adopt
Take a folder that already holds research — papers, notes, code, half-written drafts — into MAGI, without breaking what is already there.
Triage a literature-radar digest: judge each candidate against what this project is actually doing, and queue only what earns it.
$ npx -y skills add Misaka16384/Wikify --skill radar_review --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/radar_reviewContext preview
The summary Claude sees to decide when to auto-load this skill.
Triage a literature-radar digest: judge each candidate against what this project is actually doing, and queue only what earns it.
name: radar_review description: "Triage a literature-radar digest: judge each candidate against what this project is actually doing, and queue only what earns it." commands: radar_review: "Triage the pending radar digest." origin: magi
`magi radar status` reports a digest waiting, or `magi next` says so.
1. `magi radar status --json`, then read the digest. 2. Read the project first: `magi stats wiki-summary` and the open propositions in `threads/`. Relevance is to what is asked here, not the field. 3. Score each candidate by hand. The digest's score is a **rank, not a measurement**, and a project with no vector index has none at all — then the order carries nothing and every candidate has to be read. 4. `magi search "<title>"` before keeping one: a paper the project already covers is not new information, it is a duplicate. 5. `magi radar triage --id <id> --decision accept|dismiss`, ids from the digest and `--id` repeatable when the decision is the same. Never hand-edit the frontmatter — the triage ledger is what the next run reads. 6. Accepted papers: `magi ingest url "<id>"` to queue them, then hand off to the `ingest` skill. 7. `magi radar triage --done` closes the report and prints how many of how many you decided. Until you run it, `sync` and `next` keep reporting it.
reading; the judgement is yours and it is the whole point of this step.
against your reasoning about the ones before it: twenty in, that reasoning is the loudest thing in context and the least relevant, and the bar drifts.
count; say the same thing in your own report, and where you stopped.
a citation, not whether the paper is nearby. Most nearby papers are not owed.
MAGI 是一个 agent-native 的科研工作环境:人是驾驶员,LLM agent 是机体,确定性的 magi CLI 是拘束具——三者同步率越高,科研越快。它把学术论文(PDF/LaTeX)摄入、编译为 Obsidian 兼容的概念卡片项目,并用三核架构管理完整的科研状态: 进入任意项目先跑 magi sync——它输出同步率、三核状态和逐条可执行的修复提示。magi radar 是文献雷达:定时发现相关新论文,并侦察"知识上应引用我方论文却未引用"的候选。同一份 skills 通吃 Claude
Take a folder that already holds research — papers, notes, code, half-written drafts — into MAGI, without breaking what is already there.
Answer a question from this project's own knowledge — retrieved, read and cited, never from memory.
Compile raw sources into dense, interlinked reference and concept cards, and mine the concepts a compiled card left implicit.
Turn whatever the human has — a PDF, a link, a DOI, a screenshot of a citation — into raw/ sources this project can compile.
Entry point for a MAGI research project: run magi next, do what it says, and call the skill it names.