Research Material Scout

Research Material Scout

Use this skill when the user asks for research, material discovery, learning-material triage, or sends links with the 素材： prefix. Treat 调研： as the explicit active-research directive.

Mission

Build a high-signal learning and career material pipeline for the user.

Use the user's current, explicitly confirmed Decision Context to select the next learning action. Read its current version and distinguish user facts, source evidence, and assistant proposals. Career themes are defaults, not permanent weights: new responsibilities, a decision deadline, or a completed reading can change the order. Keep private context out of public queries and skill text.

Before recommending a first read, check existing Notes, managed material records, and the user's latest read-completion statements. Material importance and remaining reading effort are separate: a central paper already understood may need only one missing experiment, a design delta, or a later revisit. Do not invent a completed/archive transition; use the active lifecycle contract and an explicit disposition.

For user-directed note integration, the named target and source's primary domain take precedence over career mapping. Common discovery lanes include agent runtime/evaluation/memory, model learning, serving and RL systems, product workflows, and career signals; select among them from the current task rather than a fixed percentage split.

For decision-driven X discovery or benchmark reading recommendations, read the focused scout protocol.

Boundary With LoopX Material

This skill is the CS-Notes source-discovery and exact-reading adapter. It owns source recall, primary-source verification, reader maps, domain scoring, and the private CS-Notes landing decision.

When the connected project or goal explicitly activates Material Lifecycle, use the project-local managed loopx-material skill for generic store inventory, candidate/archive transitions, lossless migration, ranked-entry rebuild, bounded rerank, owner-gated apply, rollback, and audit. Pass exact-read evidence and the project-specific score into that workflow; do not duplicate or weaken its authority and losslessness gates here.

For CS-Notes, a managed authority pointer under .local/material-lifecycle/authority/current.json means the old candidate and archive Markdown files are immutable legacy content backing. Never append a candidate, archive an item, or edit the old Top30 in those files. Use the project-local loopx-material workflow and the private managed intake adapter. An explicit 素材： request authorizes candidate intake plus a ranking settlement in the same user workflow. Keep intake and ranking as separate receipts and, when ranking changes, separate authority revisions so CAS, preview, rollback, and audit remain independent.

Every intake must end with one explicit disposition: top_window, ranked_backlog, or no_change. Determine high value from exact-read evidence, S/A/B tier, current Decision Context, overlap, and artifact convertibility rather than tier alone. A high-value material must gain verified ranked membership in the Top30 or ranked backlog; it may not remain only an unranked candidate. no_change requires a reason and is reserved for lower-value or substantially duplicative material. If the Top30 is full, move displaced entries into the ranked backlog without loss. Do not move protected anchors without a changed Decision Context or explicit owner instruction.

The installed project skill does not itself activate Material Lifecycle. Without an active project- or goal-scoped profile, declared source authority, and current native owner authorization, this skill may research or read material but must not rewrite the managed store. An already authorized ordinary project source does not require a new Goal or a borrowed agent identity.

Source Strategy

Prefer primary sources for technical conclusions:

• Papers: arXiv, conference pages, official PDFs.
• Code: GitHub repositories, README, issues, releases.
• Products: official docs, release notes, engineering blogs.
• Internal/user links: Lark/ByteTech only when accessible through user-provided context or approved local tools.

Use social media, 微信公众号, 小红书, X/Twitter, 微博, and aggregators as discovery signals, not final truth.

When extracting mechanisms from a repo, quickstart, prompt, or code file, include file-level links in the user-facing answer and persisted notes/archive. For GitHub sources, prefer commit-pinned permalinks and record the commit read; avoid writing only a repo name or bare filename when a concrete source file drove the claim.

Use SenSight as the primary broad-recall backend when it is available:

• recent AI/Agent/RL/serving dynamics,
• social-platform opinions,
• author/researcher recent posts,
• link reading across 微信公众号 / 小红书 / X / 微博,
• keyword monitoring.

Codex remains responsible for verification, ranking, summarization, and local persistence.

Use ordinary web search, platform-specific readers, and Agent-Reach-like local tools as fallback or source-level readers, not as the primary discovery layer.

Implementation-First Catalogs

For Agent Harness / agent infra exploration, use implementation-first catalogs as a source-discovery layer before broad web search when available.

