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 Sounio-lang/sounio --skill epistemic-uncertainty-quantificationgit clone --depth 1 https://github.com/Sounio-lang/sounioWrote 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/sounio-lang/sounio/epistemic-uncertainty-quantification)<a href="https://agentmods.dev/skills/sounio-lang/sounio/epistemic-uncertainty-quantification"><img src="https://agentmods.dev/badge/skills/sounio-lang/sounio/epistemic-uncertainty-quantification/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/sounio-lang/sounio/epistemic-uncertainty-quantification"><img src="https://agentmods.dev/badge/skills/sounio-lang/sounio/epistemic-uncertainty-quantification.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 | $0.00062 | $0.01154 |
| Opus 5 | $0.00031 | $0.00577 |
| Sonnet 5 | $0.00012 | $0.00231 |
| Haiku 4.5 | $0.00006 | $0.00115 |
Grade A, and why
epistemic-uncertainty-quantification 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Epistemic Uncertainty Quantification
When to use this skill
Use this skill when you need to:
- Add new uncertainty representations beyond variance‑based
Knowledge<T>(e.g., confidence intervals, prediction intervals) - Incorporate non‑Gaussian error models (e.g., skew‑normal, Student‑t, mixture distributions)
- Implement Dempster‑Shafer or other evidence‑combination frameworks
- Validate that the new quantification methods remain GUM‑compliant
- Extend the standard library with advanced epistemic operators
When NOT to use this skill
- For basic uncertainty propagation that already works with
Knowledge<T>(use the existing epistemic library) - For changes unrelated to epistemic types (e.g., adding new numeric functions)
- For pure performance optimizations without semantic changes
Inputs required
- Clear specification of the new uncertainty representation (mathematical definition)
- References to relevant literature (GUM supplements, statistical textbooks)
- Expected behavior for common operations (addition, multiplication, transformation)
- Where the new functionality should live (
stdlib/epistemic/or a new submodule)
Workflow
-
Understand the existing epistemic foundation
- Read
stdlib/epistemic/knowledge.sioandstdlib/epistemic/gum.sio - Review the GUM compliance proofs in
docs/compiler/EPISTEMIC_GUM_COMPLIANCE.md - Examine the test suite
tests/epistemic/
- Read
-
Design the new representation
- Decide whether to extend
Knowledge<T>or create a parallel type (e.g.,IntervalKnowledge<T>) - Define the internal fields (point estimate, interval bounds, distribution parameters, etc.)
- Specify how confidence updates and provenance merging work
- Decide whether to extend
-
Implement the core type
- Create a new
.siofile instdlib/epistemic/(or a subdirectory) - Provide constructors, accessors, and basic arithmetic operations
- Ensure uncertainty propagation follows the required mathematical rules
- Create a new
-
Integrate with the type checker
- If the new type needs special checking rules, modify
self‑hosted/check/epistemic.sio - Add any necessary compiler diagnostics for misuse
- If the new type needs special checking rules, modify
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 · 128 lines · 62 tokens per session scan A 4257d646d06e
epistemic-uncertainty-quantification is a skill published in the GitHub repository Sounio-lang/sounio (6 stars, last pushed today), licensed Apache-2.0. It adds 62 tokens to every session and 1,154 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 2026-08-31.
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