research-lit

Research Literature Review

Research topic: $ARGUMENTS

Constants

• REVIEWER_BACKEND = llm-chat — Default: llm-chat MCP (defer to LLM_MODEL env). Override with — reviewer: oracle-pro for GPT-5.4 Pro via Oracle MCP.

• PAPER_LIBRARY — Local PDF collection, organized as two channels that are both globbed at retrieval time (Step 0c):

  1. literature/ — user-curated channel. PDFs the user manually places here (reading list, must-cite references, annotated copies). Read-only to the pipeline — never auto-written or deleted, so anything dropped here is safe and always considered.
  2. papers/ — machine-managed channel. The pipeline auto-downloads arXiv PDFs here (only when ARXIV_DOWNLOAD = true, or when REF_PAPER is an arXiv URL — see Phase 0.5 of /auto-claim). Regenerable scratch — safe to delete and re-fetch.
  3. Optionally, a custom path the user sets in CLAUDE.md under ## Paper Library.

  Precedence: when the same paper appears in both channels (matched by normalized title), the literature/ copy wins.

• MAX_LOCAL_PAPERS = 20 — Maximum number of local PDFs to scan (read first 3 pages each). If more are found, prioritize by filename relevance to the topic.

• ARXIV_DOWNLOAD = false — When true, download top 3-5 most relevant arXiv PDFs to PAPER_LIBRARY after search. When false (default), only fetch metadata (title, abstract, authors) via arXiv API — no files are downloaded.

• ARXIV_MAX_DOWNLOAD = 5 — Maximum number of PDFs to download when ARXIV_DOWNLOAD = true.

• OUTPUT_DIR = idea-stage/ — Where to write the two files in Step 5. Defaults to idea-stage/ (created if missing) regardless of caller; override with — output-dir: <path>. Step 5 writes two files unconditionally at the end of every run:

  1. <OUTPUT_DIR>/RESEARCH_LIT.md — raw retrieval dump (per-paper abstracts/intros + provenance). Pure audit artifact — never read back by any downstream skill, only for human inspection of what was retrieved before synthesis. Not a resume gate.
  2. <OUTPUT_DIR>/LANDSCAPE.md — synthesized landscape (structured paper table + 3-5 paragraph narrative + structural gaps). Read by /idea-creator Phase 1 from disk, then pasted into its Phase 2 llm-chat brainstorm prompt — this is the canonical inter-phase data carrier when /research-lit → /idea-creator are chained (e.g. via /auto-claim). Standalone /idea-creator calls (no prior /research-lit) run their own fallback survey and write LANDSCAPE.md themselves using this same template. Still not a resume gate — upstream skills (/auto-claim) key resume on the downstream deliverable IDEA_REPORT.md, not on LANDSCAPE.md.

💡 Overrides:

• /research-lit "topic" — paper library: ~/my_papers/ — custom local PDF path
• /research-lit "topic" — extra: semantic-scholar — also run Semantic Scholar API (published venue papers)
• /research-lit "topic" — extra: deepxiv — also run DeepXiv progressive retrieval
• /research-lit "topic" — extra: exa — also run Exa broad web search
• /research-lit "topic" — extra: semantic-scholar, deepxiv — combine multiple extras
• /research-lit "topic" — arxiv download: true — download top relevant arXiv PDFs
• /research-lit "topic" — arxiv download: true, max download: 10 — download up to 10 PDFs

Data Sources

This skill has a fixed base source set that runs on every invocation, plus three opt-in extras toggled by — extra:.

Base sources (always run all, anyone not skipable)

zotero + obsidian + local + web + arxiv + mechanic-db.

If any of Zotero / Obsidian / local PDFs isn't configured or present, that one is skipped silently — but the rest of the base set still runs. web and arxiv (via the /arxiv skill) are always available and always invoked.

