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/archive-tasks/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/archive-tasks)<a href="https://agentmods.dev/skills/fitlab-ai/agent-infra/archive-tasks"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/archive-tasks/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/archive-tasks"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/archive-tasks.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 | $0.00035 | $0.00459 |
| Opus 5.5 | $0.00014 | $0.00184 |
| Sonnet 5.5 | $0.00007 | $0.00092 |
| Haiku 4.5 | $0.00003 | $0.00046 |
Grade A, and why
archive-tasks 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 6d 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
归档已完成任务
将 <task-workspace>/completed/ 中的已完成任务归档到 <task-workspace>/archive/YYYY/MM/DD/TASK-xxx/local/，并重建三级归档索引。TASK 本地材料位于 local/，含按路径排序的 contents.sha256；TASK 根目录不存放普通文件。
- 根 manifest：
<task-workspace>/archive/manifest.md - 年 manifest：
<task-workspace>/archive/YYYY/manifest.md - 月 manifest：
<task-workspace>/archive/YYYY/MM/manifest.md
执行流程
1. 验证环境
确认 <task-workspace>/completed/ 存在，并根据用户输入选择以下四种调用方式之一：
- 无参数：归档全部已完成任务
--days N：保留最近N天，仅归档更早的任务--before YYYY-MM-DD：仅归档指定日期之前的任务TASK-ID...：仅归档指定任务
2. 运行归档脚本
执行以下命令：
bash .agents/skills/archive-tasks/scripts/archive-tasks.sh [--days N | --before YYYY-MM-DD | TASK-ID...]
脚本负责：
- 解析
task.mdfrontmatter 中的completed_at（缺失时回退到updated_at） - 按
YYYY/MM/DD/TASK-xxx/local/保存任务本地材料，不压缩 - 跳过已归档、缺少元数据或不存在的任务
- 全量重建根 / 年 / 月三级 manifest
- 输出归档与跳过摘要
3. 告知用户
向用户汇报：
- 本次归档的任务数量
- 跳过的任务数量和原因
- 根 manifest 的路径
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.
- 6d ago Changed 5b95730e72f9
- 1mo ago Changed 7306f89f6d61
- 1mo ago First seen · 46 lines · 35 tokens per session scan A 4f40da7b3932
archive-tasks is a skill published in the GitHub repository fitlab-ai/agent-infra (86 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 459 once invoked, about $0.0001 per session on Opus 5.5. 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
plan-tree
Maintain a structured planning document tree made of roadmap/status files, implementation status or handoff TODO files, topic notes, decision records, open questions, ideas/inspiration pools, and repository/file-structure hygiene plans. Use when Codex needs to create, reorganize, audit, or update a multi-file plan…
tokenrip-cli
The rip CLI for Tokenrip, the shared workspace your AI agents work from: load a workspace and pick up where the work left off, publish artifacts (documents, HTML, code, CSV, PDFs, living tables) into it, organize folders, propose an action for a person to approve before it goes out, report finished work, decide…
quality
Evaluates whether a GitHub issue is spam, empty, needs more information, or is OK to proceed.
follow-up-after-call
Use after a completed customer, prospect, partner, or important internal sales call, discovery session, demo, or conversation, including when the user supplies or uploads a transcript, notes, recording summary, or other grounded evidence. Advise on, assess, qualify, recap, and produce seller-ready follow-up actions…
persona-event-coordinator
Plan and manage events — scheduling, invitations, and logistics.
persona-project-manager
Coordinate projects — track tasks, schedule meetings, and share docs.