agent-infra: Skill for Claude Code

.agents/skills/complete-manual-validation/SKILL.md

complete-manual-validation is a skill for Claude Code, Codex from fitlab-ai/agent-infra. It costs 72 tokens per session (1,743 once invoked), scanned A, original, MIT.

A workflow for recording that a maintainer has completed manual checks for a pull request, such as tests requiring a real environment or special permissions.

In plain words
What is it for?
Use it to write a manual-validation report and update the existing pull-request summary with the validation result.
Why use it?
It keeps the pull request summary and task records aligned with checks that cannot be completed automatically.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is fitlab-ai/agent-infra's own configuration. It tells Claude Code and Codex how to work on agent-infra itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything agent-infra configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/fitlab-ai/agent-infra/main/.agents/skills/complete-manual-validation/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/fitlab-ai/agent-infra

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for complete-manual-validation

README.md
[![agentmods](https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/complete-manual-validation/github.svg)](https://agentmods.dev/skills/fitlab-ai/agent-infra/complete-manual-validation)
Your own site
<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.

agentmods 80×15 button for complete-manual-validation

Your own site · 80×15
<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>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,743 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 3d ago against content hash d681268311e3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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.

.agents/skills/complete-manual-validation/SKILL.md · 129 lines

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-requestvalidation-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.mdmanual-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>-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>-t <ref>
  • {pr-ref} 可选,支持 #NNNN 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。

Read the full file on GitHub · 129 lines

Files

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.

Changes

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.

  1. 3d ago Changed · +4 lines d681268311e3
  2. 4d ago Changed · +2 lines e274040f6907
  3. 6d ago Changed · +1 lines 05245b4eb2f9
  4. 10d ago First seen · 122 lines · 72 tokens per session scan A 4c4737d9ee0d

Subscribe to this mod's changes

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.