Opt-in extras (via — extra:)

These three are NOT part of the base set because each one is a costly external API or CLI that should only run when the upstream task explicitly calls for it. Toggle with — extra: <name> (comma-separated for multiple):

• semantic-scholar — Semantic Scholar API for published venue papers (IEEE/ACM/Springer) with citation counts and TLDR
• deepxiv — DeepXiv CLI progressive retrieval (search → brief → head → section)
• exa — Exa AI-powered broad web search with content extraction

If — extra: is absent, none of these run.

Source Table

Priority	Source	ID	How to detect	What it provides
1	Zotero (via MCP)	zotero	Try calling any mcp__zotero__* tool — if unavailable, skip	Collections, tags, annotations, PDF highlights, BibTeX, semantic search
2	Obsidian (via MCP)	obsidian	Try calling any mcp__obsidian-vault__* tool — if unavailable, skip	Research notes, paper summaries, tagged references, wikilinks
3	Local PDFs	local	Glob: papers/**/*.pdf, literature/**/*.pdf	Raw PDF content (first 3 pages)
4	mechanic-db SEARCH (cloud)	mechanic-db	Always available (via SKILL /mechanic-db-search)	~14k AI-interpretability papers and ~157M cross-disciplinary papers (e.g., from neuroscience, cognitive science, physics, biology, and the humanities), all organized as a knowledge graph.
5	Web search	web	Always available	arXiv, Semantic Scholar, Google Scholar
6	arXiv API	arxiv	Always available (via SKILL /arxiv)
7	Semantic Scholar API	semantic-scholar	scripts/semantic_scholar_fetch.py exists	Published venue papers (IEEE, ACM, Springer) with structured metadata: citation counts, venue info, TLDR. Opt-in via — extra: semantic-scholar
8	DeepXiv CLI	deepxiv	scripts/deepxiv_fetch.py and installed deepxiv CLI	Progressive paper retrieval: search, brief, head, section, trending, web search. Opt-in via — extra: deepxiv
9	Exa Search	exa	scripts/exa_search.py and installed exa-py SDK	AI-powered broad web search with content extraction (highlights, text, summaries). Covers blogs, docs, news, companies, and research papers beyond arXiv/S2. Opt-in via — extra: exa

Workflow

Execution order is strict. Run steps in numbered order: 0 → 0a → 0b → 0c → 0d → 1 → 2 → 3 → 4 → 5. Each step must produce its result block (or an explicit "skipped: " note) before the next step begins. The one and only place parallelism is allowed is inside Step 1, where the mechanic-db SEARCH lane and the external-search lane run concurrently (see Step 1). Every other step transition is strictly sequential — do not reorder, do not parallelize across steps, and do not skip ahead to a later step because it feels more "important" or "costly". In particular: the mechanic-db SEARCH (Step 1, Lane A) is very important and DO NOT omit unless the cloud service is unavailable.

Step 0: Load Prior Banlist (if available)

Skip this step entirely if research-wiki/ does not exist. This is the reader counterpart to Step 6 (which writes the wiki). It seeds the search with prior context and — critically — carries the failed-idea banlist forward into LANDSCAPE.md (Step 5.2), so /idea-creator's generation step can avoid repeating dead ends.

if research-wiki/query_pack.md exists AND is less than 7 days old:
    Read query_pack.md and use it as follows:
    - Treat listed gaps as priority search seeds for Step 1 (both lanes)
    - Treat top papers as known prior work (do not re-search them)
    - Extract the "Failed Ideas (avoid repeating)" section verbatim and carry it
      forward — it becomes the Banlist in LANDSCAPE.md Step 5.2 (do NOT discard it)
else if research-wiki/ exists but query_pack.md is stale or missing:
    python3 tools/research_wiki.py rebuild_query_pack research-wiki/
    Then read query_pack.md as above
else:
    skip — no wiki, banlist is empty

Step 0a: Search Zotero Library (if available)

Skip this step entirely if Zotero MCP is not configured.