Current primary catalog:

• Agent Harness Engineering implementation-first catalog: https://github.com/Picrew/awesome-agent-harness

Use it as an index, not as evidence by itself:

1. Pick the relevant ETCLOVG layer first: execution, tooling, context, lifecycle, observability, verification, or governance.
2. Inspect the candidate repo's README, docs, releases, issues, or paper before ranking it.
3. Prefer implementation entries that can change the user's artifacts: sandbox boundary, tool registry/schema, state/context contract, handoff/workflow loop, trace/eval schema, policy/audit layer.
4. Do not dump many frameworks into Top30. Promote only high-signal projects through the current managed candidate authority, with read status and concrete artifact deltas.
5. Treat stars and catalog summaries as recall/ranking hints, not truth.

arXiv / Paper Reading Route

For arXiv papers, do not jump straight to PDF extraction unless HTML is unavailable.

Resolution order:

1. Normalize the paper id and version from any of these forms:
   • https://arxiv.org/abs/<id>
   • https://arxiv.org/pdf/<id>
   • https://arxiv.org/html/<id>vN
   • bare title + discovered arXiv id.
2. Try arXiv HTML first:
   • if a version is known, open https://arxiv.org/html/<id>vN;
   • if only bare id is known, inspect the abs page for the current version, then try https://arxiv.org/html/<id>v<version>;
   • also try https://arxiv.org/html/<id> if versioned HTML is not obvious.
3. Use the abs page for metadata, title, authors, abstract, version history, and links.
4. For 精读, method-heavy papers, or cases where HTML lacks needed appendix, math, algorithm, prompt, or caption detail, try the arXiv TeX source before PDF fallback:
   • download https://arxiv.org/src/<id> into .local/paper-cache/<id>-src.tar.gz;
   • unpack into .local/paper-cache/<id>-src/;
   • locate the entry .tex file, usually main.tex or the file containing \documentclass;
   • recursively inspect included .tex, .bib, figure captions, tables, algorithm blocks, appendices, and prompt/templates.
5. Fall back to PDF only when HTML/source is missing, blocked, malformed, or lacks the needed figures/tables.
6. If using PDF fallback, extract text into .local/paper-cache/ and explicitly say that the read path was PDF fallback.

Why this matters:

• arXiv HTML preserves section anchors, table/figure order, equation context, and is easier for user-side parallel reading.
• arXiv TeX source often preserves appendices, captions, algorithms, prompts, and bibliography context better than PDF text extraction.
• PDF extraction can lose figures, captions, math, and table structure; it is acceptable for quick scanning but weaker for 请你读 / 精读.
• For 请你读 / 精读, always provide the user-facing HTML link when it exists, even if Codex also used the PDF for extraction.

Paper / Research Reading Protocol

For papers, research reports, benchmark papers, method repos, arXiv / OpenReview links, and paper collections, 请你读 / 精读 must use the protocol in references/paper-reading-protocol.md.

Use it as a progressive-disclosure reference rather than copying it into every answer. It synthesizes Keshav's three-pass method, CMU 11-785's paper-reading recitation, and academic / PhD / AI research lenses into a concrete output contract, plus selected ideas from a local scan of research-related skills.

Operational defaults:

• 请你读 = read first, then answer with Keshav pass 1 plus targeted pass 2 on decision-relevant sections; escalate selected parts to pass 3 only when the material is high-value.
• 精读 = same output schema, but default to pass 2 plus selective pass 3: virtually reimplement the method, challenge assumptions, and produce artifact deltas.
• For paper radars or collections, first triage all visible papers with pass 1, then deep-read only the highest-leverage subset.
• Always state what was actually read: HTML, TeX source, PDF, repo paths, figures/tables, appendix, code, or only metadata.

Paper Radar Pattern

For recurring or paper-heavy exploration, use daily-paper-reader as a design reference, not as a dependency to install. Its useful increment is the pipeline shape: intent profiles -> multi-lane recall -> fusion/rerank -> evidence scoring -> deep/quick/carryover selection.

Adopt these patterns:

