Drive an installed LaRuche App through its declared actions, never its files.
日本語の概要は準備中です。原文の説明を表示しています。
Research a question across web and academic sources and produce a cited report.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Several passes over web and academic sources, each claim cross-checked against a second source, ending in a written report with its citations, saved to a file and summarised into memory.
This is the expensive path. For a question two or three searches would settle, use
web-research instead: it is the same discipline at a tenth of the cost, and choosing it
correctly is part of doing this well.
research_mode with mode set, and a short reason. Call it BEFORE the first search,
as soon as the user asks for something thorough, deep or exhaustive, or as soon as you
realise a single lookup will not be enough. It activates the long-running research
behaviour: budgets, pacing, and the right expectations about how many passes are allowed.
Declaring it late is the usual mistake. Three shallow searches happen first, the answer is thin, and the mode is switched on when the work is nearly over.
Then check what you already know, which is free:
memory_search with query set to the subject. This may have been researched before.session_search with query runs a full-text search across PAST sessions and returns
matching excerpts. Use it when the user says "we looked at this already", or when the
subject feels familiar. It is far cheaper than repeating the research.Before any search, write down, for yourself:
Defaults when the user did not say: five to fifteen sources, an eighteen month recency window, a report written to a file. A question that has not been reduced to one sentence produces a report about several subjects and answers none of them.
web_search(query="<keywords> survey OR review 2026", num_results=10)
web_search(query="<keywords>", allowed_domains=["arxiv.org"], num_results=10)
web_deep_search(query="<one specific sub-question>")
num_results defaults to 8 and is capped at 15. allowed_domains and blocked_domains
are arrays: use the first to pin a pass to a primary source, the second to cut a content
farm that keeps winning the ranking.
Run the passes in PARALLEL, one angle per call. They are read-only, so several fit in one message. Collect arXiv ids, titles, official docs. De-duplicate as you go, not at the end.
web_deep_search returns snippets AND the full text of the top results. It is slower and
worth it on a sub-question that shallow search keeps answering with noise. It is not the
tool for the first, wide pass.
web_fetch(url="https://arxiv.org/abs/<id>")
web_fetch(url="https://arxiv.org/pdf/<id>")
web_fetch takes ONE url, as a string, and extracts a PDF served over HTTP by itself.
A long page arrives in chunks: the output states the total size and the offset for the
next one. If the section you need is not in the first chunk, read on with offset.
When a server refuses the fetch, download and read from disk instead:
curl -sL "https://arxiv.org/pdf/<id>" -o "C:\\tmp\\paper.pdf"
then read_extract with path set to that absolute path. read_extract reads LOCAL
PDF, .txt and .md files only; it has no urls argument and no field selection.
Discard as you read: no date, no author, a preprint over two years old with no citations, a page that restates the question without answering it.
Record every decisive fact with finding the moment you read it, passing fact and
source. The findings ledger survives context compaction, and a multi-pass mission WILL
be compacted. A fact you did not record can be gone before you write the report.
For each claim that carries weight:
[UNVERIFIED] in the report;# <Topic>, research report, <YYYY-MM-DD>
## Answer
<the answer to the one-sentence question, first, in two or three sentences>
## Key findings
- <finding> [<title>, <url>]
## Disagreements and gaps
- <claim A> against <claim B>, unresolved because <reason>
- <what could not be established, and what was tried>
## Sources
| # | Title | URL | Date | Kind |
## Open questions
Then save it, and leave a trace that survives the session:
file_write(path="C:/dev/research/<topic>_report.md", content="<the report>")
memory_write(node_id="research.<topic>", content="<two sentences and the file path>")
memory_write takes node_id and content. It has no key or value. node_id is
dotted and snake_case (research.world_models), never a slash path. Use an absolute path
in file_write: a relative one lands in the server's working directory, not the user's.
arxiv.org/abs/1706.03762 is the LATEST version and can differ
substantially from what you read last month. Cite the version you actually read, with
its v suffix.Every result is the same syndicated article. You found a press release. Search for what it is based on: the paper, the filing, the repository, the official announcement.
A source is paywalled or returns 403. An obstacle, not a conclusion. Try web_fetch
with render: true, then the Wayback Machine, then another source. Only report the angle
as blocked after all of those.
Context ran out mid-mission and the findings are gone. They were never written with
finding. Restart the pass, recording as you read this time.
The report has ten sources and no answer. The question was never reduced to one sentence. Go back to step 1 and write it, then re-read what you already collected against it: most of it usually answers something else.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Drive an installed LaRuche App through its declared actions, never its files.
日本語の概要は準備中です。原文の説明を表示しています。
Find academic papers on arXiv, with citation counts and BibTeX.
日本語の概要は準備中です。原文の説明を表示しています。
Render text or an image as ASCII art for terminal-friendly output.
日本語の概要は準備中です。原文の説明を表示しています。
Track RSS/Atom feeds and blogs via blogwatcher-cli.
日本語の概要は準備中です。原文の説明を表示しています。
Drive a real web browser: navigate, read, find, click, fill, screenshot
日本語の概要は準備中です。原文の説明を表示しています。
Measure a codebase: lines of code, language mix, and symbol lookups.
日本語の概要は準備中です。原文の説明を表示しています。