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 simota/agent-skills --skill sagagit clone --depth 1 https://github.com/simota/agent-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/simota/agent-skills/saga)<a href="https://agentmods.dev/skills/simota/agent-skills/saga"><img src="https://agentmods.dev/badge/skills/simota/agent-skills/saga/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/simota/agent-skills/saga"><img src="https://agentmods.dev/badge/skills/simota/agent-skills/saga.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 | $0.00033 | $0.05560 |
| Opus 5 | $0.00016 | $0.02780 |
| Sonnet 5 | $0.00007 | $0.01112 |
| Haiku 4.5 | $0.00003 | $0.00556 |
Grade A, and why
saga 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 6d 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 — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Saga
Narrative design agent that tells product and feature use cases as customer-centric stories. Transforms data and specifications into "stories people can empathize with", creating shared understanding among teams, stakeholders, and users.
"Facts are remembered 5-10% of the time. Stories raise that to 65-70%. The customer is the hero. The product is the guide."
What ships with it
12 files 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.
- _common 10 B
- reference/_common 13 B
- reference/autorun-schema.md 1.5 KB
- reference/before-after-bridge.md 1.3 KB
- reference/echo 10 B
- reference/handoffs.md 4.8 KB
- reference/hero-journey.md 1.2 KB
- reference/minto-pyramid.md 1.2 KB
- reference/saga 10 B
- reference/spark 11 B
- reference/templates.md 7.7 KB
- reference/tri-engine-narrate.md 22 KB
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.
- 6d ago First seen · 340 lines · 33 tokens per session scan A 8b43577e12e7
saga is a skill published in the GitHub repository simota/agent-skills (76 stars, last pushed 7d ago), licensed MIT. It adds 33 tokens to every session and 5,560 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 2026-09-03.
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