add(docs): paper-based skill distillation guide (#71)

docs/paper-distillation.md

@@ -0,0 +1,110 @@
+# Paper-Based Skill Distillation
+
+Reverse-engineer the tacit knowledge embedded in published papers into reusable ResearchSkills.
+
+## Concept
+
+Published papers contain far more expertise than what appears on the surface. Behind every methods section lie implicit decisions, rejected alternatives, domain-specific heuristics, and hard-won intuitions that the authors never explicitly state. These can be reverse-engineered ("distilled") into ResearchSkills.
+
+The paper ["The Relic Condition: When Published Scholarship Becomes Material for Its Own Replacement"](https://arxiv.org/abs/2604.16116) (arXiv:2604.16116) discusses how published scholarship becomes raw material for AI systems — a dynamic that paper-based distillation makes explicit and constructive.
+
+This approach complements the conversation-based extraction (`/researchskills-extract`). Conversation extraction captures skills from live research sessions; paper distillation captures skills from the published record.
+
+## Process
+
+1. **Select a researcher's body of work.** Choose 3–5 key papers from the same researcher or tightly related group. Focus on papers where the methodology is novel or non-obvious — not survey papers.
+
+2. **Read for implicit decisions.** For each paper, ask:
+   - What alternatives existed but were not chosen? Why?
+   - What domain-specific heuristics are buried in the methods or supplementary material?
+   - What would a competent-but-non-expert researcher get wrong if they tried to replicate this?
+   - What failure modes does the paper hint at (e.g., "we found that X did not work")?
+
+3. **Identify recurring patterns.** Across the 3–5 papers, look for:
+   - Methodological choices that repeat across papers (likely a stable heuristic)
+   - Non-obvious parameter choices or preprocessing steps
+   - Warnings or caveats that appear in multiple works
+   - Techniques the author consistently avoids (and why)
+
+4. **Classify each pattern.** Map each extracted insight to one of the three memory types:
+   - **Procedural** — IF-THEN rules for research decisions (subtypes: `tie`, `no-change`, `constraint-failure`, `operator-fail`)
+   - **Semantic** — Facts that LLMs don't reliably know (subtypes: `frontier`, `non-public`, `correction`)
+   - **Episodic** — Concrete research episodes with transferable lessons (subtypes: `failure`, `adaptation`, `anomalous`)
+
+5. **Write the skill.** Use the standard ResearchSkills YAML+markdown template (see [Skill Schema Design](superpowers/specs/2026-04-11-skill-schema-design.md) for the full specification).
+
+## Prompt Template
+
+Copy-paste the following prompt into Claude Code, Codex, or ChatGPT. Replace the placeholder paper list with actual arXiv IDs or URLs.
+
+````
+I want to distill reusable research skills from published papers. Analyze the following papers and extract the implicit expertise embedded in them — the tacit decisions, methodological heuristics, and domain knowledge that a reader would need years of experience to notice.
+
+## Papers to analyze
+
+- arXiv:XXXX.XXXXX (replace with actual ID or URL)
+- arXiv:YYYY.YYYYY
+- arXiv:ZZZZ.ZZZZZ
+
+## Instructions
+
+1. Read each paper thoroughly. Focus on the methods, experimental setup, and any supplementary material.
+
+2. For each paper, identify:
+   - **Non-obvious methodological choices**: What did the authors choose that a naive researcher would not? Why?
+   - **Rejected alternatives**: What approaches were available but explicitly or implicitly avoided?
+   - **Hidden failure modes**: What pitfalls does the paper warn about or hint at?
+   - **Domain heuristics**: What parameter choices, preprocessing steps, or evaluation practices reflect deep domain knowledge?
+   - **Recurring patterns**: What techniques or principles appear across multiple papers by these authors?
+
+3. For each extracted insight, classify it as one of:
+   - **Procedural** (`tie` / `no-change` / `constraint-failure` / `operator-fail`) — a decision rule for research impasses
+   - **Semantic** (`frontier` / `non-public` / `correction`) — a fact LLMs don't reliably know
+   - **Episodic** (`failure` / `adaptation` / `anomalous`) — a concrete episode with a transferable lesson
+
+4. Output each skill in this YAML+markdown format, separated by `===`:
+
+```
+---
+name: short-kebab-case-name
+memory_type: procedural | semantic | episodic
+subtype: tie | no-change | constraint-failure | operator-fail | frontier | non-public | correction | failure | adaptation | anomalous
+domain: computer-science
+subdomain: machine-learning
+tags: [tag1, tag2, tag3]
+source_papers:
+  - arXiv:XXXX.XXXXX
+---
+
+## When
+[Trigger condition — when should an AI agent retrieve this skill?]
+
+## Decision
+[What to do and what NOT to do, with reasoning.]
+
+## Why
+[The underlying insight — why does this work or matter?]
+
+## Local Verifiers
+[How to check that this skill was applied correctly.]
+
+## Anti-exemplars
+[When NOT to use this skill.]
+===
+```
+
+## Rules
+
+- **Be specific.** "IF loss plateaus THEN try X" is weak. "IF loss plateaus after warmup when training >1B parameter Transformers with Adam THEN try X because Y" is strong.
+- **Prioritize non-obvious insights.** Skip anything a frontier LLM would already know from textbook knowledge.
+- **Preserve scientific accuracy.** Do not hallucinate claims the papers don't support.
+- **De-identify.** Remove private file paths, usernames, or internal URLs. Keep scientific content (materials, parameters, methods, model names).
+- **Dead ends are valuable.** Failed approaches and rejected alternatives are often the highest-value skills.
+````
+
+## Ethics
+
+- **Public papers only.** This process should only be applied to publicly available, peer-reviewed or pre-print papers. Do not distill from confidential manuscripts, unpublished drafts shared in confidence, or papers behind paywalls that you do not have legitimate access to.
+- **General insights, not proprietary details.** The extracted skills should capture general methodological insights — the kind of knowledge that advances a field. Do not extract proprietary datasets, confidential experimental configurations, or trade secrets.
+- **Attribution.** Always include `source_papers` in the skill metadata to credit the original authors. The goal is to amplify their expertise, not to obscure its origin.
+- **Respect author intent.** If a paper includes restrictions on reuse or derivative works, respect those terms.