1. Convert the user's current goal into 1-3 intent profiles. Each profile should have:
   • a short tag,
   • 3-6 atomic English keywords for exact/BM25-style recall,
   • 1-4 semantic intent queries for embedding/web search,
   • an explicit source lane such as arXiv, OpenReview, conference proceedings, domain preprint servers, GitHub, official docs, social, or implementation catalog.
2. Run more than one lane when possible. Combine exact keywords, semantic queries, and source-specific searches instead of relying on one giant query.
   • For AI / agent infra paper search, treat arXiv, OpenReview, and major venue lanes (NeurIPS, ICLR, ICML, ACL, EMNLP, AAAI) as high-value paper sources.
   • For cross-domain or bio/chem/medical-adjacent topics, optionally add bioRxiv, medRxiv, and ChemRxiv as separate lanes.
   • Do not sweep every source by default. Pick lanes that match the topic, then record which lane found each candidate.
3. Fuse and rank qualitatively:
   • keep at least one strong result from each active lane before global ranking,
   • prefer candidates hit by multiple lanes,
   • require an evidence sentence for every S/A candidate.
4. Split results into deep, quick, background, and carryover:
   • deep: user should personally read or Codex should deep-read next;
   • quick: Codex summary is enough now;
   • background: useful but not active;
   • carryover: high-signal but not yet processed, keep it visible for the next exploration pass instead of letting daily freshness bury it.
5. For papers where figures/tables are central, inspect HTML, TeX, PDF figures, tables, or repo assets before ranking when feasible.

Do not copy these parts by default: GitHub Actions / Pages deployment, Supabase schema, remote public embedding/rerank services, front-end panels, API keys, or the exact prompt text. Keep the local skill lightweight and source-agnostic.

Multi-Source Paper Exploration Mesh

For paper-heavy exploration, absorb daily-paper-reader's paper data-source coverage rather than its hosted search/deployment stack. The key improvement is to search several paper-source lanes in parallel and then merge them with Codex's existing source stack.

Default paper lanes:

• arXiv: fast preprint and recent-paper recall.
• OpenReview: ICLR / NeurIPS / ICML / AAAI submissions, public reviews, decisions, and withdrawn-public papers when visible.
• Venue lanes: NeurIPS, ICLR, ICML, ACL, EMNLP, AAAI; use official venue pages, OpenReview, ACL Anthology, AAAI/OJS, proceedings pages, or targeted web search as appropriate.
• Domain preprint lanes: bioRxiv, medRxiv, ChemRxiv; use only when the topic is bio / medical / chemistry / scientific-agent adjacent.

Combine these with non-paper lanes:

• GitHub repos, release notes, project pages, Papers with Code-style project pages when available;
• official product or framework docs;
• implementation-first catalogs for agent infra;
• SenSight / social / aggregators as broad recall, never as final fact sources.

Operational rule:

1. For a focused paper search, choose at least 2 relevant paper lanes plus 1 implementation or docs lane when possible.
2. For a broader paper radar, choose 3-6 lanes and keep per-lane coverage visible.
3. Do not wait for a local database or hosted search service to exist. Use official source pages, platform APIs, source-specific search, ordinary web search, and available local readers in parallel.
4. Rank by cross-lane agreement, primary-source quality, code/data availability, and direct artifact impact.
5. Record which lane found each S/A candidate and which high-value lane was checked but produced no useful hit.

If this skill has scripts/multi_source_paper_explore.py, use it as the first-pass paper-source recall helper for paper-heavy tasks:

python3 .codex/skills/research-material-scout/scripts/multi_source_paper_explore.py \
  --query "<topic>" \
  --query "<alternate wording or known title>" \
  --sources arxiv,openreview,openalex,biorxiv,medrxiv,chemrxiv,venue-hints

Repeat --query for intent-query expansion. Narrow --sources to topic-fit lanes when domain preprint servers are likely irrelevant.

Use a dual-track exploration flow for real paper-heavy work:

1. Run the script for structured paper-lane recall and per-lane evidence.
2. In parallel, run Codex's existing web / SenSight / GitHub / official-doc search for project pages, repos, social/aggregator leads, implementation evidence, and missed terminology.
3. Merge the two result sets before ranking. Treat script-only hits as candidates to verify, and web-only hits as leads to trace back to primary papers / code / docs.

The script is only a recall layer; it complements rather than replaces the older exploration stack.

SenSight Backend

Discover callable SenSight tools or the current project's private .local/sensight-skill-source/sensight directory. Do not assume a previous machine's absolute path or installed version. If the source exists, read its SKILL.md and actual command help before invoking it. Do not execute placeholder installers.

When SenSight is absent or unavailable, record that limitation and continue with web search plus an authenticated platform reader and primary-source verification. Optional discovery tooling should not block already authorized research. If its authentication is essential, follow its current authorization flow; never treat an auth response as source content.

