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/hahaxiang27/FlowHarnessWrote 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/hahaxiang27/flowharness/harness.start)<a href="https://agentmods.dev/commands/hahaxiang27/flowharness/harness.start"><img src="https://agentmods.dev/badge/commands/hahaxiang27/flowharness/harness.start.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.00706 |
| Opus 5 | $0.00010 | $0.00353 |
| Sonnet 5 | $0.00004 | $0.00141 |
| Haiku 4.5 | $0.00002 | $0.00071 |
Grade A, and why
harness-start 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 7d 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
Harness Sprint 开始/恢复
上下文管理: 🔄 清空上下文 — 使用子代理（Agent）执行，确保干净的上下文环境
指令
启动或恢复一个 Sprint 的执行。本命令会在子代理中运行，获得干净的上下文。
输入参数
- Sprint 编号: $ARGUMENTS 或自动检测最新的未完成 Sprint
执行步骤
使用 Agent 工具启动子代理，传入以下 prompt：
你是 Harness 开发框架的 Sprint 执行器。
1. 读取 `.harness/sprints/sprint-{N}-progress.md` 了解当前进度
2. 找到第一个未完成的任务
3. 读取 `.harness/sprints/sprint-{N}.md` 了解该任务所在的 Day 和 Batch
4. 读取 项目中`constitution.md` 了解项目约束
5. 读取 `.harness/prompts/executor.md` 了解上下文加载协议
报告：
- 当前 Sprint 状态（已完成/总任务数）
- 下一个要执行的任务 ID 和描述
- 该任务需要加载的上下文文件（精确到行号范围）
- 建议的执行命令（提示用户运行 /harness.exec）
不要执行任务本身，只做状态报告和准备。
输出
Sprint 状态摘要和下一步操作指引。用户看到后运行 /harness.exec 开始执行。
Dashboard 观测面板更新
start 完成后，按 .harness/prompts/dashboard-updater.md 中 Step 8 规则更新 specs/{feature}/dashboard.html：
- 填充
#panel-start面板：环境检查清单表（检查项/状态两列） - harness.start 节点侧边栏状态改为
done+ok
SDD Step Gate
When specs/{REQUIREMENT_ID}/dashboard-state.json exists (SDD workflow active), after this command completes follow .harness/prompts/command-step-gate.md:
- Update dashboard-state.json and dashboard.html when applicable.
- Mark this command done, next step next, workflow_plan.phase = awaiting_user.
- Stop immediately - do not chain the next internal command in the same turn.
- Hand off with .harness/prompts/step-gate-handoff.md.
Skip only for standalone invocation without dashboard state, or when the user explicitly asks to batch remaining steps.
Requirement Artifact Path Convention
For a requirement id {REQUIREMENT_ID}, keep all delivery artifacts bucketed by that id:
- Specs and dashboards:
specs/{REQUIREMENT_ID}/ - Harness sprint plans/progress:
.harness/sprints/{REQUIREMENT_ID}/ - Harness metrics and reports:
.harness/metrics/{REQUIREMENT_ID}/
Do not create a second numbered feature folder for the same requirement.
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.
- 7d ago First seen · 78 lines · 21 tokens per session scan A 1aedcb4a87af
harness-start is a command published in the GitHub repository hahaxiang27/FlowHarness (4 stars, last pushed 2mo ago), licensed MIT. It adds 21 tokens to every session and 706 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.