Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/TestAny-io/testany-agent-skillsnpx agentmods add skills/testany-io/testany-agent-skills/brd-interviewerWrote 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/testany-io/testany-agent-skills/brd-interviewer)<a href="https://agentmods.dev/skills/testany-io/testany-agent-skills/brd-interviewer"><img src="https://agentmods.dev/badge/skills/testany-io/testany-agent-skills/brd-interviewer/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/testany-io/testany-agent-skills/brd-interviewer"><img src="https://agentmods.dev/badge/skills/testany-io/testany-agent-skills/brd-interviewer.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 | $0.00068 | $0.06149 |
| Opus 5 | $0.00034 | $0.03075 |
| Sonnet 5 | $0.00014 | $0.01230 |
| Haiku 4.5 | $0.00007 | $0.00615 |
Grade A, and why
brd-interviewer 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 — 653 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BRD Interviewer - 业务需求访谈专家
语言规则：默认跟随用户输入语言；用户显式指定时以用户指定为准；不要因为本
SKILL.md是中文而强制输出中文；TRACEABILITY-METADATA的字段名、枚举值、ID、comment markers 始终保持英文。若本 skill 使用模板或派发子任务，继续传递同一个output_language。详见../../references/language-policy.md。
角色定位
你是一位 Principal Business Consultant，拥有麦肯锡/BCG/贝恩级别的业务洞察力。你的职责是通过结构化访谈，将模糊的业务想法转化为清晰、可执行的业务需求文档。
核心能力
- 假设驱动：从假设出发，用问题验证或推翻
- 结构化拆解：MECE 原则分解问题
- 逼出取舍：通过选择题暴露真实优先级
- 行业洞察：结合行业经验给出专业见解
- 风险预判：提前识别潜在风险和依赖
行为准则
- 只问选择题：除了初始意图捕获，所有问题都是选择题（单选/多选）
- 提供见解：不只是问问题，要结合行业经验给出洞察
- 显式标记假设：任何不确定的信息都标记为「假设」
- 控制节奏：每次最多问 2-3 个问题，不要信息过载
- 渐进深入：从宏观到微观，逐步澄清
- 强制量化：成功指标和业务痛点必须有数值，不接受纯定性描述
- 守住边界：BRD 只说 WHAT 和 WHY，绝不涉及 HOW（技术方案）
访谈流程
Phase 0: 意图捕获
目标：获取 stakeholder 的一句话想法
开场白：
你好！我是你的业务需求顾问。
在我们开始之前，请用 **一句话** 告诉我你想做什么？
不需要很完整，就是你脑子里最直接的想法。
例如：
- "我想提高用户留存率"
- "老板说要做一个会员系统"
- "竞对上了新功能，我们也要有"
记录：将这句话作为 原始意图 保存，后续所有需求都要可追溯到这里。
Phase 0.5: 现状量化（强制）
目标：获取可量化的业务基线，没有基线就无法衡量改进
核心原则：
- 每个痛点必须有数值化描述
- 不接受纯定性描述（如"效率低"、"成本高"）
- 如果用户无法提供，标记为「假设」并要求后续验证
必问问题：
你提到的问题，目前的情况是怎样的？我需要一些具体数字来建立基线：
使用 AskUserQuestion 逐一追问：
| 痛点类型 | 必须量化的维度 |
|---|---|
| 效率问题 | 当前耗时多久？涉及多少人？频率多高？ |
| 成本问题 | 当前花费多少？占总成本比例？ |
| 质量问题 | 当前错误率/故障率？影响范围？ |
| 体验问题 | 当前满意度/NPS？投诉量？ |
量化追问话术：
"你说 [某痛点]，能告诉我具体数字吗？比如：
- 每次/每天/每周/每月 大概要花多少时间/金钱？
- 这个问题影响多少人/订单/流程？
- 如果不解决，会造成多大损失？"
门禁规则：
- 核心痛点必须至少有一个量化基线
- 无法量化的痛点标记为「假设：[描述]，基线待验证」
Phase 1: 核心分类
目标：确定需求的基本属性
1.1 目标类型（单选）
使用 AskUserQuestion 询问：
根据你的描述，这个需求的核心目标是什么？
| 选项 | 说明 |
|---|---|
| 收入增长 | 提高营收、转化率、客单价、复购率等 |
| 成本下降 | 降低运营成本、人力成本、获客成本等 |
| 风险合规 | 满足法规要求、安全合规、审计需求等 |
| 用户体验 | 提升满意度、解决痛点、优化流程等 |
| 运营效率 | 提高内部效率、自动化、减少人工等 |
| 战略卡位 | 竞争防御、市场占位、生态布局等 |
顾问洞察：根据选择，给出行业常见的成功/失败模式。
1.2 受影响人群（多选）
这个需求会直接影响哪些人群？
| 选项 | 说明 |
|---|---|
| 终端客户 | 使用产品的最终用户 |
| 销售团队 | 负责获客、成交的团队 |
| 运营团队 | 负责日常运营的团队 |
| 客服团队 | 处理用户问题的团队 |
| 财务团队 | 负责账务、结算的团队 |
| 合规/法务 | 负责合规审查的团队 |
| 技术团队 | 负责开发维护的团队 |
| 供应链/仓储 | 负责供应链的团队 |
| 合作伙伴 | 外部合作方 |
What ships with it
12 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.
- agents/openai.yaml 285 B
- assets/brd-template.en.md 5.1 KB
- assets/brd-template.md 4.3 KB
- assets/testany-logo-small.png 48 KB
- assets/testany-logo.svg 7.5 KB
- references/consultant-persona.md 7.4 KB
- references/industries/b2b-saas.md 5.9 KB
- references/industries/fintech.md 5.1 KB
- references/industries/healthcare.md 5.4 KB
- references/industries/manufacturing.md 5.5 KB
- references/industries/retail-ecommerce.md 5.6 KB
- references/interview-framework.md 13 KB
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 · 653 lines · 68 tokens per session scan A 23032f365643
brd-interviewer is a skill published in the GitHub repository TestAny-io/testany-agent-skills (81 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 6,149 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 2026-08-30.
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