learn-modules

A project command for learning the behavior of step-runner modules one at a time and recording the findings in authored reference files.

In plain words
What is it for?
Use it to initialize or continue module learning, test uncertain behavior, mark modules complete or skipped, and regenerate the project documentation.
Why use it?
It gives a repeatable way to study modules without changing their source documentation or losing track of progress.

Command for Cursor

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.

agentmods
npx agentmods add commands/quickerhub/quicker-rpc/learn-modules
Clone the repo
git clone --depth 1 https://github.com/QuickerHub/quicker-rpc

Made for: Cursor.

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 440 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00000 $0.00440
Opus 5 $0.00000 $0.00220
Sonnet 5 $0.00000 $0.00088
Haiku 4.5 $0.00000 $0.00044

Measured 2d ago against content hash b5db9e71395f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

learn-modules 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 2d 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.

.cursor/commands/learn-modules.md · 25 lines

What it actually says

learn-modules — 逐模块学习 step-runner 并蒸馏 authored ref

L1 单模块(143/143 维护态)。动作级 pattern / 动作库学习用 /learn-authoring;命令索引见 /cursor-cli

docs/superpowers/plans/2026-06-13-step-module-learning.md 执行一轮模块学习。

步骤

  1. docs/authoring-references/step-modules/.learning-progress.json 不存在: npm run docs:modules:learning-init
  2. node scripts/step-module-learning-progress.mjs --next 取本批模块 id。 输出 ALL_DONE 时:跑 npm run docs:modules:gen && npm run docs:gen,结束。
  3. 对本批每个模块执行 plan 中的「单模块学习协议 P1–P5」:
    • qkrpc step-runner get --key sys:<key> --json(逐 controlField 分支)
    • kc/<id>.md 提取 get 之外的增量
    • 无增量 → --mark-skip <id> --reason "<一句话>"
    • 不确定点 → __module_learning__ 临时动作实跑 --trace 验证,用后即删
    • authored/<id>.md(SPEC §3/§7)→ --mark-done <id>
  4. 批末:npm run docs:modules:analyze && npm run docs:modules:gen && npm run docs:gen

约束

  • 勿改 kc/ 正文;勿删除非 __module_learning__ 前缀的动作
  • 用户要求持续后台跑时:先手动执行一轮,再 pwsh scripts/run-learning-loop.ps1 arm loop
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. 2d ago First seen · 25 lines · 0 tokens per session scan A b5db9e71395f

Subscribe to this mod's changes

learn-modules is a command published in the GitHub repository QuickerHub/quicker-rpc (13 stars, last pushed 19d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 440 tokens. 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.