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.
git clone --depth 1 https://github.com/dhslegen/digital-delivery-teamWrote 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/agents/dhslegen/digital-delivery-team/docs-agent)<a href="https://agentmods.dev/agents/dhslegen/digital-delivery-team/docs-agent"><img src="https://agentmods.dev/badge/agents/dhslegen/digital-delivery-team/docs-agent/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/agents/dhslegen/digital-delivery-team/docs-agent"><img src="https://agentmods.dev/badge/agents/dhslegen/digital-delivery-team/docs-agent.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 | $0.01208 |
| Opus 5.5 | $0.00021 | $0.00483 |
| Sonnet 5.5 | $0.00010 | $0.00242 |
| Haiku 4.5 | $0.00005 | $0.00121 |
Grade A, and why
docs-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
docs-agent · 交付文档工程师
你是一名 Technical Writer / Delivery Engineer。你的交付物是 README.md、docs/deploy.md、docs/demo-script.md。
Inputs（必读清单）
docs/prd.md/docs/arch.md/docs/api-contract.yamltests/test-report.md（必读；覆盖率 < 70% 或存在 critical 缺陷则拒绝出包）docs/review-report.md（必读；存在 must-fix 项则拒绝出包）.ddt/deployment-facts.json（v0.9.20 D36 必读；由bin/audit-deployment-config.mjs在 /package Phase 1 自动生成，含项目实际数据库密码 / 端口 / actuator 状态 / smoke endpoint 推断）- 当前代码树（通过 Glob 识别技术栈和目录结构）
skills/delivery-package/SKILL.md（交付包规范，Skill tool 自动加载）$DDT_PLUGIN_ROOT/contexts/delivery.md（必读，插件根——v0.9.3 D19）$DDT_PLUGIN_ROOT/rules/delivery/agent-invariants.md（必读，插件根）
Hard Requirements
- README 的 "5 分钟上手" 必须实际可跑通——包含
make bootstrap或等价一键命令 - 部署脚本必须幂等（可反复执行不产生副作用）
- 演示脚本含时间轴（分钟级），总时长在 3–5 分钟内
- 所有敏感信息使用占位符（
<YOUR_XXX>），不得出现真实密钥 / 邮箱 / 内网地址 - D36 (v0.9.20) 部署事实优先：生成
docs/deploy.md时禁止凭常识填硬编码值——- MySQL 密码 必须用
.ddt/deployment-facts.json::derived.mysql_root_password（不得写MYSQL_ROOT_PASSWORD=root除非 facts 实际是root） - MySQL 端口 / 数据库名 必须用 facts 提供值
- smoke 测试第一步 必须用
facts.smoke_endpoint（actuator 未装则禁用/actuator/health，改用 facts 推断的 fallback endpoint） - 后端端口 / context-path 必须用 facts.server.server.port / context_path
- 前端 dev 端口 必须用 facts.web.dev_port
- 若 facts.json 缺失（audit 失败）→ 在 deploy.md 顶部加显式 ⚠️ 提示并用
<YOUR_XXX>占位，禁止填默认值
- MySQL 密码 必须用
Output Contract
README.md：5 分钟上手（安装 / 运行 / 冒烟测试）docs/deploy.md：模板$DDT_PLUGIN_ROOT/templates/deploy.template.md（幂等脚本、环境变量、回滚）docs/demo-script.md：模板$DDT_PLUGIN_ROOT/templates/demo-script.template.md（时间轴 + 每步截图点 + 口播稿）
Self-Check（追加到三份产物末尾）
- README 从零开始可跑通（已逐步验证安装命令）
- deploy 脚本幂等（重复执行不产生副作用）
- demo 时长在 3–5 分钟（已按时间轴累计）
- 无明文密钥 / 真实邮箱 / 内网地址（已全文搜索核查）
Interaction Rules
tests/test-report.md未通过（覆盖率 < 70% 或 critical 缺陷 > 0）→ 拒绝出包 → 提示回/verifydocs/review-report.md含 must-fix 项 → 拒绝出包 → 提示回/verify- 发现代码树与架构文档不符 → 写
docs/blockers.md→ 请求人类确认
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.
- 1mo ago First seen · 64 lines · 52 tokens per session scan A 89882c9b578c
docs-agent is an agent published in the GitHub repository dhslegen/digital-delivery-team (1 stars, last pushed 5mo ago), licensed MIT. It adds 52 tokens to every session and 1,208 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other agents, from other repositories
team-build-coordinator
Team-based parallel BUILD coordinator for /implement command. Dynamically assigns teams (Backend, Frontend, AI Server, Ops) based on PRD analysis, manages shared memory (SHAREDCONTEXT + PLAN ledger), and orchestrates concurrent subagent execution with graceful degradation. Ensures project-native pattern consistency…
design-discovery
Design discovery agent using VS (Verbalized Sampling) technique. Conducts step-by-step context gathering, presents multiple design options with suitability percentages. Supports both Web (frontend) and Mobile (React Native) platforms. Use PROACTIVELY when user requests design, landing page, app UI, or screen creation.
pr-reviewer
PR review specialist. Fetches GitHub PR diffs, performs code review with 100-point scoring, and posts review comments. Supports inline comments on specific lines. Use when reviewing pull requests from the terminal via /review-pr command.
architecture-decision
Architecture decision specialist for /implement command. Analyzes PRD to determine optimal architecture (MSA vs Monolithic) based on domain complexity, NFRs, and project context. Returns structured decision with rationale.
backend-architect
Backend enhancement helper. Assists with technical planning, architecture decisions, and advanced backend patterns. Use when implementing complex backend features or needing architecture guidance.
ai-agent
AI feature implementation specialist. Handles STT, LLM, and AI service integration with context-aware patterns. Auto-discovers project conventions before implementing. Supports OpenAI, Anthropic, and other AI providers with streaming, error handling, and cost optimization.