Try calling a Zotero MCP tool (e.g., search). If it succeeds:

1. Search by topic: Use the Zotero search tool to find papers matching the research topic
2. Read collections: Check if the user has a relevant collection/folder for this topic
3. Extract annotations: For highly relevant papers, pull PDF highlights and notes — these represent what the user found important
4. Export BibTeX: Get citation data for relevant papers (useful for /paper-write later)
5. Compile results: For each relevant Zotero entry, extract:
   • Title, authors, year, venue
   • User's annotations/highlights (if any)
   • Tags the user assigned
   • Which collection it belongs to

📚 Zotero annotations are gold — they show what the user personally highlighted as important, which is far more valuable than generic summaries.

Step 0b: Search Obsidian Vault (if available)

Skip this step entirely if Obsidian MCP is not configured.

Try calling an Obsidian MCP tool (e.g., search). If it succeeds:

1. Search vault: Search for notes related to the research topic
2. Check tags: Look for notes tagged with relevant topics (e.g., #diffusion-models, #paper-review)
3. Read research notes: For relevant notes, extract the user's own summaries and insights
4. Follow links: If notes link to other relevant notes (wikilinks), follow them for additional context
5. Compile results: For each relevant note:
   • Note title and path
   • User's summary/insights
   • Links to other notes (research graph)
   • Any frontmatter metadata (paper URL, status, rating)

📝 Obsidian notes represent the user's processed understanding — more valuable than raw paper content for understanding their perspective.

Step 0c: Scan Local Paper Library

Before searching online, check if the user already has relevant papers locally:

1. Locate library: Check PAPER_LIBRARY paths for PDF files

   Glob: papers/**/*.pdf, literature/**/*.pdf
   

2. De-duplicate against Zotero: If Step 0a found papers, skip any local PDFs already covered by Zotero results (match by filename or title).

3. Filter by relevance: Match filenames and first-page content against the research topic. Skip clearly unrelated papers.

4. Summarize relevant papers: For each relevant local PDF (up to MAX_LOCAL_PAPERS):

   • Read first 3 pages (title, abstract, intro)
   • Extract: title, authors, year, core contribution, relevance to topic
   • Flag papers that are directly related vs tangentially related

5. Build local knowledge base: Compile summaries into a "papers you already have" section. This becomes the starting point — external search fills the gaps.

📚 If no local papers are found, skip to Step 1. If the user has a comprehensive local collection, the external search can be more targeted (focus on what's missing).

Step 0d: Clarify an ambiguous topic (ask before the expensive search)

A survey aimed at the wrong reading of the topic wastes the slow cloud call and produces an off-target LANDSCAPE.md. Before Step 1, judge whether $ARGUMENTS is genuinely ambiguous — two or more plausible interpretations that would send the search (and the downstream ideas) in materially different directions. Typical triggers: an acronym/term with multiple field meanings; underspecified scope (method vs. phenomenon vs. application vs. survey, unclear time window, unclear model/domain); a cross-domain phrase that could be AI-interpretability or a human-science field; a named entity that resolves several ways.

Worked example — "LLM belief". Genuinely ambiguous; readers emphasize different things. Plausible readings to offer: (a) belief representation / probing — does the model linearly encode a truth/belief direction in its activations; (b) belief revision & consistency — how stated beliefs update under evidence or contradict across a conversation; (c) epistemic calibration — confidence/uncertainty vs. correctness; (d) theory-of-mind / belief attribution — modeling other agents' beliefs (false-belief tasks). Each routes to different keywords, db, enums, and competitive sets.

Decision:

• First, try to disambiguate from context (R10): if task.md, the upstream research goal, prior turns, the Step 0 query_pack.md, or a caller-passed scope already pin the reading, use that — no need to ask.
• Else, if clearly ambiguous and you can ask → use the AskUserQuestion tool to present 2–4 candidate interpretations as an options box, most-likely first and labelled (recommended), each with a one-line note on what the survey would then cover. Keep it to one focused question (two at most). Search according to the user's choice.
• Else (unambiguous, or non-interactive) → proceed with the single most likely interpretation and record it in LANDSCAPE.md's Scope line ("Interpreted as: …"). Never manufacture ambiguity for a clear topic — a needless prompt is worse than a sensible default.

