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 agentmods add skills/jongwony/epistemic-protocols/probenpx skills add jongwony/epistemic-protocols --skill probegit 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/probe)<a href="https://agentmods.dev/skills/jongwony/epistemic-protocols/probe"><img src="https://agentmods.dev/badge/skills/jongwony/epistemic-protocols/probe.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.06463 |
| Opus 5 | $0.00016 | $0.03231 |
| Sonnet 5 | $0.00006 | $0.01293 |
| Haiku 4.5 | $0.00003 | $0.00646 |
Grade A, and why
probe 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 today.
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 — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Probe Skill
Deficit Recognition Probe — when the user is uncertain which epistemic deficit (and therefore which protocol) fits the current situation, surface AI-generated multi-hypothesis candidates with reverse-evidence conditions, and route on user-constituted recognition. Type: (DeficitUnrecognized, AI, RECOGNIZE, UserSituation) → ProtocolRoute.
Invoke directly with /probe when the user wants a fit review across the protocol catalog before committing to a single protocol invocation. The AI may also surface these hypotheses on its own when it detects genuine deficit-ambiguity — offered as a low-confidence horizon for the user's fusion, not a covert frame (Rule 1); explicit invocation is one entry, not the only one.
Definition
Probe (ἐπίγνωσις, epígnōsis: knowing-upon, recognition of what was already there): A dialogical act of resolving a user's vague sense that "something is off" into a recognized deficit + protocol route, where AI scans the user's recent situation against the catalog of epistemic deficits, presents at minimum two hypotheses with falsification conditions, and the user constitutes the route via recognition — never AI-resolved scoring.
This skill stands in structural homology with Anamnesis (/recollect) — both realize the RECOGNIZE operation family. Anamnesis recognizes past context from vague recall; Probe recognizes present-situation deficit from vague unease. Anamnesis output is RecalledContext; Probe output is ProtocolRoute. Both treat user recognition as the constitutive act and refuse AI-side resolution.
When to Use
Invoke this skill when:
- The user feels something is off but does not yet name which epistemic deficit fits
- A protocol invocation is being considered, and the user wants a fit review across alternatives before committing
- Multiple plausible routes coexist (e.g., "is this a goal problem or a context problem?") and the user wants the alternatives surfaced explicitly with their reverse-evidence conditions
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.
- today Changed · -2 lines a2d3c882f961
- 5d ago First seen · 300 lines · 32 tokens per session scan A 6ee9f95220fb
probe is a skill published in the GitHub repository jongwony/epistemic-protocols (160 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 6,463 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
TaiyiForge 第 4 阶段 — UI/UX 契约，产出 UI-DESIGN.md。四端通用。.
taiyi-evolve
TaiyiForge 辅助 — 实现后架构与文档同步（architecture-sync）。OpenCode / Claude / Codex / Cursor 通用。.
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.
taiyi-diagram-c4
TaiyiForge 辅助 — 从代码反推 C4 架构文档（Observed/Inferred 分层 · Mermaid 真源）。OpenCode / Claude / Codex / Cursor 通用。.
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.