Useful Actions

Use these actions as retrieval, not as final authority:

Examples:

python3 scripts/sensight.py retrieve_summarize \
  --query "Agent infra 最新进展" \
  --enhance_query "最近一周 Agent infra、OpenClaw、Claude Code、agent memory、long-running coding agent 的高质量技术动态" \
  --size 20 \
  --result_form article_summary

python3 scripts/sensight.py social_search \
  --query "GPT 5.4 评价" \
  --platforms 1 2 3 4 \
  --size 20

python3 scripts/sensight.py search_author_posts \
  --platform 1 \
  --author_name "Anthropic"

Authenticated Social Link Reading

Use public metadata when sufficient; for incomplete posts, replies, or signed-in search, read the installed ego-browser skill and follow its current TaskSpace and API contract. Do not copy stale browser methods into this skill. Reuse the user's sign-in; inspect visible posts and original links, and close only task-owned surfaces.

Capture relevant author, date, post URL, primary-source links, read scope, and the claim to verify. Avoid unrelated recommendations, DMs, account statistics, or a bulk feed dump. Inspect needed images through supported browser/media tools; do not download remote media to work around display restrictions. Store private evidence under .local/. A social post can be a first-party statement of its author's claim, but reported experimental results still require the paper, code, or data protocol.

Agent-Reach Complement

Agent-Reach (https://github.com/Panniantong/Agent-Reach) is useful as a complementary local scaffolding layer, especially when SenSight is unavailable, too aggregated, or lacks a channel.

Use it as a design reference or optional install, not the default primary source.

What it adds:

• source-level tools rather than platform-side aggregation,
• web reading through Jina Reader,
• YouTube / Bilibili transcript extraction through yt-dlp,
• GitHub through gh,
• RSS through feedparser,
• Reddit / Twitter / 小红书 / 抖音 / LinkedIn via separate upstream CLIs or MCP tools,
• agent-reach doctor style capability diagnostics.

When it helps more than SenSight:

• Need to inspect a specific URL/video/repo/thread, not just discover candidates.
• Need an open-source, auditable local route.
• Need video subtitles, RSS feeds, GitHub issues/PRs, or Reddit threads.
• SenSight auth is unavailable or results are too summarized.

When SenSight should stay primary:

• Broad topic discovery.
• Recent social sentiment.
• Cross-platform opinion summaries.
• AI papers/blogs/model-release radar.
• Low-maintenance material scouting.

Do not install Agent-Reach automatically unless the user asks. It may install many dependencies and configure cookies/proxies. If installed, keep secrets/cookies local and never commit them.

Intake Workflow

Directive convention:

• 素材：<link/text> means intake. Read, classify, summarize, preserve the original link, and append one managed candidate through exact-read evidence, immutable content backing, authority CAS, readback, and receipt.
• 调研：<question/topic> means active research. Use broad recall plus source verification, then write high-signal candidates and recommendations.
• 整理笔记：<link/text> or "整理笔记 + named note/theme" means direct note integration. The output surface is Notes/, not the candidate library by default. Named target and source-domain taxonomy win over career priority.
• 请你读：<material id/link/title> means Codex reads first, then returns an illustrated mechanism-first summary and a reader map in the conversation. Do not organize or edit notes during this command. For papers / research artifacts, load references/paper-reading-protocol.md and follow its output contract. 精读 is a compatibility alias with the same output boundaries, but defaults to deeper pass-3 reconstruction when warranted.
• 读完：<material id/link/title + user notes> means close the reading loop. Update the best Notes/ landing before archiving unless the content is private-only. Treat user-highlighted points as retention requirements: concrete prompts, env flags, schema fields, tool/API names, figures/tables, failure cases, doubts, and comparison phrases should be preserved in the public note when safe and conceptually useful; .local may keep raw/private/full detail, but must not be the only landing for points the user explicitly asked to remember.
• 继续调研 means continue the latest active-research theme, but only if adding new sources or a new decision-relevant synthesis.

For each user-provided material:

0. Classify the request intent before choosing tools or files:
   • 整理笔记: use the repository note-integration workflow. If the user named a file/section, inspect that target first; if not, classify the source's primary contribution such as model algorithm, training method, inference system, agent runtime, product strategy, or career signal, then find the matching note. Do not default to Agent infra just because the material is AI-related.
   • 素材: intake to the current managed material authority. Do not write the legacy candidate Markdown.
   • 调研: prioritize by the user's current confirmed Decision Context.
   • 请你读 / 精读: read and explain in the conversation, with relevant images displayed inline. Save only source caches, figure assets and reading evidence under .local/ or the task artifact directory; do not edit Notes/, its indexes, or the material lifecycle/ranking. Note integration requires an explicit 整理笔记 or 读完 request; an artifact delta here is a proposal, not permission to implement it.