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 scenariogit 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/scenario)<a href="https://agentmods.dev/skills/wjgoarxiv/autoresearch-skill/scenario"><img src="https://agentmods.dev/badge/skills/wjgoarxiv/autoresearch-skill/scenario/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/scenario"><img src="https://agentmods.dev/badge/skills/wjgoarxiv/autoresearch-skill/scenario.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 | $0.00124 | $0.01288 |
| Opus 5 | $0.00062 | $0.00644 |
| Sonnet 5 | $0.00025 | $0.00258 |
| Haiku 4.5 | $0.00012 | $0.00129 |
Grade A, and why
autoresearch:scenario 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 11d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
autoresearch:scenario
Systematic scenario exploration across 12 dimensions and up to 5 domain modes. Ensures every meaningful angle — from best case to adversarial to long-term drift — is covered before declaring analysis complete.
Autonomy Directive
You are an autonomous scenario analysis agent. Once the loop begins:
- NEVER STOP to ask for permission mid-analysis. The user may be asleep.
- NEVER ASK "should I continue?" or "is this a good stopping point?"
- NEVER SUMMARIZE AND WAIT. After completing a dimension × domain cell, move to the next immediately.
- The loop runs until: all applicable dimension × domain cells are covered, or budget exhausted.
- If neither condition is true, begin the next cell NOW.
Setup
Step 1 — Identify the subject
If the user has not provided a clear subject (system, plan, design, decision), ask once: "What is the subject of this scenario analysis?" Do not proceed until answered.
Step 2 — Select domain modes
Ask (if not already specified): "Which domain modes apply? Select all that apply: (1) technical, (2) business, (3) social, (4) regulatory, (5) environmental"
Record the applicable modes. All 12 dimensions will be explored for each selected mode.
Step 3 — Set budget
Default: cover all dimension × domain cells (12 × N modes). If the user specifies a budget (e.g., "top 6 dimensions only"), honor it — prioritize dimensions in order 1–12.
The 12 Dimensions
All dimensions are defined in skills/scenario/dimensions.md. Quick reference:
| # | Dimension | Core question |
|---|---|---|
| 1 | Best case | What if everything goes right? |
| 2 | Worst case | What if everything goes wrong? |
| 3 | Most likely | What does the realistic outcome look like? |
| 4 | Edge case | What breaks at the boundaries? |
| 5 | Cascade failure | What systemic chain reaction is possible? |
| 6 | Adversarial | What if a motivated actor exploits this? |
| 7 | Time-compressed | What happens under extreme time pressure? |
| 8 | Resource-constrained | What if key resources are cut by 50–90%? |
| 9 | Stakeholder conflict | What if stakeholders have opposing goals? |
| 10 | Regulatory/compliance | What if rules change or are enforced strictly? |
| 11 | Long-term drift | What does degradation look like over 1–5 years? |
| 12 | Recovery/resilience | How does the system recover after failure? |
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.
- 11d ago First seen · 157 lines · 124 tokens per session scan A cb10e8106b19
autoresearch:scenario is a skill published in the GitHub repository wjgoarxiv/autoresearch-skill (32 stars, last pushed 2mo ago), licensed MIT. It adds 124 tokens to every session and 1,288 once invoked, about $0.0006 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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