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/commands/dhslegen/digital-delivery-team/relay)<a href="https://agentmods.dev/commands/dhslegen/digital-delivery-team/relay"><img src="https://agentmods.dev/badge/commands/dhslegen/digital-delivery-team/relay/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/commands/dhslegen/digital-delivery-team/relay"><img src="https://agentmods.dev/badge/commands/dhslegen/digital-delivery-team/relay.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 | $0.00039 | $0.01056 |
| Opus 5.5 | $0.00016 | $0.00422 |
| Sonnet 5.5 | $0.00008 | $0.00211 |
| Haiku 4.5 | $0.00004 | $0.00106 |
Grade B, and why
relay scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
[ -f "$DDT_PLUGIN_ROOT/bin/aggregate.mjs" ] || DDT_PLUGIN_ROOT=$(cat "${HOME}/.claude/delivery-metrics/.ddt-plugin-root" 2>/dev/null) How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/relay
跨会话 / 跨 AI 接力不失忆。一键生成 AI 友好的 prompt,用户复制到下一会话即可无缝续作。
何时使用
- 当前会话 token 接近上限,需要切到新会话
- 把项目交给同事 / 自己换设备
- 切换 AI 模型(Claude → GPT → Gemini)
- 长项目阶段性归档备份
执行
[ -f "$DDT_PLUGIN_ROOT/bin/aggregate.mjs" ] || DDT_PLUGIN_ROOT=$(cat "${HOME}/.claude/delivery-metrics/.ddt-plugin-root" 2>/dev/null)
[ -f "$DDT_PLUGIN_ROOT/bin/aggregate.mjs" ] || DDT_PLUGIN_ROOT="${HOME}/.claude/plugins/marketplaces/digital-delivery-team"
[ -f "$DDT_PLUGIN_ROOT/bin/aggregate.mjs" ] || { echo "❌ DDT plugin root 未解析。可能原因:(1) 插件未安装;(2) shell 中 DDT_PLUGIN_ROOT 指向无效路径,请 unset DDT_PLUGIN_ROOT 后重启会话;(3) 运行 /digital-delivery-team:doctor 自检"; exit 1; }
export DDT_PLUGIN_ROOT
node "$DDT_PLUGIN_ROOT/bin/build-relay-prompt.mjs" $ARGUMENTS
输出位置:
- 屏幕:完整 prompt(用户可一键复制)
- 文件:
.ddt/relay-<YYYYMMDD-HHMMSS>.md(归档备份)
13 段式模板
DDT 自动填的 4 段:
- 项目背景(项目 ID / 当前 phase / 已完成 phase / 技术栈摘要 / 关键产物路径)
- Git 摘要(分支 / 最近 5 commits / 未提交改动)
- 续作必做指南(接力 AI 第一步做什么)
- 元数据(生成时间戳 / DDT 版本)
LLM 在生成 prompt 时必须补充的 9 段:
- What We Are Building — 项目目标
- What WORKED (with evidence) — 已验证有效(带证据)
- What Did NOT Work (and why) — 失败尝试 + 精确原因(最关键)
- What Has NOT Been Tried Yet — 未尝试方向
- Current State of Files — 文件状态表
- Decisions Made — 关键决策 + 理由
- Blockers & Open Questions — 阻塞与未答问题
- Exact Next Step — 下一步精确指令
- Environment & Setup Notes(可选)— 启动命令 / 环境变量
使用示例
跨会话续作
# 旧会话
/relay
# 屏幕显示完整 prompt,用户 Cmd+A 全选 + Cmd+C 复制
# 新会话(同设备 / 不同设备 / 不同 AI 都可)
[粘贴 prompt 到第一句对话]
# AI 立即知道:项目在哪、做到哪步、下一步做什么、什么不要重试
与同事交接
/relay --out shared/handoff-2026-04-29.md
# 文件可直接给同事;同事在他的 Claude Code 里粘贴文件内容续作
切换 AI 模型
/relay --quiet --out /tmp/relay.md
cat /tmp/relay.md | pbcopy # macOS 复制到剪贴板
# 在 ChatGPT / Gemini 里粘贴 → AI 接手
与 /resume 的差异
| 命令 | 用途 | 输出 |
|---|---|---|
/digital-delivery-team:resume |
同会话自动恢复 | 屏幕(briefing) |
/digital-delivery-team:relay |
跨会话/跨设备/跨 AI 接力 | prompt 文件 + 屏幕 |
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 · 98 lines · 39 tokens per session scan B 2636705bc2f3
relay is a command published in the GitHub repository dhslegen/digital-delivery-team (1 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 1,056 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
recall-save
Generate / overwrite .recall/context.md with Recall's local offline summarizer.
show
Show this project's saved Recall context (.recall/context.md).
context
Analyze and optimize Claude Code's context configuration (analyze, optimize, or reset).
ccc-context-budget
Show how full the context window is, which components use the most of it, and when to save the session and compact. Use when the session feels slow, long or near its context limit.
check-versions
Audit dependencies against upstream registries (npm, pypi, nuget, crates.io).
pin
Break checkpoint — status display, session log, optional MEMORY.md update.