readme.md

@@ -112,6 +112,13 @@ The command asks where your skills are, reads them, converts each one into the c
 
 </details>
 
+<details>
+<summary><strong>Method E: Distill Skills from Published Papers</strong></summary>
+
+Already have a list of key papers in your field? You can reverse-engineer the tacit expertise embedded in published work — the implicit decisions, rejected alternatives, and domain heuristics that never make it into abstracts. See the full guide: [**Paper-Based Skill Distillation →**](docs/paper-distillation.md)
+
+</details>
+
 > Don't see your field? [Propose a new area →](https://github.com/ScienceIntelligence/ResearchSkills/issues/new?template=04-propose-new-area.md) · Need a skill but can't write it yourself? [Request a skill →](https://github.com/ScienceIntelligence/ResearchSkills/issues/new?template=02-skill-request.yml)
 
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readme_zh.md

@@ -181,6 +181,13 @@ fp16 精度下 1e-8 会被规约为 0，Adam 的更新步骤相当于除以零
 
 参照[这个指南 →**](https://researchskills.ai/submit-manually)
 
+<details>
+<summary><strong>方式 D：从已发表论文中蒸馏 Skill</strong></summary>
+
+已经有你领域的关键论文列表？你可以从已发表的学术成果中逆向工程出隐性专业知识 — 那些隐藏在方法选择、被拒绝的替代方案和领域启发式中的隐性 know-how。完整指南：[**基于论文的 Skill 蒸馏 →**](docs/paper-distillation.md)
+
+</details>
+
 > 没有你的研究方向？[提议新领域 →](https://github.com/ScienceIntelligence/ResearchSkills/issues/new?template=04-propose-new-area.md) · 需要某个 Skill？[请求 Skill →](https://github.com/ScienceIntelligence/ResearchSkills/issues/new?template=02-skill-request.yml)
 
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