Borrowing it
Nothing to install: this file belongs to fitlab-ai/agent-infra. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/fitlab-ai/agent-infra/main/.agents/skills/complete-manual-validation/SKILL.mdgit clone --depth 1 https://github.com/fitlab-ai/agent-infraWrote 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/fitlab-ai/agent-infra/complete-manual-validation)<a href="https://agentmods.dev/skills/fitlab-ai/agent-infra/complete-manual-validation"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/complete-manual-validation/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/fitlab-ai/agent-infra/complete-manual-validation"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/complete-manual-validation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00072 | $0.01743 |
| Opus 5 | $0.00036 | $0.00872 |
| Sonnet 5 | $0.00014 | $0.00349 |
| Haiku 4.5 | $0.00007 | $0.00174 |
Grade A, and why
complete-manual-validation 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 3d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
完成人工验证
--agent取值见.agents/rules/task-management.md「合作者 token 规范」。
生命周期事件必须携带显式触发信息:编排调用使用 {trigger-initiator}=orchestrator,否则使用 model;{request-id} 是本任务与本轮产物的稳定单行标识,{reason-code} 使用 user-request 或 validation-rerun;started 与 completed 使用同一组值。
行为边界 / 关键规则
持久化报告证据
生成验证完成报告时,先读取 .agents/rules/evidence-reporting.md。状态核对和同步结果记录命令、范围、结构化结果、实际结论和未覆盖部分;继续遵守人工验证的 basename-only 与 sanitized result 脱敏边界。
- 本技能用于收尾已有 PR 摘要评论中的人工校验状态,不创建并行的普通验证留言。
- 必须写入
manual-validation.md或manual-validation-r{N}.md,让后续 PR 摘要刷新可复用人工验证结果。 - 找不到
sync-pr摘要评论时失败,不创建部分摘要兜底。 - 生成会同步到 Issue 的人工验证 artifact Markdown 前,先读取
.agents/rules/sync-content-generation.md并遵循其中的生成端约束;Issue 同步保持透明,不解析或改写正文。 - 执行本技能后必须立即更新
task.md。
版本戳规则:创建或更新 task.md frontmatter 时,先读取 .agents/rules/version-stamp.md,并写入或刷新 agent_infra_version。
第 0 步:状态核对(执行前硬约束)
在加载 workflow / skill / rules 指令之后、做任何任务状态判断或用户可见结论之前,必须先执行状态核对。指令类文件读取不算对外动作或结论。
运行以下命令,并在本轮产物的 ## 状态核对 段记录任务/产物范围、关键结果和未覆盖部分;正常成功不粘贴完整目录清单或 task.md 尾部。失败、阻塞、身份不一致或争议时,附决定性原文行:
agent-infra-internal task-snapshot {task-id} --format text
任务上下文解析
入口可省略 task ref;显式 task scope 仅接受
--task <ref>或-t <ref>,不再解释位置 task ref。保留其余业务操作数后调用agent-infra-internal task-context resolve {task-scope};{task-scope}为空或 task flag 之一。只读取结构化结果的taskId,后续把{task-id}绑定为完整TASK-YYYYMMDD-HHMMSS。解析失败时透传非零退出码,不自行扫描任务。
解析任务引用,并确认任务位于本技能支持的状态或目录且存在
task.md;无法定位时按未找到任务处理并停止。
步骤开始:声明 started 事件
确认前置条件和产物上下文后、本轮第一个产出动作之前执行 agent-infra-internal task-event {task-id} manual-validation.started --agent {standard-agent-token} --initiator {trigger-initiator} --request-id {request-id} --reason-code {reason-code},并以返回的 artifactContext 记录本轮身份。
执行步骤
1. 解析入参
输入格式:
complete-manual-validation [--task <ref> | -t <ref>] [{pr-ref}] {verification-summary}
- task scope 可省略;显式 scope 只接受
--task <ref>或-t <ref>。 {pr-ref}可选,支持#NN、NN或完整 PR URL。{verification-summary}必填。若缺失,立即停止并提示补充验证说明;不写产物、不更新 PR。
What ships with it
3 files 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.
- 3d ago Changed · +4 lines d681268311e3
- 4d ago Changed · +2 lines e274040f6907
- 6d ago Changed · +1 lines 05245b4eb2f9
- 10d ago First seen · 122 lines · 72 tokens per session scan A 4c4737d9ee0d
complete-manual-validation is a skill published in the GitHub repository fitlab-ai/agent-infra (83 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 1,743 once invoked, about $0.0004 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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