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 BlueprintOS/analysis-to-delivery --skill setup-analysis-deliverygit clone --depth 1 https://github.com/BlueprintOS/analysis-to-deliveryWrote 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/blueprintos/analysis-to-delivery/setup-analysis-delivery)<a href="https://agentmods.dev/skills/blueprintos/analysis-to-delivery/setup-analysis-delivery"><img src="https://agentmods.dev/badge/skills/blueprintos/analysis-to-delivery/setup-analysis-delivery/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/blueprintos/analysis-to-delivery/setup-analysis-delivery"><img src="https://agentmods.dev/badge/skills/blueprintos/analysis-to-delivery/setup-analysis-delivery.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 | $0.00052 | $0.01275 |
| Opus 5 | $0.00026 | $0.00638 |
| Sonnet 5 | $0.00010 | $0.00255 |
| Haiku 4.5 | $0.00005 | $0.00128 |
Grade A, and why
setup-analysis-delivery 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 8d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setup Analysis-Delivery — 项目配置初始化
Contract
- 输入: 项目根目录,已检测到的项目文件,可选的既有项目配置
- 输出:
paths/knowledge-path.md、paths/compliance-path.md、paths/tech-stack-path.md、paths/doc-naming-path.md;可选decisions.mdADR - 门控: 4 个
paths/*.md文件存在且非空(legacy 项目根*-path.md可接受但产生 warning);knowledge-path.md至少含 1 个真实路径后才进入下游字段工作 - Required rules:
context-pointer - Required paths:
knowledge-path,compliance-path,tech-stack-path,doc-naming-path - 下一步:
/grill-task
适用场景
- 拿到一个新项目,需要接 analysis-to-delivery 工作流
- 给现有项目补这套工作流的配置
流程步骤
1. 检测项目根
- 必须在 git 仓库根执行(
git rev-parse --show-toplevel) - 项目根不存在
.git→ 提示用户先git init
2. 检测项目类型
读项目根文件,推断技术栈:
pom.xml/build.gradle→ Java/Maven/Gradlepackage.json→ Node/前端pyproject.toml/requirements.txt→ Pythongo.mod→ Go
3. 生成 4 个项目级配置(canonical)
默认:在项目根的 paths/ 目录下生成 4 个空模板(用户填写后提交到 git)。
这 4 个文件是唯一项目级配置加载输入:
| Canonical 文件 | 作用 | 模板来源 |
|---|---|---|
paths/knowledge-path.md |
列项目涉及的外部知识库(领域表结构、合规法规等)路径 | paths/knowledge-path.md |
paths/compliance-path.md |
列项目适用的合规规则文件路径 + 启用开关 | paths/compliance-path.md |
paths/tech-stack-path.md |
列后端/前端/数据库/中间件 + 团队规范路径 | paths/tech-stack-path.md |
paths/doc-naming-path.md |
文档编号、命名前缀、存放目录 | paths/doc-naming-path.md |
Legacy 兼容:既有 v1.1 项目可能用项目根 *.md(knowledge-path.md 等)。setup-check.py
会把它们识别为 warning 并继续通过。生成时加 --legacy 切换到兼容输出位置(仅 v1.1 旧项目)。
4. 可选记录配置使用 ADR
config-used.md不是配置文件,不参与配置加载,不属于 4 个项目级配置- 如需记录"本项目用了哪些配置、为什么这么选",从
templates/CONFIG_USED.md复制生成 config-used.md应作为阶段 1 交付产物提交,用于审计和交接
5. 验证
- 4 个文件存在且非空(允许只有注释)
- 跑
python3 scripts/setup-check.py --strict <project>确认通过(legacy 项目根文件会产生 warning 但仍通过) - 跑
python3 scripts/field-alignment-check.py --help确认脚本可用
调用的 rule
rules/context-pointer— 三层配置加载规则
结束条件
- 4 个
paths/*.md全部生成在项目根(或兼容的 legacy 项目根*.md) - 用户已填写真实内容(
paths/knowledge-path.md必须至少 1 个真实路径) - 如生成
config-used.md,已明确标注为配置使用记录 / ADR,而非配置输入 -
setup-check.py --strict <project>通过(warning 可接受) - 已提交到 git(可选,但建议)
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.
- 8d ago First seen · 88 lines · 52 tokens per session scan A 4dc61c8b2858
setup-analysis-delivery is a skill published in the GitHub repository BlueprintOS/analysis-to-delivery (26 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 1,275 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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