implementations, similar patterns, comments referencing the topic, related tests
- If the repo is part of a monorepo or org, note whether broader search might be useful
Docs Search
- Search
{{notes}}for internal documentation, decision records, or previous research on the topic - Look for: ADRs, RFCs, design docs, wiki pages, previous research briefs
Step 3: Evaluate and Filter Sources
For each source found:
- Assess relevance (directly addresses the topic vs. tangentially related)
- Assess credibility (official docs > well-known authors > random blog posts)
- Assess freshness (recent > old, especially for fast-moving topics)
- Discard sources that are low-relevance, outdated, or unreliable
- Keep the top 5-10 most valuable sources
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.
- 2mo ago First seen · 136 lines · 15 tokens per session scan A 39c131a9eb27
research is a skill published in the GitHub repository mpaarating/ai-workflow-kit (2 stars, last pushed 5mo ago), licensed MIT. It adds 15 tokens to every session and 1,314 once invoked, about $0.0001 per session on Opus 5.5. 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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