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 Benknightdark/neo-skills --skill neo-clarificationgit clone --depth 1 https://github.com/Benknightdark/neo-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/benknightdark/neo-skills/neo-clarification)<a href="https://agentmods.dev/skills/benknightdark/neo-skills/neo-clarification"><img src="https://agentmods.dev/badge/skills/benknightdark/neo-skills/neo-clarification/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/benknightdark/neo-skills/neo-clarification"><img src="https://agentmods.dev/badge/skills/benknightdark/neo-skills/neo-clarification.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 | $0.00041 | $0.00711 |
| Opus 5 | $0.00020 | $0.00356 |
| Sonnet 5 | $0.00008 | $0.00142 |
| Haiku 4.5 | $0.00004 | $0.00071 |
Grade A, and why
neo-clarification 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Requirement Clarification Specifications
Apply the Inversion & Generator Pattern. Follow this protocol strictly to translate raw, chaotic user complaints and screenshots into clean, structured system specifications.
1. Perceive Phase
-
Information Extraction:
- Carefully read the user's text and inspect any attached screenshots or logs.
- Filter out emotional noise, frustrations, and blame.
- Separate objective facts (what is currently happening or visible) from user expectations (what they wanted to accomplish).
-
Identify System Boundaries:
- Determine the scope, domain, and potential technical layers affected by the feedback (e.g., frontend rendering, network APIs, database states, permission groups).
2. Reason Phase
-
Load Analysis Framework:
- Always read the external analysis guide before starting your deduction: 5w1h-framework.md
-
Context Reconstruction (5W1H):
- Map the extracted facts to the 5W1H framework (Who, Where, When, What, Why, How).
- Formulate logical hypotheses on the root causes of UI anomalies or system behaviors.
-
Identify Gaps:
- Pinpoint critical missing information (e.g., browser environment, specific action steps, parameters, error logs).
- Prepare a list of clarifying questions to ask the user.
3. Act Phase
Generate a structured "Requirement Translation and Clarification Report" strictly in Traditional Chinese (Taiwan). Follow these steps:
-
Load Output Template:
- Read the standard markdown structure from: clarification-template.md
-
Compile the Report:
- Fill in the template using Traditional Chinese.
- Context Restoration: Present objective facts concisely without emotional adjectives.
- User Story: Use the strict format: "身為... 我想要... 以便於..."
- System Requirements & Hypotheses: Highlight key rendering, API, and validation checkpoints for the development team.
- Open Questions: List between 2 and 10 polite, precise, and constructive clarifying questions.
What ships with it
5 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.
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 · 71 lines · 41 tokens per session scan A 32180cd8b487
neo-clarification is a skill published in the GitHub repository Benknightdark/neo-skills (7 stars, last pushed 5d ago), licensed MIT. It adds 41 tokens to every session and 711 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 2026-08-31.
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