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 yqi96/warranted --skill auto-researchgit clone --depth 1 https://github.com/yqi96/warrantedWrote 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/yqi96/warranted/auto-research)<a href="https://agentmods.dev/skills/yqi96/warranted/auto-research"><img src="https://agentmods.dev/badge/skills/yqi96/warranted/auto-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/yqi96/warranted/auto-research"><img src="https://agentmods.dev/badge/skills/yqi96/warranted/auto-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 | $0.00068 | $0.02810 |
| Opus 5.5 | $0.00027 | $0.01124 |
| Sonnet 5.5 | $0.00014 | $0.00562 |
| Haiku 4.5 | $0.00007 | $0.00281 |
Grade A, and why
auto-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 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.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Channel
You are handed a research goal plus reference material and data, and no fixed argument to extract. The graph is the counterweight. Every question you raise gets a structural home, and every conclusion must be earned through proposition content, evidence, warrant, rebuttal handling, and qualifier judgment - never asserted because the analysis "looked right."
Current Warranted graphs use one node type: Proposition. Each proposition has five slots:
contentevidencewith local attachments and/or referenced proposition idswarrant, either inline text or a promoted propositionrebuttals, represented by propositions that attack content or warrantqualifier:refuted,unestablished,possibly,probably, orcertainly
The persistent graph lives in .toulmin/graph.db. Use the current 11-tool surface: create_propositions, update_proposition, set_qualifier, promote_warrant, delete_proposition, get_argument, find_propositions, get_stats, get_history, review, and dismiss. Do not invent retired node-type helper tools or retired completion gates.
Framing Router
The first decision is where the proposition targets come from. Test each input assertion: could it be shown false?
- Weak Framing: the input already states a specific question or a specific expected answer to test. Cast the question's answer as a proposition with
create_propositions. If the user supplied an expected answer, record it as a proposition too, usually withunestablisheduntil evidence exists. Do not re-decompose; the target is given, so proceed like a bounded reproduction. - Strong Framing: the input is a research goal or direction with no pre-stated falsifiable answer. Decompose it into a small set of answerable research questions, then create one load-bearing proposition per answer whose qualifier matters. Because nothing external fixes the target, the falsification obligation below is mandatory before any supporting evidence run.
- Mixed input — apply Weak Framing to the stated parts and Strong Framing to the gaps.
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 Changed · +11 lines · -2 tokens per session ee38f7dc1321
- 1mo ago First seen · 126 lines · 70 tokens per session scan A 41cb82b5a5a4
auto-research is a skill published in the GitHub repository yqi96/warranted (2 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 2,810 once invoked, about $0.0003 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.
Other skills, from other repositories
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idea-creator
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research-refine
A research-planning guide that turns a broad research idea into a specific method and a small validation plan.