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freesearch-search

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更新时间2026年7月22日 09:34

Run the lit-review orchestrator free index search channel against keyless OpenAlex, Crossref, and Semantic Scholar keyword-search endpoints. Use when the user invokes the free search channel, asks for keyless scholarly index search, or needs Stage 4e fallback discovery without SearchAPI, Gemini, or Undermind credentials.

安装

用 Codex 或 Claude 帮你安装复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。

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来源

kennethkhoocy

kennethkhoocy/applied-micro-skills

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在 Manus 中运行

Free Index Search channel (Stage 4e) — keyless OpenAlex / Crossref / Semantic Scholar

A zero-key discovery channel: it runs the Stage-0 query list against the free, keyless keyword-search endpoints of OpenAlex, Crossref, and Semantic Scholar and normalizes the real records into the pipeline schema. Together with the agent web-search channel (Stage 4d) it is the "only Claude Code" search fallback — no search account or API key required.

Unlike web search, this is a plain subprocess (it calls keyless HTTP APIs), so it runs the same way in any context.

When to use

The user has no SEARCHAPI_API_KEY / GEMINI_API_KEY / Undermind login — this is the keyless search backbone, OR
You want extra grounded, clean-metadata coverage from the scholarly indexes alongside the other channels.

Run it in parallel with whatever else is available; its output merges at dedup.

Recipe

Use the same Stage-0 query list Google Scholar uses:

python freesearch-search/scripts/freesearch_search.py \
    --queries-file OUT/scholar_queries.json -o OUT/stage4e_freesearch.json

Or a single query with --query "...". Options: --per-query N (default 20) and --sources openalex,crossref,semanticscholar (any subset). It writes stage4e_freesearch.json (+ .ris) with source set per index, deduped across indexes by DOI then title. The stage[0-9]*.json dedup glob picks it up automatically.

Keys

None required. OpenAlex and Crossref use the polite pool via a mailto (set LITREVIEW_CONTACT_EMAIL to use your own contact). If present, OPENALEX_API_KEY and SEMANTIC_SCHOLAR_API_KEY are used only to raise rate limits. It makes no LLM API calls.

Anti-hallucination

Every hit is a real index record, so there is nothing to fabricate — relevance, not existence, is the only variable, and screening handles relevance. Stage 5b verification still confirms each paper downstream. The keyless Semantic Scholar tier is rate-limited, so it is queried with a short delay and skipped gracefully on a 429.

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kennethkhoocy/applied-micro-skills

Build human adjudication / hand-labeling sheets from LLM-pipeline data without evidence truncation. Use when: (1) preparing a CSV/Excel sheet for a human to rule on cases an LLM classifier or rater panel judged, (2) a labeler reports "there is no information to label from" or cells look empty in Excel, (3) excerpt columns cluster at one exact length (e.g. all 1,500 chars — a hard truncation cap). Covers: full rating-basis recovery, Excel 32,767-char cell cap, multi-line CSV mangling, ruling dropdowns, companion text files.

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N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.

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kennethkhoocy/applied-micro-skills

Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.

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Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.

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cite-placement

kennethkhoocy/applied-micro-skills

Place pre-screened literature citations into a LaTeX or Word manuscript, or restyle the citations already in one. Three modes: (1) inline placement — inline \cite{}/\citet{}/\citep{} with a compiled references.bib, for author-date journals (APA, MLA, Harvard, Chicago author-date, IEEE, Vancouver); (2) footnote placement — full formatted \footnote{} or OOXML footnotes for legal and notes styles (Bluebook, OSCOLA, Chicago, APA, McGill) with Id./supra short forms; (3) restyle — convert existing footnote citations from one style to another. This skill is manual-invoke ONLY — trigger ONLY when the user explicitly runs /cite-placement or explicitly names the "cite-placement" skill. Do NOT auto-trigger on general citation, footnote, or reference requests.

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kennethkhoocy/applied-micro-skills

Download the actual PDF binary from bot-gated sites (taxpolicycenter.org, urban.org, SSRN-hosted mirrors, think-tank/publisher sites) via the Wayback Machine id_ URL form. Use when: (1) curl/WebFetch of a .pdf URL returns HTML instead of a PDF even with a browser User-Agent, (2) pypdf fails with "invalid pdf header: b'<!DOC'" or "EOF marker not found" on a freshly downloaded file, (3) Firecrawl can parse the PDF to markdown but you need the original file on disk (e.g., filing a reference copy).

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name	freesearch-search
description	Run the lit-review orchestrator free index search channel against keyless OpenAlex, Crossref, and Semantic Scholar keyword-search endpoints. Use when the user invokes the free search channel, asks for keyless scholarly index search, or needs Stage 4e fallback discovery without SearchAPI, Gemini, or Undermind credentials.

Free Index Search channel (Stage 4e) — keyless OpenAlex / Crossref / Semantic Scholar

Unlike web search, this is a plain subprocess (it calls keyless HTTP APIs), so it runs the same way in any context.

When to use

The user has no SEARCHAPI_API_KEY / GEMINI_API_KEY / Undermind login — this is the keyless search backbone, OR
You want extra grounded, clean-metadata coverage from the scholarly indexes alongside the other channels.

Run it in parallel with whatever else is available; its output merges at dedup.

Recipe

Use the same Stage-0 query list Google Scholar uses:

python freesearch-search/scripts/freesearch_search.py \
    --queries-file OUT/scholar_queries.json -o OUT/stage4e_freesearch.json