cfd-literature

name	cfd-literature
description	Retrieve CFD literature for a topic via Semantic Scholar (with optional OpenAlex / arXiv / web supplements) and write a normalized lit.json. Use when the user wants a literature survey for a CFD study, or when cfd-pipeline / cfd-hypothesis / cfd-paper needs lit.json upstream. Self-contained — no Python helper required, but a one-shot script fast-path is documented at the bottom.

cfd-literature

Build the canonical lit.json artifact that every downstream skill (cfd-hypothesis, cfd-requirements, cfd-paper) consumes.

This skill is self-contained. The agent makes the HTTP calls itself, normalizes the response, and writes the artifact. The Python helper is only an optional one-shot.

Hard rules (slice 14)

1. Semantic Scholar is mandatory. lit.json must contain at least one record sourced from https://api.semanticscholar.org/graph/v1/paper/search (i.e. at least one record with "source": "semanticscholar"). The OpenAlex / arXiv / web supplements (Step 5) are additive — they do not substitute for the S2 call. Authoring lit.json without making the S2 HTTP call is forbidden.
2. S2_API_KEY MUST be used when set. Before making any S2 HTTP request, run echo "${S2_API_KEY:+set}" (or printenv S2_API_KEY | head -c 4). If the variable is non-empty, the request MUST include the x-api-key: $S2_API_KEY header. Falling back to the public (unauthenticated) endpoint while S2_API_KEY is set is a defect — it artificially trips the public rate limit even though the user provisioned a key. The only acceptable path to the unauthenticated endpoint is S2_API_KEY being genuinely unset/empty in the shell. If you see 429 from S2 and you have a key set, the request was misconfigured — fix the header, do not retry without it.
3. Provenance is recorded. Every record carries a "source" field ("semanticscholar", "openalex", "arxiv", or "web"). Records without a source field are treated as fabricated by downstream gates.
4. Empty-result handling is explicit, not silent. If S2 returns zero useful records after filtering, write lit.json as [] AND emit literature_empty_result to the timeline AND surface the empty result to the agent in the response so the user sees it. Do NOT silently advance to cfd-hypothesis with an empty lit set — the downstream H10 paper-stage citation gate would still demand citations the agent has no source for.
5. checkpoints/literature_done.json requires lit.json to exist (even if []) AND the timeline event to be present. The orchestrator-skill stage-gate --mode preflight --target-stage hypothesis checks checkpoints/literature_done.json directly; if the agent writes the checkpoint without S2 being called, the gate cannot detect it, so a downstream cfd-paper Stage 0.5 enrichment + H10 audit acts as the final defense.

Inputs

• topic (required): research topic string
• out-dir (required): run directory; created if missing
• max_papers (optional, default 20): respect the user's limit if they specified one
• s2_api_key (optional): read from S2_API_KEY env var if not passed

Output

<out-dir>/lit.json — JSON array of paper records:

[
  {
    "paperId": "string",
    "title": "string",
    "abstract": "string or null",
    "year": 2024,
    "citationCount": 42,
    "venue": "string or null",
    "externalIds": {"DOI": "10.xxx/...", "ArXiv": "..."},
    "url": "https://...",
    "authors": [{"authorId": "...", "name": "..."}],
    "source": "semanticscholar | openalex | arxiv | web"
  }
]

Recipe (primary, agent-driven)

Step 1 — Build the query

Read the user's topic verbatim. Do not rephrase or "improve" it — the user's wording is the ground truth. If the topic is multi-clause (e.g. "LES of backward-facing step Re=5100 with comparison to DNS"), keep it as-is for Semantic Scholar; the API does fuzzy keyword matching.

If the user provided a specific maximum number of papers, use that exactly. Otherwise default to 20.

Step 2 — Call Semantic Scholar Graph API

Endpoint: GET https://api.semanticscholar.org/graph/v1/paper/search

Query parameters:

• query = the topic string (URL-encoded)
• limit = min(max_papers, 100) (S2 hard caps at 100 per request; paginate with offset if you need more)
• fields = paperId,title,abstract,year,citationCount,venue,externalIds,url,authors

Authentication — read this first, do NOT skip:

Before issuing the request, check whether S2_API_KEY is set:

if [ -n "${S2_API_KEY:-}" ]; then
  echo "S2_API_KEY is set; authenticated endpoint MUST be used"
else
  echo "S2_API_KEY is not set; public endpoint is the only option"
fi

• If S2_API_KEY is non-empty: include x-api-key: $S2_API_KEY on every request to api.semanticscholar.org. This is mandatory (Hard rule #2). The authenticated endpoint has dramatically higher rate limits and is the path the user provisioned. Example with curl:

  curl -sS -H "x-api-key: $S2_API_KEY" \
    "https://api.semanticscholar.org/graph/v1/paper/search?query=<urlencoded>&limit=<n>&fields=paperId,title,abstract,year,citationCount,venue,externalIds,url,authors"
  

  Example with Python requests:

  import os, requests
  headers = {"x-api-key": os.environ["S2_API_KEY"]}
  r = requests.get(
      "https://api.semanticscholar.org/graph/v1/paper/search",
      params={"query": topic, "limit": limit, "fields": fields},
      headers=headers,
      timeout=30,
  )
  

  If you call S2 without the header while the key is set, that is the defect this rule exists to prevent. Re-issue the request with the header.

