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 agentmods add skills/fitlab-ai/agent-infra/plan-tasknpx skills add fitlab-ai/agent-infra --skill plan-taskgit 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/plan-task)<a href="https://agentmods.dev/skills/fitlab-ai/agent-infra/plan-task"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/plan-task.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 | $0.00046 | $0.03105 |
| Opus 5 | $0.00023 | $0.01553 |
| Sonnet 5 | $0.00009 | $0.00621 |
| Haiku 4.5 | $0.00005 | $0.00311 |
Grade A, and why
plan-task 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 yesterday.
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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
设计技术方案
--agent取值见.agents/rules/task-management.md「合作者 token 规范」。
若入口业务操作数包含 --orchestrated，绑定 {execution-flag} = --orchestrated 并原样转发给 completed 事件；否则绑定为空。不得从 orchestration.json、环境变量或历史产物推断该标记。
行为边界 / 关键规则
- 本技能仅产出技术方案文档（
plan.md或plan-r{N}.md）—— 不修改任何业务代码 - 生成会同步到 Issue 的任务或生命周期 Markdown 前，先读取
.agents/rules/sync-content-generation.md并遵循其中的生成端约束；同步端不解析或改写正文 - 这是一个强制性的人工审查检查点 —— 不要自动进入实现阶段
- 方案涉及兼容、迁移、旧格式或旧入口时，先读取
.agents/rules/compatibility-policy.md；未通过准入门槛时不得设计 adapter、shim、双写或并行状态机 - 执行本技能后，你必须立即更新 task.md 中的任务状态
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，并把原文粘贴到本轮产物的 ## 状态核对 段：
agent-infra-internal task-snapshot {task-id} --format text
状态核对完成前，禁止任何关于外部状态的断言（例如“代码没变”“测试已通过”“没有其他引用”），包括思考阶段。本门禁只提供结构下限；逐条证据配对和真实性仍需按报告模板与审查要求核对。
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
确认前置条件和轮次后、本轮第一个产出动作之前执行：
agent-infra-internal task-event {task-id} plan.started --agent {standard-agent-token}
执行步骤
1. 验证前置条件
检查必要文件：
.agents/workspace/active/{task-id}/task.md- 任务文件- 至少一个分析产物：
analysis.md或analysis-r{N}.md
注意：{task-id} 格式为 TASK-{yyyyMMdd-HHmmss}，例如 TASK-20260306-143022
如果任一文件缺失，提示用户先完成前置步骤。
2. 解析方案上下文
运行 agent-infra-internal task-artifact {task-id} inspect --family plan。仅当结果为 ready 时继续；从 inputs 取得最新 {analysis-artifact}，从 next.round / next.name 取得 {plan-round} / {plan-artifact}。不得自行扫描轮次或拼装文件名。随后执行 started 事件并复核返回身份。
3. 阅读需求分析
读取步骤 2 核心返回的最新 {analysis-artifact}，
以理解：
- 需求及其背景
- 相关文件和代码结构
- 影响范围和依赖关系
- 已识别的技术风险
- 工作量和复杂度评估
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.
- yesterday Changed · +8 lines 4e0e208d85d2
- 5d ago First seen · 196 lines · 46 tokens per session scan A 2bf73a244753
plan-task is a skill published in the GitHub repository fitlab-ai/agent-infra (83 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 3,105 once invoked, about $0.0002 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
contextualize
Detect application-context mismatch after execution. Fires when correct output may not fit the actual context. Type: (ApplicationDecontextualized, AI, CONTEXTUALIZE, Result) → ContextualizedExecution.
review-loop
Convergence-paced review-resolve loop over a change and its governing surfaces. Verifies each finding against the codebase and the base it is measured from, then re-reviews until each is disposed of.
conduct
Conduct method before object-level work. Fires when the work needs several moves in non-trivial order. Type: (MethodUnderdetermined, Hybrid, CONDUCT, WorkProspect × MoveGround) → ConductedMethod.
apportion
Apportion an autonomous goal into execution units carrying their own completion conditions. Type: (GoalPlanUncompiled, User, APPORTION, AutonomousGoal × ExecutionHorizon) → ConditionBearingUnitPlan.
ground
Validate structural mapping between abstract and concrete domains. Presents concrete instantiations when mapping uncertainty is detected. Type: (MappingUncertain, AI, GROUND, R) → ValidatedMapping.
steer
Project-profile recalibration. Audits session calibration drift, presents per-cluster evidence for a user verdict, writes the updated project-profile rule, and inscribes a settled-direction clause.