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 jongwony/epistemic-protocols --skill white-beargit clone --depth 1 https://github.com/jongwony/epistemic-protocolsWrote 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/jongwony/epistemic-protocols/white-bear)<a href="https://agentmods.dev/skills/jongwony/epistemic-protocols/white-bear"><img src="https://agentmods.dev/badge/skills/jongwony/epistemic-protocols/white-bear.svg" alt="Measured on agentmods" 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 | $0.00046 | $0.02041 |
| Opus 5 | $0.00023 | $0.01020 |
| Sonnet 5 | $0.00009 | $0.00408 |
| Haiku 4.5 | $0.00005 | $0.00204 |
Grade A, and why
white-bear 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 9d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
White Bear Audit
A semantic audit of LLM-facing prose for the White Bear authoring principle: keep attention on the necessary path — a mention of a competing non-target (a forbidden act, a superseded path, a rejected alternative) earns its place only when it is load-bearing. Read-only — it emits structured findings and writes no fixes. The human author decides which to rewrite, mark as load-bearing, or dismiss.
Purpose
Surface prose that holds the model's attention on an unnecessary competing target, before it ships. Prohibition framing names a forbidden act, superseded-path mention names a retired path, negated anchoring names a rejected alternative — each keeps the non-target available as a competing action candidate. This drift survives deterministic structural checks — it is a meaning-level pattern, so a semantic reviewer catches what literal pattern matching cannot.
Inputs
Manual invocation only (interactive /white-bear):
- The caller passes target file paths or a glob; with no argument, the skill enumerates the in-scope set under the working tree HEAD.
- Files are read at their working-tree state — the post-edit, pre-commit content the author is about to ship.
Scope
In scope (LLM-facing prose where this principle applies):
- Skill instruction files (
*/skills/*/SKILL.md), considered outside formal/definition blocks - Agent system-prompt files (
*/agents/*.md) - Output-style files
Out of scope (positively framed by purpose, or the principle does not apply):
- Formal-definition blocks within instruction files — regions delimited by
── <NAME> ──headers (FLOW, MORPHISM, TYPES, PHASE TRANSITIONS, and peers). Notation patterns are the content there. - Fenced code blocks (
``` ... ```) — code is content, and example code attached to a definition is part of that definition. - Human-facing documentation (README files, design notes, reference material) — examples serve human comprehension there.
- Rule-tier and principle-tier prose authored for contributors — such prose admits intentional negative formulations as discriminant signals; a one-pass rewrite would erase calibration signals at intentionally preserved decision points.
- Session and context substrates outside this audit's surface.
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.
- 9d ago First seen · 112 lines · 46 tokens per session scan A 45dae5139ab9
white-bear is a skill published in the GitHub repository jongwony/epistemic-protocols (160 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 2,041 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 2026-08-30.
Other skills, from other repositories
taiyi-ui-design
A design-planning guide for describing how an application's user interface should look and behave. It produces a UI-DESIGN.md document covering layouts, components, interactions, accessibility, and error states.
taiyi-evolve
A workflow skill that compares the implemented code with the frozen design after development and testing. It records architecture changes and proposes updates to DESIGN.md, the document describing the intended system structure.
taiyi-diagram-c4
A code-scanning tool that builds C4 architecture documents from a repository. It separates facts observed in the code from conclusions inferred about the design and uses Mermaid diagrams as the source format.
flow-next-resolve-pr
Resolve PR review feedback. Fetches unresolved threads, triages, fixes, replies and resolves via GraphQL. Use when asked to address review comments.
flow-next-tracker-sync
Project a flow-next spec to a tracker issue (Linear, GitHub, GitLab, Jira) and reconcile two-way. Use when asked to sync to a tracker. NOT plan-sync.
writing-style
Use for technical communication - GitHub/GitLab tickets, PR/MR descriptions, issue comments, code review comments, commit messages. Direct, brief style with no AI-speak. NOT for README.md, public docs, or blog posts.