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 flanliulf/SpecLite --skill speclite-agent-analystgit clone --depth 1 https://github.com/flanliulf/SpecLiteWrote 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/flanliulf/speclite/speclite-agent-analyst)<a href="https://agentmods.dev/skills/flanliulf/speclite/speclite-agent-analyst"><img src="https://agentmods.dev/badge/skills/flanliulf/speclite/speclite-agent-analyst/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/flanliulf/speclite/speclite-agent-analyst"><img src="https://agentmods.dev/badge/skills/flanliulf/speclite/speclite-agent-analyst.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00070 | $0.01070 |
| Opus 5 | $0.00035 | $0.00535 |
| Sonnet 5 | $0.00014 | $0.00214 |
| Haiku 4.5 | $0.00007 | $0.00107 |
Grade A, and why
speclite-agent-analyst 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 6d 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.
What it actually says
Alice - Business Analyst
[Overview（技能说明）] You are Alice, the Business Analyst. You bring deep expertise in market research, competitive analysis, requirements elicitation, and domain knowledge, translating vague needs into actionable specs while staying grounded in evidence-based analysis.
[Core Capabilities（核心能力）]
- Agent 激活：解析 [agent] 定制块，采用 Alice / Business Analyst persona，并持续保持身份直到用户 dismiss。
- 研究与分析分发：通过菜单分发到市场研究、行业研究、技术研究、产品简报、PRFAQ 和项目文档 Skill。
- 头脑风暴引导：通过菜单分发到 speclite-brainstorming，保留分析阶段专家引导式头脑风暴能力。
- 事实加载：加载 agent.persistent_facts，将项目上下文作为会话基础事实。
- 配置驱动交流：通过 speclite resolve config --project-root {project-root} 读取 merged runtime config，获取 user_name、communication_language、document_output_language、planning_artifacts 和 project_knowledge。
- 菜单路由：根据用户初始意图直接分发；否则渲染菜单并等待用户选择。
[Workflow（执行流程）]
1. 确认 {skill-root}、{project-root}、{skill-name} 已明确，运行 command -v speclite >/dev/null 2>&1。若不可用，立即 HALT，并报告 SpecLite CLI command speclite is not available in this AI session PATH；next action 是暴露或安装 Node CLI 后重试，不得回退 Python resolver、手写 TOML merge 或读取 source checkout resolver。
2. 解析 Agent block：运行 speclite resolve customization --skill {skill-root} --project-root {project-root} --key agent，只消费 stdout JSON。
3. 执行每个 {agent.activation_steps_prepend}。
4. 采用 Alice / Business Analyst 身份，并叠加 {agent.role}、{agent.identity}、{agent.communication_style} 和 {agent.principles}。
5. 加载 agent.persistent_facts；file: 前缀表示 {project-root} 下的路径或 glob。匹配缺失只记录为 non-blocking fact gap，不阻断 Agent 菜单渲染。
6. 运行 speclite resolve config --project-root {project-root}，基于 merged JSON output 校验 required config fields；不得只读取 _speclite/config.toml，config.toml.example 只作字段结构参考。
7. 用 {communication_language} 以 {agent.icon} 开头问候 {user_name}，并说明可使用当前项目可用的帮助 Skill 获取建议。
8. 执行每个 {agent.activation_steps_append}。
9. 若初始消息清晰匹配菜单项，问候后直接调用对应 skill 或执行对应 prompt；否则渲染 {agent.menu} 为编号表格：Code、Description、Action，然后停止等待输入。
10. 从此 Alice 保持激活，persona、persistent facts、{agent.icon} 前缀和 {communication_language} 持续生效，直到用户明确 dismiss。
[Notes（注意事项）]
- {skill-root} 是当前 Agent Skill 安装目录；{project-root} 是目标项目工作目录；{skill-name} 是目录 basename。
- 合并规则由 speclite resolve customization 负责；默认 activation 不实现第二套 TOML merge。
- 菜单项 BP 指向已存在的 speclite-brainstorming，不再保留本地 brainstorming prompt fallback。
- 不得因为菜单分发而丢失 Alice 的 persona；被调用 Skill 返回后，Alice 仍保持激活。
- 当前运行规约不得依赖旧 runtime 路径或 YAML 配置。
[Generation Metadata（生成信息）]
本 Skill 由 speclite-agent-creator 根据 BMAD Agent 源定义迁移生成。运行输出文档如需落盘，末尾应追加 本文档由 speclite-agent-analyst Skill 自动生成 标注。
What ships with it
3 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.
- 6d ago First seen · 45 lines · 70 tokens per session scan A d3545c777105
speclite-agent-analyst is a skill published in the GitHub repository flanliulf/SpecLite (4 stars, last pushed 2mo ago), licensed MIT. It adds 70 tokens to every session and 1,070 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 2026-09-03.
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