learn-done

learn-done is a command for Claude Code from Sean-xhz/ai-learning-platform. It costs 42 tokens per session (1,114 once invoked), scanned A, original, MIT.

A command that closes a day’s study by testing what you learned, recording the session, updating the plan, and adding weak areas to a review list.

In plain words
What is it for?
Use it after studying to compare before-and-after test results, turn learning into project or subject ideas, update progress, and schedule follow-up review.
Why use it?
It prevents a study session from ending without checking progress or capturing what still needs work.

Command for Claude Code

Written for Claude Code: $ARGUMENTS substitution. Also seen: mentions subagents.

Part of the ai-learning-platform plugin — 2 skills, 5 commands, 3 agents, 2 hooks shipped together

Good fit Use it after studying to compare before-and-after test results, turn learning into project or subject ideas, update progress, and schedule follow-up review.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/sean-xhz/ai-learning-platform/learn-done
Install

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.

Clone the repo
git clone --depth 1 https://github.com/Sean-xhz/ai-learning-platform

Made for: Claude Code.

Or install ai-learning-platform, the plugin that ships this one along with the rest of its 2 skills, 5 commands, 3 agents, 2 hooks.

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 learn-done

README.md
[![agentmods](https://agentmods.dev/badge/commands/sean-xhz/ai-learning-platform/learn-done.svg)](https://agentmods.dev/commands/sean-xhz/ai-learning-platform/learn-done)
Your own site
<a href="https://agentmods.dev/commands/sean-xhz/ai-learning-platform/learn-done"><img src="https://agentmods.dev/badge/commands/sean-xhz/ai-learning-platform/learn-done.svg" alt="Measured on agentmods" height="20"></a>
Per session 42 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,114 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.
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 $0.00042 $0.01114
Opus 5 $0.00021 $0.00557
Sonnet 5 $0.00008 $0.00223
Haiku 4.5 $0.00004 $0.00111

Measured 6d ago against content hash b042a9cdcf9d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

learn-done 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.

commands/learn-done.md · 66 lines

What it actually says

/learn-done

今天的学习告一段落,帮我收尾。

$ARGUMENTS

当前进度:grep '当前进度' learning-plan.md 2>/dev/null | head -1 学习模式:grep '学习模式' learning-plan.md 2>/dev/null | head -1

这是每日学习闭环的收尾命令,与 /learn-today(开环)成对。按以下步骤编排,不要跳步

Step 1:定位今天

  • 如果 $ARGUMENTS 含天数(如 "Day 3"),用它
  • 否则从上方"当前进度"读取
  • 读取该日的主题、核心问题、学习模式
  • 若今天是缓冲日(Day 标题含 📥 缓冲日(复习日)):复习日的"所学"就是复习本身——跳过 Step 2-4(队列更新已在 /learn-today 深度复习时完成),直接执行 Step 5 更新状态

Step 2:Post-test(调用 assessment-officer)

  • 让测评官出 Post-test(与 Pre-test 等难度、不同题),重点覆盖 Pre-test 标记的盲区
  • 计算学习增量,指出具体进步在哪个维度
  • 将 Pre/Post 结果写入 sessions/session-log-dayXX.md(不存在则从 templates/session-log.md 创建)
  • 写入后 progress-tracker Hook 会自动更新进度面板

Step 3:project-mentor 价值转化(按学习模式)

调用 project-mentor Subagent(显式传入:当天 Day 号、今日 session-log 路径 sessions/session-log-dayXX.md、学习模式),把今日所学转化为价值,输出追加到 session-log 的「项目导师建议」区:

  • 🎯 项目驱动 → 改进动作列表(P0-P3 优先级,目标文件/模块)
  • 🔭 领域探索 → 领域洞察列表(趋势/机会/风险)
  • 💡 纯粹认知 → 知识网络图(新概念 ↔ 旧知识的连接)

遵守 project-mentor 的约束:找不到真实映射就说没有,不编造关联。

Step 4:采集盲区与疑问入复习队列(P1-7)

从今日 session-log 提取,追加到 review-queue.md(不存在则从 templates/review-queue.md 创建):

  • 最大盲区(类型=盲区):取 session-log Post-test 表的「最大盲区」行(Post-test 暴露的最薄弱点,不是 Pre 区的「起点盲区」)
  • 仍存在的疑问(类型=疑问):session-log「仍存在的疑问」里学习者自己的开放问题

每条新项:来源Day=今天首次出现=今天下次复习Day=今天+1复习次数=0忘记次数=0状态=待复习去重:若队列已有相同内容的项,不重复添加,仅在该项状态为「已掌握」时重新激活。

Step 5:更新计划状态

  • 将该日 - **状态**: 行改为 ✅ 已完成(只保留单一状态值)
  • 更新「当前进度」为下一天
  • 若今天是计划的最后一天(没有下一天可推进):把 review-queue.md 中所有 状态=待复习/已重置 的项改为 carryover——计划结束时仍未消解的盲区/疑问,交由方法论收割(methodology-harvest)作为认知陷阱模式的素材处理

Step 6:阶段末/缓冲日提示

  • 若今天是一个阶段的最后一天:先把「里程碑检查点」表中该阶段的「实际完成日」填为今天、「状态」改为 ✅,再提示"本阶段完成,要不要运行方法论收割(methodology-harvest)提炼可复用 pattern?"
  • 若明天是缓冲日:提示"明天是复习日,/learn-today 会带你做累积复习而非新内容"

约束

  • Post-test 和 Pre-test 不得有重复题目
  • 复习队列采集必须去重,不得堆量
  • 状态字段只保留单一值(⬜/🔄/✅ 三选一),不要把三个选项都留下
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. 6d ago First seen · 66 lines · 42 tokens per session scan A b042a9cdcf9d

Subscribe to this mod's changes

learn-done is a command published in the GitHub repository Sean-xhz/ai-learning-platform (2 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 1,114 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 2026-08-31.