Borrowing it
Nothing to install: this file belongs to prime-radiant-inc/gauntlet. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/prime-radiant-inc/gauntlet/main/.claude/skills/writing-gauntlet-stories/SKILL.mdgit clone --depth 1 https://github.com/prime-radiant-inc/gauntletWrote 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/prime-radiant-inc/gauntlet/writing-gauntlet-stories)<a href="https://agentmods.dev/skills/prime-radiant-inc/gauntlet/writing-gauntlet-stories"><img src="https://agentmods.dev/badge/skills/prime-radiant-inc/gauntlet/writing-gauntlet-stories/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/prime-radiant-inc/gauntlet/writing-gauntlet-stories"><img src="https://agentmods.dev/badge/skills/prime-radiant-inc/gauntlet/writing-gauntlet-stories.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00070 | $0.02649 |
| Opus 5 | $0.00035 | $0.01324 |
| Sonnet 5 | $0.00014 | $0.00530 |
| Haiku 4.5 | $0.00007 | $0.00265 |
Grade A, and why
writing-gauntlet-stories 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 12d 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Gauntlet Stories
Thesis
A Gauntlet story is an outcome, scoped by a persona, graded by observable conditions — with the mechanics deliberately left out.
The agent figures out the how. The card specifies what counts as success in a way that distinguishes a real pass from a confident-sounding bluff.
If you remember nothing else: outcome + persona + observable post-conditions. No clicks, no selectors, no file paths.
Why this differs from the frames you already know
Three familiar frames look right and are wrong in contagious ways. Read these before you start writing — if you skip this, you will write the wrong shape.
- Agile user story (
As a X, I want Y, so that Z). Not this. The persona is not a stakeholder whose needs are being designed for. It is an identity the agent inhabits during the run, used to anchor inference into the context tree. - QA step-script (Step 1: navigate. Step 2: type. Step 3: assert). Not this. The whole point of an LLM in the loop is that the agent figures out the path. Enumerating steps recreates exactly what we are trying to escape.
- BDD Given/When/Then. Not this. Acceptance criteria here are end-state conditions, not consequents of a prescribed sequence. A Gauntlet AC presupposes nothing about how you got there.
If your card reads like any of these, rewrite it.
The rules
Each rule has a rationale (why) and a smell (what it looks like when you are violating it).
1. Open with persona binding, not a profile reference
Every card starts You are <Name>. Never Use the profile at profiles/x/profile.md.
- Why: the agent reads the context tree and infers which profile to use from the name. That inference is part of the test, especially when decoy profiles exist. Naming the path destroys the inference.
- Smell: any file path, any reference to a specific profile location, any phrase like "load the profile for…".
2. Describe outcomes, not actions
Verbs sit at intent level (sign in, post, find, confirm) — not mechanics level (click #login, press down-arrow twice, type email then password).
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.
- 12d ago First seen · 217 lines · 70 tokens per session scan A 4edfe79031cc
writing-gauntlet-stories is a skill published in the GitHub repository prime-radiant-inc/gauntlet (55 stars, last pushed yesterday), licensed Apache-2.0. It adds 70 tokens to every session and 2,649 once invoked, about $0.0003 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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