Step 1: Search (mechanic-db ‖ external — two lanes, run concurrently)

Precondition: Steps 0, 0a, 0b, 0c, 0d have each produced their result block (or an explicit "skipped: " note). Do not start this step until then.

Parallelism — the only place it is allowed. This step has two lanes:

• Lane A — mechanic-db cloud SEARCH (slow, ~3-10 min per call)
• Lane B — external search (WebSearch + arXiv + opt-in extras)

Launch Lane A first — it is the slow cloud call — then, without waiting for it to return, run Lane B in parallel while Lane A is in flight. Both lanes must complete (or be marked "skipped: ") before Step 2 begins. This concurrency is confined to Step 1; no other steps overlap. De-duplicate Lane A results against Lane B by normalized title once both lanes return.

Lane A: SEARCH (mechanic-db) — launch first

This lane runs on every invocation unless the cloud SEARCH service is unavailable — in which case the skill silently records "mechanic-db skipped" in the "Sources scanned" line of RESEARCH_LIT.md and moves on. It is the integration point for the cloud mechanic_database SEARCH service (interp_db + sciatlas_db).

1. Build the decomposed query using the Agent's full context. The Agent sees task.md, the upstream research goal, prior search results and analysis — use that context to route the query into at most one sub-query per database (interp_db / sciatlas_db), packing any cross-domain breadth into the relevant database's single sub-query. The JSON schema, routing rules, and closed enums are documented in /mechanic-db-search.

2. Invoke /mechanic-db-search with the crafted query. The skill then calls the mechanic-db MCP server's search_papers tool.

3. Read the result JSON. If "skipped": true, this source is unavailable for this run — note it in RESEARCH_LIT.md and move on. Otherwise, merge the returned papers[] into the downstream paper pool. De-duplicate against other sources by normalized title.

4. Multi-round refinement. When after Step 3 the merged corpus has clear gaps (sub-area entirely missing, off-topic flood, recency mismatch), refine the decomposition (sharpen semantic_query / keywords / hyde_text, fix the interp enums, flip temporal_mode, or set year_min / recent_alpha) and call /mechanic-db-search again. Cap at 3 rounds. Each round produces a fresh result JSON in ./mechanic_db_cache/.

Lane B: External search (web + arXiv + Semantic Scholar + DeepXiv + Exa)

• Use WebSearch to find recent papers on the topic — use 5+ different query formulations (the distinct queries you ran are recorded in RESEARCH_LIT.md's "Query formulations used" line)
• Check arXiv, Semantic Scholar, Google Scholar
• Focus on papers from last 2 years unless studying foundational work; for the frontier, also pull arXiv preprints from the last 6 months
• Read the abstracts and introductions of the top 15-20 papers, ranked by relevance to the research task
• De-duplicate: Skip papers already found in Zotero, Obsidian, or local library

arXiv API search (always runs, no download by default):

Locate the fetch script and search arXiv directly:

# Resolve arxiv_fetch.py from the sibling /arxiv skill.
SCRIPT="${CLAUDE_SKILL_DIR}/../arxiv/scripts/arxiv_fetch.py"
[ -f "$SCRIPT" ] || SCRIPT=""

# Search arXiv API for structured results (title, abstract, authors, categories).
[ -n "$SCRIPT" ] && python3 "$SCRIPT" search "QUERY" --max 10

If arxiv_fetch.py is not found at the sibling skill path, use the inline-Python arXiv-API fallback documented in /arxiv (Step 2) — arXiv is a base source and must run, so do not silently drop to WebSearch-only.

