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 attentiondotnet/davidondrej-skills --skill research-promptgit clone --depth 1 https://github.com/attentiondotnet/davidondrej-skillsWrote 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/attentiondotnet/davidondrej-skills/research-prompt)<a href="https://agentmods.dev/skills/attentiondotnet/davidondrej-skills/research-prompt"><img src="https://agentmods.dev/badge/skills/attentiondotnet/davidondrej-skills/research-prompt/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/attentiondotnet/davidondrej-skills/research-prompt"><img src="https://agentmods.dev/badge/skills/attentiondotnet/davidondrej-skills/research-prompt.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 | $0.00081 | $0.01012 |
| Opus 5 | $0.00041 | $0.00506 |
| Sonnet 5 | $0.00016 | $0.00202 |
| Haiku 4.5 | $0.00008 | $0.00101 |
Grade A, and why
research-prompt 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 12d 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 research-prompt — 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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Prompt
Goal: turn a vague research need into ONE self-contained paragraph that a researcher with zero prior knowledge of the project can act on with zero back-and-forth.
Rules
- One paragraph. No headers, no bullet list in the deliverable.
- Prompt the job, not the topic. Give search handles (timeframe, ranking, source type, decision logic) — not just a subject.
- Assume zero prior knowledge. Write for a researcher who has never heard of the project. Open by explaining, in plain English, what the project/product is, why it exists, and the current situation — so they understand what's going on, what we need, and why we need it.
- Lead with the goal + decision. Right after that explainer, state the single question the research must answer and the decision/use it informs.
- Embed all context. Names, dates, product, prior known facts, constraints. The researcher must not need to ask anything or guess.
- Number the sub-questions inline (1, 2, 3…) so coverage is explicit. Keep to 3–6. One mission per prompt — don't cram unrelated questions.
- State constraints. What to include, what to avoid (e.g. "only non-Chinese competitors", "no marketing fluff").
- Source hierarchy. Prefer primary sources (official docs, GitHub, papers, filings, changelogs); forums/X/Reddit are weak signal only, never factual proof.
- Contradiction handling. If sources conflict, separate confirmed facts / inference / unresolved uncertainty — don't force fake consensus. Flag low-confidence claims for verification.
- Completion bar (define "done"). Don't stop at the first plausible answer. Corroborate each key claim with multiple independent primary sources where they exist; where sources are scarce, say so explicitly instead of padding. Keep going until every numbered sub-question is covered to this bar.
- Gap round before finishing. Require a final self-critique pass: list gaps, contradictions, and any single-source claims, then run another round of searches to close them — repeat until clean.
- Constrain output hard, method loosely. Be strict on the deliverable; leave the search path flexible so the researcher can explore.
- Demand a fixed output per finding: source link + specific claim + one-line "why it matters / why a viewer should care".
- Verifiable, citable facts only. No opinions.
- Last sentence: instruct them to output everything into a single detailed markdown file.
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.
- 12d ago First seen · 43 lines · 81 tokens per session scan A d54545abdfbe
research-prompt is a skill published in the GitHub repository attentiondotnet/davidondrej-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 1,012 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to research-prompt, differing in 0 lines, and is treated as a copy.
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