search-conference

Use when tasks involve semantically matching a user's idea (query or example papers) to papers in a specific OpenReview venue. Uses `embed-papers` to crawl metadata, build/use embedding caches, run cosine-similarity search, then produces a short, grouped Markdown reading list with brief rationales.

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Install skill "search-conference" with this command: npx skills add codeboyphilo/phi-skills/codeboyphilo-phi-skills-search-conference

When to use

  • Need to find papers in an OpenReview venue that match one or more ideas/topics.

Dependencies

  • embed-papers must be installed and available in PATH.
    • Check with: command -v embed-papers
    • Install with: pip install embed-papers
  • OpenReview must be reachable.
  • OPENAI_API_KEY is required to compute embeddings of the papers (cached) or the search intent (not cached).
    • Check with: printenv OPENAI_API_KEY
    • If missing and embeddings are required: stop and tell the user how to set it (immediate stop).

Inputs from user

  • Venue (one of):
    • venue_id (preferred), e.g. ICLR.cc/2024/Conference
    • OR {conference, year} to derive venue_id as {CONF}.cc/{YEAR}/Conference, e.g. NeurIPS, 2025
  • Search intent (one of):
    • query (string of ideas)
    • OR examples (list of {title, abstract} objects)
  • Optional:
    • top_k (default: 100) for retrieval breadth

CLI contract (how to interpret tool output)

  • Success envelope: { ok: true, schema_version: "1", command, data }
  • Error envelope: { ok: false, schema_version: "1", command, error: { type, message } }
  • Always parse stdout as JSON.
  • Treat any ok=false as a terminal error unless the error section below says otherwise.

HARD CONSTRAINT (TOOLS):

  • Do NOT call Read/Glob/Grep on any cache/embedding files or directories (e.g. anything under .cache/ or any path containing "cache", "embedding", "paper", "atlas").
  • Treat caches as opaque implementation details. Never inspect them “just to check”.
  • If you need cache status, ONLY use embed-papers warm-cache and rely on its JSON stdout.
  • If a command outputs a cache path, DO NOT open it; proceed using the CLI utilities.

Pipeline

  1. Resolve venue_id

    • If the user gave {conference, year}, build: {CONF}.cc/{YEAR}/Conference
    • If ambiguous, ask a single clarifying question (conference acronym + year).
  2. Crawl venue metadata (idempotent)

    • Run:
      • embed-papers crawl --venue-id "<venue_id>" --skip-if-exists
    • Record:
      • data.output_file
      • data.total
  3. Ensure embeddings are available (cache)

    • Run:
      • embed-papers warm-cache --venue-id "<venue_id>"
    • If this fails due to missing API key, stop and instruct the user to set OPENAI_API_KEY.
    • This command also computes the embedding if no cache is found.
    • You MUST NOT access the cache.
    • You MUST use the package's provided utility.
  4. Search (choose based on user input)

    • Query mode:
      • embed-papers search --venue-id "<venue_id>" --query "<query>" --top-k <top_k>
    • Examples mode:
      • If needed, write a temporary JSON file containing:
        • [{"title":"...","abstract":"..."}, ...]
      • Then run:
        • embed-papers search --venue-id "<venue_id>" --examples-file "<tmp.json>" --top-k <top_k>
  5. Organize results (post-processing)

    • Group primarily by primary_area (if present).
    • Within groups, prefer papers with clear overlap to the query/examples.
    • For each recommended paper, add agent judgment notes:
      • why it matches
      • what seems novel/different
      • caveats (weak match, missing abstract, unclear claims, etc.)

Report requirements (Markdown only)

  • Output is a Markdown report only (no raw JSON).
  • Keep the final recommendation list short: 5-10 papers max.
  • Do not output a full ranked list or appendix by default (only if the user asks).

What I'd start with

  • Begin this section with a short, casual sentence (lowercase is fine).
    • Example: "here's what i recommend you to read as a beginning."
  • Then list 5-10 papers.
  • Each item must include:
    • Title (bold)
    • OpenReview link
    • 1-2 sentence rationale (fit + why it matters, use italic to emphasis)

How I organized it

  • Briefly explain grouping logic and where judgment calls were applied.
  • Note missing metadata (e.g., missing abstracts) when relevant.

Why these stand out

  • Use informal labels in the narrative (no formal rubric), e.g.:
    • "the obvious hits"
    • "the surprisingly relevant ones"
    • "the quirky but promising picks"

Error handling

  • NoPapersFoundError
    • Likely invalid venue_id; suggest the pattern {CONF}.cc/{YEAR}/Conference and ask for the correct venue.
  • CacheMissRequiresApiKeyError
    • Instruct the user to set OPENAI_API_KEY and retry.
  • OpenReviewRequestError / EmbeddingRequestError
    • Suggest retrying, reducing load (smaller top_k), or trying later (rate limits / transient failures).

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