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 wjgoarxiv/autoresearch-skill --skill predictgit clone --depth 1 https://github.com/wjgoarxiv/autoresearch-skillWrote 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/wjgoarxiv/autoresearch-skill/predict)<a href="https://agentmods.dev/skills/wjgoarxiv/autoresearch-skill/predict"><img src="https://agentmods.dev/badge/skills/wjgoarxiv/autoresearch-skill/predict/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/wjgoarxiv/autoresearch-skill/predict"><img src="https://agentmods.dev/badge/skills/wjgoarxiv/autoresearch-skill/predict.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 | $0.00099 | $0.02200 |
| Opus 5 | $0.00049 | $0.01100 |
| Sonnet 5 | $0.00020 | $0.00440 |
| Haiku 4.5 | $0.00010 | $0.00220 |
Grade A, and why
autoresearch:predict 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 9d 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Predict: Multi-Perspective Deliberation Engine
A structured deliberation protocol that forces genuine disagreement before synthesis. Inspired by structured analytic techniques (SATs) used in intelligence analysis to counter groupthink.
Autonomy Directive
You are an autonomous deliberation agent. Once the deliberation begins:
- NEVER STOP between phases to ask for permission.
- NEVER collapse positions early — each persona must reason independently before seeing others.
- NEVER let the judge synthesize before all rounds are complete.
- Run all 8 phases sequentially without interruption. The user may have walked away.
The 8-Phase Deliberation Protocol
[Question] --> [Frame] --> [Personas] --> [Independent Positions]
--> [Position Summary] --> [Cross-Examination] --> [Rebuttal]
--> [Anti-Herd Detection] --> [Judge Synthesis] --> [predict-report.md]
Phase 1 — Frame the Question Precisely
Before any persona speaks, the agent must sharpen the question:
- Restate the question in unambiguous terms. Remove vagueness.
- Identify the decision horizon (short-term? 5 years? upon release?).
- Define measurable outcomes where possible ("will X exceed Y by date Z").
- List what the question does NOT include (scope boundaries).
- State what a correct prediction would look like — what evidence would confirm or deny it.
Log the framed question to predict-report.md under ## Framed Question.
Phase 2 — Enumerate Personas
Select 4–6 personas from persona-templates.md based on the question domain.
Selection rules:
- Always include at least one Optimist and one Pessimist for baseline polarity.
- Always include at least one domain Expert for technical grounding.
- For decisions with tail risks, add the Black Swan Hunter.
- For questions with a consensus-leaning answer, add the Devil's Advocate to stress-test it.
- For technical architecture decisions, swap in: Architect, Security Engineer, Product Manager, Operations.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 260 lines · 99 tokens per session scan A f8fc62fec8a0
autoresearch:predict is a skill published in the GitHub repository wjgoarxiv/autoresearch-skill (32 stars, last pushed 2mo ago), licensed MIT. It adds 99 tokens to every session and 2,200 once invoked, about $0.0005 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 2026-08-30.
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