The arXiv API returns structured metadata (title, abstract, full author list, categories, dates) — richer than WebSearch snippets. Merge these results with WebSearch findings and de-duplicate.

Semantic Scholar API search (only when semantic-scholar is in sources):

When — extra: semantic-scholar is specified, search for published venue papers beyond arXiv:

S2_SCRIPT="${CLAUDE_SKILL_DIR}/scripts/semantic_scholar_fetch.py"
[ -f "$S2_SCRIPT" ] || S2_SCRIPT=""

# Search for published CS/Engineering papers with quality filters
[ -n "$S2_SCRIPT" ] && python3 "$S2_SCRIPT" search "QUERY" --max 10 \
    --fields-of-study "Computer Science,Engineering" \
    --publication-types "JournalArticle,Conference"

If semantic_scholar_fetch.py is not found, skip silently.

Why use Semantic Scholar? Many IEEE/ACM journal papers are NOT on arXiv. S2 fills the gap for published venue-only papers with citation counts and venue metadata.

De-duplication between arXiv and S2: Match by arXiv ID (S2 returns externalIds.ArXiv):

• If a paper appears in both: check S2's venue/publicationVenue — if it has been published in a journal/conference (e.g. IEEE TWC, JSAC), use S2's metadata (venue, citationCount, DOI) as the authoritative version, since the published version supersedes the preprint. Keep the arXiv PDF link for download.
• If the S2 match has no venue (still just a preprint indexed by S2): keep the arXiv version as-is.
• S2 results without externalIds.ArXiv are venue-only papers not on arXiv — these are the unique value of this source.

DeepXiv search (only when deepxiv is in sources):

When — extra: deepxiv is specified, use the DeepXiv adapter for progressive retrieval:

# Resolve deepxiv_fetch.py from this skill's bundled scripts.
DEEPXIV_SCRIPT="${CLAUDE_SKILL_DIR}/scripts/deepxiv_fetch.py"
[ -f "$DEEPXIV_SCRIPT" ] || DEEPXIV_SCRIPT=""

[ -n "$DEEPXIV_SCRIPT" ] && python3 "$DEEPXIV_SCRIPT" search "QUERY" --max 10

Then deepen only for the most relevant papers:

[ -n "$DEEPXIV_SCRIPT" ] && python3 "$DEEPXIV_SCRIPT" paper-brief ARXIV_ID
[ -n "$DEEPXIV_SCRIPT" ] && python3 "$DEEPXIV_SCRIPT" paper-head ARXIV_ID
[ -n "$DEEPXIV_SCRIPT" ] && python3 "$DEEPXIV_SCRIPT" paper-section ARXIV_ID "Experiments"

If scripts/deepxiv_fetch.py or the deepxiv CLI is unavailable, skip this source gracefully and continue with the remaining requested sources.

Why use DeepXiv? It is useful when a broad search should be followed by staged reading rather than immediate full-paper loading. This reduces unnecessary context while still surfacing structure, TLDRs, and the most relevant sections.

De-duplication against arXiv and S2:

• Match by arXiv ID first, DOI second, normalized title third
• If DeepXiv and arXiv refer to the same preprint, keep one canonical paper row and record deepxiv as an additional source
• If DeepXiv overlaps with S2 on a published paper, prefer S2 venue/citation metadata in the final table, but keep DeepXiv-derived section notes when they add value

Exa search (only when exa is in sources):

When — extra: exa is specified, use the Exa tool for broad AI-powered web search with content extraction:

EXA_SCRIPT="${CLAUDE_SKILL_DIR}/scripts/exa_search.py"
[ -f "$EXA_SCRIPT" ] || EXA_SCRIPT=""

# Search for research papers with highlights
[ -n "$EXA_SCRIPT" ] && python3 "$EXA_SCRIPT" search "QUERY" --max 10 --category "research paper" --content highlights

# Search for broader web content (blogs, docs, news)
[ -n "$EXA_SCRIPT" ] && python3 "$EXA_SCRIPT" search "QUERY" --max 10 --content highlights

If scripts/exa_search.py or the exa-py SDK is unavailable, skip this source gracefully and continue with the remaining requested sources.

