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 agentmods add skills/oneworks-ai/app/standard-dev-flownpx skills add oneworks-ai/app --skill standard-dev-flowgit clone --depth 1 https://github.com/oneworks-ai/appWhat 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 | $0.00021 | $0.01183 |
| Opus 5 | $0.00010 | $0.00592 |
| Sonnet 5 | $0.00004 | $0.00237 |
| Haiku 4.5 | $0.00002 | $0.00118 |
Grade A, and why
standard-dev-flow 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 2d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
标准开发流
这个 skill 用于把通用开发任务拆成稳定的交付阶段,并通过统一 CLI runtime protocol mode 协调不同实体。
默认阶段
- 规划:使用
dev-planner收敛目标、边界、风险和验证点。 - 实现:使用
dev-implementer完成代码与测试改动。 - 评审:使用
dev-reviewer检查回归风险、行为变化和测试缺口。 - 验证:使用
dev-verifier执行相关命令并整理证据。
调度原则
- 先规划,再进入实现,不要跳过
dev-planner。 - 实现完成后再进行评审和验证,这两个步骤可以并行。
- 如果目标不清、上下文缺失或计划失效,回退到规划阶段。
- 每个子任务都要求输出结论、证据、风险和建议下一步。
CLI Protocol 使用
- 使用当前 CLI 入口对应的
<cli> run --input-format stream-json --output-format stream-json作为标准入口,向 stdin 写入 typed runtime protocol envelope,并从 stdout 或 runtime store 读取结果。例如上层入口是dyai时使用dyai run,上层入口是ow时使用ow run。 - 用
session.startprotocol command 启动实体任务,字段至少包含entity、title、message。 - 用
session.status/session.eventsprotocol command,或直接读取 runtime store 投影出的状态与事件,跟踪后台任务状态。 - 用
session.messageprotocol command 给同一条任务补充指令;已完成或失败的任务会在 runtime 支持时用同一会话直接恢复。 - 用
session.submitprotocol command 处理等待输入或审批的任务。 - 必要时用
session.stopprotocol command 停止明显跑偏的任务,只有 graceful stop 无法恢复时才设置mode为force。 - 不要使用专用 agent 子命令、旧 StartTasks、手写 DB 或临时 TS 脚本来创建子任务;Agent Room 会在 server-managed host session 下由
session.start的 runtime store metadata/events 自动投影生成。 - server-managed host session 会把当前 adapter、model、effort、permission mode 注入为 runtime protocol 默认值;不写这些字段表示继承 host 选择,只有子任务需要不同运行配置时才显式指定。
- 专用 agent start/status/events/send/submit/stop 子命令只可视为兼容或调试 alias,不作为标准工作流入口。
可复制启动示例
每个子任务写一条 session.start JSONL;多个子任务就写多行:
cat <<'JSONL' | <cli> run --input-format stream-json --output-format stream-json
{"commandId":"start-planner","type":"session.start","payload":{"title":"规划:<目标>","message":"写清楚目标、约束、已有上下文和交付预期。","entity":"dev-planner","background":true},"title":"规划:<目标>","message":"写清楚目标、约束、已有上下文和交付预期。","entity":"dev-planner","background":true}
{"commandId":"start-implementer","type":"session.start","payload":{"title":"实现:<目标>","message":"附上规划结论、影响范围、需要补的测试或验证。","entity":"dev-implementer","background":true},"title":"实现:<目标>","message":"附上规划结论、影响范围、需要补的测试或验证。","entity":"dev-implementer","background":true}
JSONL
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.
- 2d ago First seen · 79 lines · 21 tokens per session scan A 7344a04cc11f
standard-dev-flow is a skill published in the GitHub repository oneworks-ai/app (18 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 1,183 once invoked, about $0.0001 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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