learn

# Learn: From Raw Materials to Published Output

Prefix your first line with 🥷 inline, not as its own paragraph.

Collect, organize, translate, explain, structure. Support the user's thinking; do not replace it.

## Outcome Contract

- Outcome: unfamiliar material becomes a reliable mental model, reference, article, or notes set the user can use.
- Done when: primary sources are collected or supplied, contradictions are handled explicitly, and the final structure teaches the topic without hiding uncertainty.
- Evidence: source URLs or files, fetched content, notes from digestion, outline decisions, and self-review against the requested output.
- Output: research notes, outline, publish-ready draft, or canonical reference, matching the chosen mode.

**Boundary**: single URL that only needs fetching belongs in `/read`. A single URL that needs summary or analysis can use `/read` as the fetch step, but the final answer should satisfy the user's requested summary or analysis. `/learn` is for multi-source research that produces a new structured output.

## Pre-check

Check whether `/read` and `/write` skills are installed (look for their SKILL.md in the skills directories). Warn if missing, do not block:
- `/read` missing -- Phase 1 fetch falls back to native `WebFetch` / `curl`; coverage on paywalled, JS-heavy, and Chinese-platform pages degrades.
- `/write` missing -- Phase 5 AI-pattern stripping falls back to manual scan. Phases 1-4 are unaffected.

## Choose Mode

Ask the user to confirm the mode, using the environment's native question or approval mechanism if it has one:

| Mode | Goal | Entry | Exit |
|------|------|-------|------|
| **Deep Research** | Understand a domain well enough to write about it | Phase 1 | Phase 6: publish-ready draft |
| **Quick Reference** | Build a working mental model fast, no article planned | Phase 2 | Phase 2: notes only |
| **Write to Learn** | Already have materials, force understanding through writing | Phase 3 | Phase 6: publish-ready draft |
| **Canonical Article** | One article that covers a topic so thoroughly readers need nothing else | Phase 1 | Phase 6: single authoritative reference |

If unsure, suggest Quick Reference.

## Canonical Article Mode

Activate when: "一篇就够", "一站式参考", "整理成长文", "目的是大家只需要看这篇就好了", or the user wants a single authoritative reference on a topic.

Goal: after reading the article, no one should need to search for anything else on this topic.

Additional requirements on top of standard Deep Research:
- Every major sub-topic must have its own section; nothing left as a footnote
- Include worked examples, not just principles
- Cover common mistakes and how to avoid them
- Add a "Further Reading" section with the 3-5 sources that go deepest; flag which ones are the best starting points
- Phase 6 self-review must confirm: "Could a reader implement/understand this from this article alone?"

## Phase 1: Collect

Gather primary sources only: papers that introduced key ideas, official lab/product blogs, posts from builders, canonical "build it from scratch" repositories. Not summaries. Not explainers.

Three ordered steps per source -- no shortcuts, no merging:

1. **Discover** -- use an installed search plugin (e.g., PipeLLM) to map the landscape, then deep-search the 2-3 most promising sub-topics. No plugin: use the environment's native web search. Output is a URL list; do not fetch content here.
2. **Fetch** -- every URL goes through `/read` when available. `/read` owns the proxy cascade, paywall detection, and platform routing (WeChat, Feishu, PDF, GitHub). Native fetch tools and raw `curl` silently fail on JS-heavy or paywalled sites and skip all of that. If `/read` is missing (Pre-check warned), fall back to native fetch and accept reduced coverage.
3. **File** -- tell `/read` the research project's source directory when one exists. If no directory was specified, let `/read` use a per-session temp directory and return the saved path. Move or index saved files into sub-topic directories after fetch returns. Move, don't refetch.

Target: 5-10 sources for a blog post, 15-20 for a deep technical survey.

## Phase 2: Digest

Work through the materials. For each piece: read it fully, keep what is good, cut what is not. At the end of this phase, cut roughly half of what was collected.

For key claims, ask before including in the outline:
- Does this idea appear in at least two different contexts from the same source?
- Can this framework predict what the source would say about a new problem?
- Is this specific to this source, or would any expert in the field say the same thing?

Generic wisdom is not worth distilling. Passes two or three: belongs in the outline. Passes one: background material. Passes zero: cut it.

When two sources contradict on a factual claim, note both positions and the evidence each gives. Do not silently pick one.

### Conversation Or Review Distillation

When the input is a recent conversation, project review, scorecard, or diagnostic report, treat it as raw material:

- Prefer already-distilled summaries, memory entries, and review outputs first; open raw transcripts only to verify a disputed detail or recover the exact source of a repeated pattern.
- Build a candidate matrix before editing durable guidance: source/project, repeated failure, transferable rule, target layer, evidence count, and redaction risk. Promote only candidates with cross-source support or a repeated failure in the same project family.
- Extract repeated workflow failures, invariants, and verifier surfaces.
- Drop dated line numbers, current-score framing, private paths, one-machine setup, and repo-specific commands unless the output is explicitly for that same repo.
- Map each durable lesson to its target layer: project docs, shared rules, skill references, or deterministic scripts.
- Prefer references or existing skill sections for adaptive workflow guidance; use scripts only for deterministic checks that can fail reliably without pr