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/run-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/run-manual-validation)<a href="https://agentmods.dev/skills/fitlab-ai/agent-infra/run-manual-validation"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/run-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/run-manual-validation"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/run-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 | $0.00061 | $0.01235 |
| Opus 5 | $0.00030 | $0.00617 |
| Sonnet 5 | $0.00012 | $0.00247 |
| Haiku 4.5 | $0.00006 | $0.00123 |
Grade A, and why
run-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 4d 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
运行人工校验
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。
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。先解析 --scope、--timeout、--format，在 -- 后原样保留用户命令，再调用 agent-infra-internal task-context resolve {task-scope}。解析失败时透传非零退出码，不自行扫描任务。内部 task-validate 协议仍使用位置 task ref。
行为边界
持久化报告证据
生成验证报告时，先读取 .agents/rules/evidence-reporting.md。只记录命令名称、目标范围、退出状态、sanitized result 和覆盖缺口；不得记录完整 argv、环境变量、token、绝对路径或原始敏感 transcript。
- 本技能负责选择校验模式、调用唯一机械入口并记录证据；不把 PR 人工校验标为完成。
complete-manual-validation仍是维护者确认覆盖充分后的最终登记入口。- 禁止直接操作临时 worktree、lease 或 container；只调用
agent-infra-internal task-validate。 - 产物不得记录 token、环境变量、完整 argv、绝对用户路径或原始 transcript。
- 生成会同步到 Issue 的验证 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。
第 0 步：状态核对（执行前硬约束）
解析任务引用后运行，并在产物中记录任务/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
执行步骤
- 读取
reference/discovery-and-execution.md，解析输入模式；非法或半截输入在 started 前停止，不写产物。 - 运行
agent-infra-internal task-artifact {task-id} inspect --family validation-run，读取最新 review-code 人工校验项；再运行agent-infra-internal platform-pr inspect {task-id}，按 reference 的状态矩阵发现、归并并编号。仅在自动模式下，可靠来源无项或唯一可能来源不可读时才在 started 前停止；合法显式模式始终以用户命令作为有效工作继续。 - 从核心结果取得轮次和产物名；确认存在有效显式工作或非空发现清单后，运行
agent-infra-internal task-event {task-id} validation-run.started --agent {standard-agent-token} --initiator {trigger-initiator} --request-id {request-id} --reason-code {reason-code}，并逐项分类为executable|unavailable|unknown|unsafe|unresolved。 - 每个可执行项分别调用
agent-infra-internal task-validate {task-ref} --scope snapshot --format json -- {command...}；只有证据表明必须原位时才对该项进行第二次显式 inplace 调用。零项可执行时不运行伪造命令，但仍继续产出覆盖缺口证据。 - 读取
reference/report-template.md，创建validation-run.md|validation-run-r{N}.md；记录输入模式、发现清单、逐项结果、CLI JSON allowlist 与去敏摘要。 - 运行
agent-infra-internal task-event {task-id} validation-run.completed --agent {standard-agent-token} --initiator {trigger-initiator} --request-id {request-id} --reason-code {reason-code} --artifact {artifact}。存在 Issue 时依次运行agent-infra-internal platform-comment sync {task-id} --kind task --agent {standard-agent-token}和agent-infra-internal platform-comment sync {task-id} --kind artifact --artifact {artifact} --agent {standard-agent-token}。 - 运行
agent-infra-internal task-verify {task-id} validation-run.completed --artifact {artifact} --format text；未通过则修复后重跑。 - 告知用户证据路径、覆盖缺口和验证结果；明确仍需维护者判断是否执行
complete-manual-validation。读取.agents/rules/next-step-output.md，最后一行输出Completed at。
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.
- 4d ago Changed · +4 lines 2d67d49b4998
- 5d ago Changed · +2 lines a69e129adc4e
- 7d ago Changed · +5 lines 7be4d9fbedca
- 11d ago First seen · 43 lines · 61 tokens per session scan A 2ea421c901a2
run-manual-validation is a skill published in the GitHub repository fitlab-ai/agent-infra (84 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 1,235 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 2026-08-30.
Other skills, from other repositories
tdd-cycle
Execute full TDD red-green-refactor cycle with validation gates. Use when saying "TDD cycle", "test-driven development", or "full TDD workflow".
done
Close an increment: ledger check, specweave verify, optional review, then specweave complete. Use when all tasks are done and saying "close increment", "we are done", or "finish up".
genie-orca-review
Independent, read-only review of a group, a wish, or a PR on Orca — SHIP / FIX-FIRST / BLOCKED with severity-tagged findings. Council and retro are this skill with a different input.
argot-check
Score your working changes with argot — flag code foreign to this repo's own patterns (unfamiliar dependencies, APIs, constructs), functions the repo already has, code filed in the wrong place, imports that break the repo's layering, and tests weakened, disabled, or deleted alongside a production change — before…
atomic-tdd
Test-first discipline. Auto-triggers on "let's implement X", "add feature Y", "fix bug Z", "write a test for", "implement", "build out", and similar pre-code-change phrases. Iron rule: failing test exists before production code. Skip only for pure docs/config changes with an explicit "skipped because:" note. Explicit…
ralphctl-test-driven-development
Execute-phase skill — write the failing test before the code that makes it pass; for bug fixes, this is the reproduction test itself. Use for any logic change, bug fix, or behavioural modification; for the full root-cause triage pipeline around an unexpected failure, see ralphctl-debugging-and-error-recovery.