Why use Exa? Exa provides AI-powered search across the broader web (blogs, documentation, news, company pages) with built-in content extraction. It fills a gap between academic databases (arXiv, S2) and generic WebSearch by returning richer content with each result.

De-duplication against arXiv, S2, and DeepXiv:

• Match by URL first, then normalized title
• If Exa returns an arXiv paper already found by arXiv/S2, prefer the structured metadata from those sources
• Exa results from non-academic domains (blogs, docs, news) are unique value not covered by other sources

Optional PDF download (only when ARXIV_DOWNLOAD = true):

After all sources are searched and papers are ranked by relevance:

# Download top N most relevant arXiv papers
[ -n "$SCRIPT" ] && python3 "$SCRIPT" download ARXIV_ID --dir papers/

• Only download papers ranked in the top ARXIV_MAX_DOWNLOAD by relevance
• Skip papers already in the local library
• 1-second delay between downloads (rate limiting)
• Verify each PDF > 10 KB

Step 2: Analyze Each Paper

For each relevant paper (from all sources), extract:

• Problem: What gap does it address?
• Method: Core technical contribution (1-2 sentences)
• Results: Key numbers/claims
• Relevance: How does it relate to our work?
• Source: Where we found it (Zotero/Obsidian/local/web) — helps user know what they already have vs what's new

Step 3: Synthesize

• Group papers by approach/theme
• Identify consensus vs disagreements in the field
• Note recurring limitations mentioned in "Future Work" sections, and open problems stated by multiple papers
• Identify structural gaps — scan explicitly for each of these kinds (this taxonomy feeds the "Structural Gaps" section of LANDSCAPE.md Step 5.2):
  • Methods that work in domain A but haven't been tried in domain B
  • Contradictory findings between papers (opportunity for resolution)
  • Assumptions that everyone makes but nobody has tested
  • Scaling regimes that haven't been explored
  • Diagnostic questions that nobody has asked
• If Obsidian notes exist, incorporate the user's own insights into the synthesis

Step 4: Output

Present as a structured literature table:

| Paper | Venue | Method | Key Result | Relevance to Us | Source |
|-------|-------|--------|------------|-----------------|--------|

Plus a narrative summary of the landscape (3-5 paragraphs).

If Zotero BibTeX was exported, include a references.bib snippet for direct use in paper writing.

Step 5: Save

5.1 — Raw retrieval dump (always, unconditional)

Write the raw retrieved content (every paper actually fetched in Steps 1–3, before synthesis) to <OUTPUT_DIR>/RESEARCH_LIT.md. Default OUTPUT_DIR is idea-stage/ (mkdir -p it if missing). This is the audit trail for what was pulled, not what it means — keep abstracts/intros verbatim, do not paraphrase.

Suggested file template:

# Raw Literature Retrieval: <topic from $ARGUMENTS>

**Date**: <YYYY-MM-DD>
**Query**: <one-line from $ARGUMENTS>
**Sources scanned**: <which of mechanic-db / arXiv / S2 / Zotero / Obsidian / local / Exa / DeepXiv were actually queried; mark absent ones
**Query formulations used**:
- <query 1>
- <query 2>
- ...

---

## Retrieved Papers

### Paper 1: <Title>
- **Authors**: <author list>
- **Year**: <year>
- **Venue**: <venue or "arXiv preprint">
- **Source**: <mechanic-db / arXiv API / Semantic Scholar / Zotero / Obsidian / local PDF / WebSearch / Exa / DeepXiv>
- **Identifier**: <arXiv ID / DOI / file path>
- **URL**: <link if available>

**Abstract**:
<full abstract text, verbatim>

**Introduction / first pages** (only when available — local PDFs read first 3 pages; arXiv via WebFetch when fetched):
<intro text, verbatim>

---

### Paper 2: ...

Order papers by relevance to the topic (most relevant first) or by source priority (Zotero → Obsidian → local → mechanic-db results → arXiv API → WebSearch). Note papers de-duplicated across sources by listing all matched sources in the Source field (e.g., Source: arXiv API + WebSearch).

