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 asherzj/ashers-agent-skills --skill flow-large-effortgit clone --depth 1 https://github.com/asherzj/ashers-agent-skillsWrote 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/asherzj/ashers-agent-skills/flow-large-effort)<a href="https://agentmods.dev/skills/asherzj/ashers-agent-skills/flow-large-effort"><img src="https://agentmods.dev/badge/skills/asherzj/ashers-agent-skills/flow-large-effort/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/asherzj/ashers-agent-skills/flow-large-effort"><img src="https://agentmods.dev/badge/skills/asherzj/ashers-agent-skills/flow-large-effort.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 | $0.00064 | $0.00842 |
| Opus 5 | $0.00032 | $0.00421 |
| Sonnet 5 | $0.00013 | $0.00168 |
| Haiku 4.5 | $0.00006 | $0.00084 |
Grade A, and why
flow-large-effort 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 9d 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
超大型工作交付流程
依次组合 from-transcript（按需）、wayfinder、to-spec、to-tickets 和 implement。这是一个跨会话状态机，耐久状态存放在已配置的工单系统中。开始或恢复前都要读取仓库配置、工单操作、领域术语和相关 ADR。
输入是会议逐字稿、连续口述或未经整理的讨论记录时，先用 from-transcript 形成经用户确认的输入简报；目的地、迷雾和决策工单只从确认后的内容建立。
1. 绘制并推进决策地图
围绕目标调用 wayfinder。地图 Issue、决策工单、迷雾、原生阻塞边、认领和“已有决策”索引都由它负责。
绘图占一个会话，不顺手解决人工决策工单。之后每个会话最多处理一个决策工单；只有 wayfinder 明确允许的研究任务可以并行。此阶段产出决策，不产出产品交付物。
如果绘图发现根本没有有意义的迷雾，停止使用本流程，并根据规模转到 flow-feature 或 flow-small-change。
2. 通过交付桥接门禁
只要仍有范围内的开放决策工单，或“尚未明确”中还有与目标相关的迷雾，就不能离开寻路阶段。只有路线已经清晰到可以陈述为一份连贯构建计划时，才进入交付。
在边界处加载地图、相关决议评论、原型或研究产物、领域术语和 ADR。地图只是索引，必须读取链接后的完整决策，不能只依赖一行摘要。
3. 把决策收敛成规格
调用 to-spec。这一步不可跳过：决策地图不能直接送入 implement。确认验收接缝、正常与异常验收用例、故障与一致性决策和非目标，并按工单系统约定发布规格。
4. 创建交付工单
对规格调用 to-tickets。让用户确认纵向切片粒度、验收用例分配、阻塞边和临时结构删除工单后再发布。生成的工单天然是 ready-for-agent，禁止再次分诊。
5. 推进可执行前沿
每个干净上下文只对一张无阻塞工单调用 implement。每次都重新读取工单、评论、相关地图决策、领域术语和 ADR。默认顺序执行；只有用户明确要求并行，且每张工单有隔离分支和可写工作树时才并行。
如果当前环境无法开启下一个干净上下文，列出当前前沿和准确调用方式后停止，不能把一张工单的偶然上下文带到下一张。
完成条件
决策地图已经清晰，规格和批准后的交付图已经发布，所有范围内工单都有实现证据并关闭，全部验收用例有证据，临时结构有明确结局，仓库规定的项目级完成钩子已经执行，且没有未解决阻塞。汇报地图、规格、工单、提交和远端分支指针，不要复制它们的全部内容。
What ships with it
1 file 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.
- 9d ago First seen · 44 lines · 64 tokens per session scan A 9b0fffbed113
flow-large-effort is a skill published in the GitHub repository asherzj/ashers-agent-skills (2 stars, last pushed 12d ago), licensed MIT. It adds 64 tokens to every session and 842 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 2026-08-31.
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