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 team-attention/plugins-for-claude-natives --skill vaguegit clone --depth 1 https://github.com/team-attention/plugins-for-claude-nativesWrote 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/team-attention/plugins-for-claude-natives/vague)<a href="https://agentmods.dev/skills/team-attention/plugins-for-claude-natives/vague"><img src="https://agentmods.dev/badge/skills/team-attention/plugins-for-claude-natives/vague/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/team-attention/plugins-for-claude-natives/vague"><img src="https://agentmods.dev/badge/skills/team-attention/plugins-for-claude-natives/vague.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 | $0.00103 | $0.00936 |
| Opus 5 | $0.00051 | $0.00468 |
| Sonnet 5 | $0.00021 | $0.00187 |
| Haiku 4.5 | $0.00010 | $0.00094 |
Grade A, and why
vague 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 10d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vague: Requirement Clarification
Transform vague or ambiguous requirements into precise, actionable specifications through hypothesis-driven questioning. ALWAYS use the AskUserQuestion tool — never ask clarifying questions in plain text.
When to Use
- Ambiguous feature requests ("add a login feature")
- Incomplete bug reports ("the export is broken")
- Underspecified tasks ("make the app faster")
For strategy/planning blind spot analysis, use the unknown skill. For content-vs-form reframing, use the metamedium skill.
Core Principle: Hypotheses as Options
Present plausible interpretations as options instead of asking open questions. Each option is a testable hypothesis about what the user actually means.
BAD: "What kind of login do you want?" ← open question, high cognitive load
GOOD: "OAuth / Email+Password / SSO / Magic link" ← pick one, lower load
Protocol
Phase 1: Capture and Diagnose
Record the original requirement verbatim. Identify ambiguities:
- What is unclear or underspecified?
- What assumptions would need to be made?
- What decisions are left to interpretation?
Phase 2: Iterative Clarification
Use AskUserQuestion to resolve ambiguities. Batch up to 4 related questions per call. Each option is a hypothesis about what the user means.
Cap: 5-8 total questions. Stop when all critical ambiguities are resolved, OR user indicates "good enough", OR cap reached.
Example AskUserQuestion call:
questions:
- question: "Which authentication method should the login use?"
header: "Auth method"
options:
- label: "Email + Password"
description: "Traditional signup with email verification"
- label: "OAuth (Google/GitHub)"
description: "Delegated auth, no password management needed"
- label: "Magic link"
description: "Passwordless email-based login"
multiSelect: false
- question: "What should happen after registration?"
header: "Post-signup"
options:
- label: "Immediate access"
description: "User can use the app right away"
- label: "Email verification first"
description: "Must confirm email before access"
multiSelect: false
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.
- 10d ago First seen · 110 lines · 103 tokens per session scan A 2694637500f6
vague is a skill published in the GitHub repository team-attention/plugins-for-claude-natives (824 stars, last pushed 4mo ago), licensed MIT. It adds 103 tokens to every session and 936 once invoked, about $0.0005 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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