5.2 — Synthesized landscape (always, unconditional)

Write the Step 4 output — the structured paper table + 3-5 paragraph narrative + structural gaps — to <OUTPUT_DIR>/LANDSCAPE.md. This is the digested view: what downstream phases (idea brainstorm prompts, gap-driven idea generation) actually consume conceptually. The raw papers behind it live in RESEARCH_LIT.md.

Suggested file template:

# Landscape: <topic from $ARGUMENTS>

**Date**: <YYYY-MM-DD>
**Scope**: <one-line scope, including any year cutoff; if the topic was ambiguous, lead with "Interpreted as: <disambiguated reading>" per Step 0d>
**Based on**: <N> retrieved papers — see `RESEARCH_LIT.md` for the raw retrieval dump

---

## 1. Structured Paper Table

| Paper | Venue | Method | Key Result | Relevance to Us | Source |
|-------|-------|--------|------------|-----------------|--------|

## 2. Core Landscape Narrative

<3-5 paragraphs describing the current state of the field, grouped by sub-direction / approach, with consensus and disagreements called out>

## 3. Sub-direction-Specific Work

<group by sub-direction; for each, list the most representative works with one-line takeaways and the gap it leaves>

## 4. Structural Gaps

<explicit gaps from Step 3, one per bullet, each tied to a competitive set>:
- **Gap G1** — <description> — *Competitive set*: <papers> — *Why open*: <reason>
- **Gap G2** — ...

## 5. Banlist — Failed Ideas (do not regenerate)

<paste the "Failed Ideas (avoid repeating)" entries extracted from `research-wiki/query_pack.md` in Step 0 when available, verbatim (title + why it failed). These are ideas already tried and abandoned; `/idea-creator` pastes this section into its brainstorm prompt so the generator does not re-propose them or close variants. If no prior banlist is active, write exactly: _(no prior banlist)_>
- **<failed idea title>** — <why it failed>
- ...

Important — file roles differ:

• RESEARCH_LIT.md is a pure audit artifact — never read back by any downstream skill. Only for human inspection.
• LANDSCAPE.md is the canonical inter-phase data carrier: /idea-creator Phase 1 reads it from disk when present, and Phase 2 pastes it into the llm-chat brainstorm prompt. Standalone /idea-creator calls (no prior /research-lit) run a fallback survey and write LANDSCAPE.md themselves using the Step 5.2 template.

Neither file is a resume gate. Overwriting on re-run is intended: both files always reflect the most recent retrieval. Resume protocols in upstream skills must continue keying off their own downstream deliverables (e.g. IDEA_REPORT.md), never off these files. Rationale: if IDEA_REPORT.md is missing, we have no proof the Phase 1 → Phase 2 chain completed; re-running Phase 1 to overwrite LANDSCAPE.md fresh is safer than trusting an interrupted run's leftovers.

5.3 — Optional saves (only when explicitly requested)

• Save downloaded paper PDFs to papers/ (when ARXIV_DOWNLOAD = true)
• Update related work notes in project memory
• If Obsidian is available, optionally create a literature review note in the vault

Key Rules

• Always include paper citations (authors, year, venue)
• Distinguish between peer-reviewed and preprints
• Be honest about limitations of each paper
• Note if a paper directly competes with or supports our approach
• Never fail because a MCP server is not configured — always fall back gracefully to the next data source
• Zotero/Obsidian tools may have different names depending on how the user configured the MCP server (e.g., mcp__zotero__search or mcp__zotero-mcp__search_items). Try the most common patterns and adapt.