• Only if S2_API_KEY is unset/empty: use the public endpoint (no x-api-key header). It is rate-limited to ≈100 requests / 5 min — back off and retry with exponential delay on 429.

Never use the public endpoint when S2_API_KEY is set. There is no scenario where stripping the header is the correct choice while the user has a key provisioned.

Step 3 — Filter for CFD relevance

S2 search is loose; many results will be off-topic. Drop a paper when:

• abstract is null AND title doesn't contain any CFD/fluid keyword (flow, turbulence, Reynolds, LES, RANS, DNS, OpenFOAM, mesh, boundary layer, viscosity, …) — only filter on title when the abstract is unavailable.
• year < 1980 (very old; usually not what the user wants unless the topic explicitly mentions a classical reference).
• The paper is a duplicate of one already kept (match on paperId, then on lowercased title).

Don't be aggressive. When in doubt, keep the paper.

Step 4 — Sort

Primary: citationCount descending. Secondary: year descending. This biases toward foundational + recent.

Step 5 — Optional supplements

If after filtering you have fewer than max_papers / 2 results, supplement from:

• OpenAlex — GET https://api.openalex.org/works?search=<topic>&per-page=25 — same field-mapping (title, abstract, publication_year, cited_by_count, doi, authors).
• arXiv — GET http://export.arxiv.org/api/query?search_query=all:<topic>&max_results=25 (Atom XML; parse entry/title, entry/summary, entry/published, entry/id).

Tag supplemented entries with "source": "openalex" or "source": "arxiv". Do not invent records.

Step 6 — Write lit.json

• Truncate to max_papers.
• Sort once more (citation desc).
• Write JSON, indent 2, UTF-8.

Step 7 — Append to timeline

Open <out-dir>/timeline.json (create as [] if missing) and append:

{"stage": "literature", "event": "complete", "ts": "<iso8601>", "paper_count": <n>, "topic": "<topic>", "with_api_key": true|false}

Skip if already done

If <out-dir>/lit.json exists and is a non-empty array, skip the search and append a literature_skipped_existing event. The user can force regeneration by deleting lit.json first.

Anti-hallucination rules

• Never invent a paper. If the API returns nothing useful, write an empty [] and emit a timeline event literature_empty_result so downstream skills can react. Do not fabricate titles, DOIs, or abstracts to "fill in" the array.
• Never invent citation counts. If the field is missing in the response, leave it null (not 0).
• Never invent authors. Trust the API response shape exactly.

Optional script fast-path

If scripts/lit.py is present (it is, in this repo's LangGraph pipeline) and you would prefer to delegate the HTTP/parse work, you may run:

python scripts/lit.py \
  --topic "<topic>" \
  --limit <max_papers> \
  --output <out-dir>/lit.json \
  --timeline <out-dir>/timeline.json

Same artifact contract. The Python script reads S2_API_KEY from the environment and attaches the x-api-key header automatically (scripts/lit.py:148), so the auth contract from Hard rule #2 is preserved. Make sure the variable is exported in the shell before invoking:

export S2_API_KEY=<your_key>
python scripts/lit.py --topic "..." --output <out-dir>/lit.json --timeline <out-dir>/timeline.json

Use this script when running inside the conda env and the script is reachable; use the agent recipe otherwise (e.g. when running from a different agent framework or when cfd-skills/ is symlinked into a non-Python project).

Notes

• Citation enrichment for the bibliography (cfd-paper) happens later via DBLP/CrossRef in cfd-paper's citation step. This skill produces only the discovery dataset.
• For multi-flow OED (--oed-multi-flow) you may want to run cfd-literature once per flow with the flow-specific topic — that produces multiple lit.json files; downstream skills handle either case.

Next

After lit.json and checkpoints/literature_done.json exist on disk, your next action is to invoke the next skill in the chain. Do not stop, summarize, or wait — this is a literal handoff.

Read <out-dir>/state.json#mode, then invoke from this table:

mode	Next skill
research, code_mod, open_discovery	Skill cfd-skills/cfd-pipeline — run only Steps 5 (baseline_setup) and 6 (metric_setup); write baseline_metrics.json, metric_definitions.json, and the matching checkpoints/*_done.json. Its own ## Next then chains onward.

mode == analysis_only / paper_only / mesh_gate does not pass through this skill, so no branch is needed here.