feat: IMAP mail sync with thread-to-contact matching (PRD §6.1)
Real IMAP connection via imapflow library: - Connects to IMAP server (IMAPFlow, TLS on port 993) - Fetches recent messages (configurable limit, default 20) - Extracts: from, fromEmail, subject, date, snippet (200 chars) - Thread-to-contact matching (PRD §6.1): 1. Match sender email against persons table 2. If no person match, extract domain and match against company.website 3. Returns matchedCompany + matchedPerson for each email - Audit-logged (action: crm.mailsync:imap) - Zod validated (host, port, user, password, folder, limit) - 502 on IMAP connection failure with detail - 400 on validation failure Tested: invalid creds → 502 'IMAP connection failed', missing host → 400
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name: research-explorer
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description: Use when the user has a vague research direction and wants to explore feasible specific topics. Outputs a structured analysis with candidate topics, innovation/feasibility scoring, and a pre-survey of 20–30 representative works. Single-stage, no Python runtime.
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---
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# Research Explorer
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## Overview
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Research-topic exploration SKILL. Takes a broad direction, performs multi-dimensional web research with the agent's own WebSearch / WebFetch tools, and produces three structured Markdown deliverables. **Single stage, full quality from the start.** No Python runtime, no LLM SDK.
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## When to Use
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- User says "I want to research X" without a specific topic.
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- User wants to know "what are the hot topics in X".
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- User needs help narrowing a broad field into 5–10 candidate topics.
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- User asks for "research landscape overview".
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## When NOT to Use
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- User already has a specific research question → use `literature-survey` or `paper-writer`.
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- User wants a quick fact-check → use WebSearch directly.
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## Workflow
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### Step 1 — Understand the direction
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Confirm with the user:
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- **Direction** — the broad area of interest (e.g., "federated learning", "NLP for healthcare").
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- **Constraints** — theory vs. applied, specific methods, target venue, compute budget, time horizon.
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- **Language** — default English in conversation; reports in English unless the user requests otherwise.
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### Step 2 — Set up the run directory
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```bash
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DIRECTION="<direction>"
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SLUG=$(python3 -c "import re,hashlib,sys; t=sys.argv[1]; n=re.sub(r'[\\s_]+','-',re.sub(r'[^\\w\\s-]','',t.lower().strip())).strip('-')[:40].rstrip('-'); h=hashlib.sha1(t.encode()).hexdigest()[:8]; print(f'{n}-{h}')" "$DIRECTION")
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TS=$(date +%Y-%m-%d_%H%M%S)
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RUN=output/research-explorer/$SLUG/$TS
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mkdir -p "$RUN"
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ln -sfn "$TS" "output/research-explorer/$SLUG/latest"
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```
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In commands below `$RUN` = `output/research-explorer/<slug>/latest`.
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### Step 3 — Multi-dimensional exploration
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Run **AMiner academic search** and **web search** across the following
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dimensions (one query per dimension, more if returns are thin):
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1. **Hot topics** — "<direction> 2024 2025 hot topics" / "recent advances".
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2. **Open problems** — "<direction> open problems" / "challenges".
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3. **Surveys** — "<direction> survey 2024" / "<direction> review".
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4. **Benchmarks** — "<direction> benchmark" / "<direction> evaluation dataset".
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5. **Applications** — "<direction> applications" / "<direction> industry use cases".
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6. **Cross-field** — "<direction> + <adjacent field>" (pick 1–2 adjacent fields).
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7. **Recent breakthroughs** — papers from the last 6–12 months at top venues.
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**Search strategy (prefer structured academic search over generic web):**
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- **AMiner `search_papers`** — Use this FIRST for every dimension. It returns
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papers with structured metadata (citations, venue, DOI, authors) that web
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search cannot provide. Example: `search_papers(query="federated learning survey 2024", max_results=10, sort_by_citation=true)`.
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- **AMiner `search_authors`** — Use to find leading researchers in the
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direction. Example: `search_authors(query="federated learning", max_results=10)`.
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- **`web_search`** — Use as a supplement when AMiner returns insufficient
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results, or for non-academic sources (blogs, documentation, benchmarks).
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For each kept candidate, extract canonical title / authors / year / venue /
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citation count from the AMiner response. For web-search-only results,
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**WebFetch** the abstract URL to extract metadata. Persist intermediate notes
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to `$RUN/search_notes.md` after every dimension so the work resumes cleanly.
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### Step 4 — Produce the three deliverables
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Write these in `$RUN/`:
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#### 4.1 `research_exploration.md`
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Structured analysis containing:
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- **Direction recap & constraints**.
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- **Landscape map** — main subfields and the relationships between them.
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- **5–10 candidate topics**, each with:
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- Title (specific enough to be a paper title).
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- Motivation (why this matters now).
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- Innovation angle (what would be new).
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- Feasibility score (low / medium / high) with a brief justification (data availability, compute requirements, prior work density).
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- Risk / open question.
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- **Recommendation** — which 1–3 the user should pursue and why.
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#### 4.2 `topic_matrix.md`
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A hierarchical Markdown outline of the topic space:
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```
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# <Direction>
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## Subfield A
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### Topic A.1
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### Topic A.2
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## Subfield B
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### Topic B.1
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```
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This file is consumable by the `mindmap-render` skill to produce a visual mindmap.
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#### 4.3 `literature_pre_survey.md`
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A pre-survey table of **20–30 representative works** discovered above, with columns: title, authors, year, venue, URL, one-sentence relevance note. Every entry must have a URL the agent fetched in this session.
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### Step 5 — Optional handoff
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If the user picks a topic, suggest the next skill:
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- For a paper: the `paper-writer` skill (using the chosen topic).
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- For a survey: the `literature-survey` skill.
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- For an experiment package: the `experiment-suite` skill.
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- For a visual topic map: the `mindmap-render` skill consuming `topic_matrix.md`.
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## Cross-skill data flow (path convention)
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A downstream skill can locate this exploration via the slug:
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- `output/research-explorer/<slug>/latest/topic_matrix.md`
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- `output/research-explorer/<slug>/latest/literature_pre_survey.md`
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If the user picks one topic from the matrix, downstream skills compute their own slug from the **topic** (not the original direction), so the slug paths diverge from this skill onward — which is correct.
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## Important rules
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- **No LLM SDK in this skill.** Just a procedure + this `SKILL.md`.
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- **Candidates are suggestions, not guaranteed novel** — the user must verify originality before committing.
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- **Feasibility scores are heuristic** — flag uncertainty explicitly when relevant.
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- Every literature entry must have a URL fetched in this session; no memory-only entries.
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