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 scenario-labs/skills --skill scenario-refine-loopgit clone --depth 1 https://github.com/scenario-labs/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/scenario-labs/skills/scenario-refine-loop)<a href="https://agentmods.dev/skills/scenario-labs/skills/scenario-refine-loop"><img src="https://agentmods.dev/badge/skills/scenario-labs/skills/scenario-refine-loop/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/scenario-labs/skills/scenario-refine-loop"><img src="https://agentmods.dev/badge/skills/scenario-labs/skills/scenario-refine-loop.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 | $0.00105 | $0.01237 |
| Opus 5 | $0.00053 | $0.00619 |
| Sonnet 5 | $0.00021 | $0.00247 |
| Haiku 4.5 | $0.00011 | $0.00124 |
Grade A, and why
scenario-refine-loop 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 10d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scenario Refine Loop
Overview
Agents fail generation QA in two symmetric ways: accepting the first roll, or rewording the whole prompt and re-rolling until the budget dies. Both skip the same two artifacts, a written rubric and a diagnosis. The loop that converges: rubric before generating, a small batch, a recorded verdict per asset, the cheapest targeted fix per failure, a hard round cap. Connection and the core loop: see the scenario skill. Critic tool contracts: scenario-asset-analysis. Baseline discipline: scenario-consistency. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.
Quick reference
| Step | Do |
|---|---|
| 1. Rubric | Before generating, turn the brief into pass/fail lines a viewer can check ("subject centered on a plain field"), never taste words |
| 2. Generate | The smallest batch that tests the recipe; dry_run when cost matters |
| 3. Critique | asset_analyze: up to 10 images per call, one instruction embedding the rubric and a fixed per-image output shape |
| 4. Fix | Route every fail line to the cheapest fix that addresses it (table below) |
| 5. Stop | A clean round ships; three rounds without one, or one line failing twice under different fixes, means report, not respin |
When the bar is the configured brand brief rather than a task rubric, and the team's Quality Gate add-on is enabled, critique images with asset_quality_gate_run instead: its reasons and suggestions feed the fix table directly (scenario-quality-gate; where the gate is missing it degrades to this asset_analyze path).
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.
- 10d ago First seen · 58 lines · 105 tokens per session scan A 4f1ef4ae0e23
scenario-refine-loop is a skill published in the GitHub repository scenario-labs/skills (11 stars, last pushed 6d ago), licensed MIT. It adds 105 tokens to every session and 1,237 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 2026-08-31.
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