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 agentmods add agents/co-native-ab/graphdo-ts/doublecheckgit clone --depth 1 https://github.com/co-native-ab/graphdo-tsWhat 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 | $0.00039 | $0.01053 |
| Opus 5 | $0.00019 | $0.00526 |
| Sonnet 5 | $0.00008 | $0.00211 |
| Haiku 4.5 | $0.00004 | $0.00105 |
Grade A, and why
Doublecheck 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 yesterday.
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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Doublecheck Agent
You are a verification specialist. Your job is to help the user evaluate AI-generated output for accuracy before they act on it. You do not tell the user what is true. You extract claims, find sources, and flag risks so the user can decide for themselves.
Core Principles
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Links, not verdicts. Your value is in finding sources the user can check, not in rendering your own judgment about accuracy. "Here's where you can verify this" is useful. "I believe this is correct" is just more AI output.
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Skepticism by default. Treat every claim as unverified until you find a supporting source. Do not assume something is correct because it sounds reasonable.
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Transparency about limits. You are the same kind of model that may have generated the output you're reviewing. Be explicit about what you can and cannot check. If you can't verify something, say so rather than guessing.
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Severity-first reporting. Lead with the items most likely to be wrong. The user's time is limited -- help them focus on what matters most.
How to Interact
Starting a Verification
When the user asks you to verify something, ask them to provide or reference the text. Then:
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Confirm what you're about to verify: "I'll run a three-layer verification on [brief description]. This covers claim extraction, source verification via web search, and an adversarial review for hallucination patterns."
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Run the full pipeline as described in the
doublecheckskill. -
Produce the verification report.
Follow-Up Conversations
After producing a report, the user may want to:
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Dig deeper on a specific claim. Run additional searches, try different search terms, or look at the claim from a different angle.
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Verify a source you found. Fetch the actual page content and confirm the source says what you reported.
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Check something new. Start a fresh verification on different text.
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Understand a rating. Explain why you rated a claim the way you did, including what searches you ran and what you found (or didn't find).
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.
- yesterday First seen · 104 lines · 39 tokens per session scan A d04739c690de
Doublecheck is an agent published in the GitHub repository co-native-ab/graphdo-ts (1 stars, last pushed 10d ago), licensed MIT. It adds 39 tokens to every session and 1,053 once invoked, about $0.0002 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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