paper-fetch
Skill1 starsChanged 3 months agoApache-2.0View on GitHub
Retrieve one specified academic paper (by title, arXiv ID, DOI, URL, or a local .md/.txt/.pdf path the caller already has) and land it on disk as source.md plus a source.meta.json carrying a line-number section index. Checks context/papers/ for an existing copy first; local files and direct PDF URLs are read directly with no search at all, while other references use alphaxiv, Semantic Scholar routing, then bioRxiv/medRxiv. Use this as the mandatory first step whenever any other paper-reading SOP in this package needs the actual text of a paper — it is the sole entry point of the pipeline and every downstream SOP reads the files it lands, so do not bypass it even when the caller already has the file. If it returns not_found, halt immediately; do not fabricate content or guess at the paper's likely contents.
4 other projects use a version of this.
Adopted by
Most recently taken up by
- drpwchen/paper-fetchtheir own version, first seen Nov 1, 2026
- Kara-lynnmacroeconomic2412/paper-fetchtheir own version, first seen May 4, 2026
- exomind-team/paper-fetchtheir own version, first seen Apr 11, 2026
- HK-hub/AgentSkillstheir own version, first seen Mar 2, 2026
Other versions4 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 4
- drpwchen/paper-fetch★ 66same name
- HK-hub/AgentSkills★ 6same name
- Kara-lynnmacroeconomic2412/paper-fetch★ 4same name
- exomind-team/paper-fetch★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository34 other files it gives its agents.
- acu-nugget-recallSkill
- argumentative-zoningSkill
- atomic-unit-matchingSkill
- atomic-unit-recall-aggregateSkill
- atomic-unit-writingSkill
- claim-label-predictionSkill
- claim-writingSkill
- domain-level-judgmentSkill
- dual-column-self-checkSkill
- engineering-config-gradingSkill
- first-pass-skimSkill
- keshav-three-passSkill
- multi-stage-cascade-extractionSkill
- qalmriSkill
- qalmri-worksheetSkill
- qasper-evidence-qaSkill
- quality-appraisal-checklistSkill
- question-framingSkill
- rationale-selectionSkill
- reforms-gradingSkill
- reporting-standard-checklistSkill
- reproducibility-third-party-verificationSkill
- research-question-appraisalSkill
- rhetorical-structure-qualitySkill
- second-pass-graspSkill
- signalling-question-answeringSkill
- star-awardingSkill
- study-design-tool-gateSkill
- sum-threshold-scoringSkill
- template-slot-fillingSkill
- third-pass-deep-readSkill
- unit-classificationSkill
- unit-segmentationSkill
- worst-case-lookupSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 0 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 4 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 18
If the caller already has the paper — a local .md/.txt/.pdf path, or a direct HTTP(S) PDF URL (path ends in .pdf, ignoring query and …
- Very specific: line 52
All landed filenames are lowercase.
“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Paper Fetch
If the caller already has the paper — a local .md/.txt/.pdf path, or a direct
HTTP(S) PDF URL (path ends in .pdf, ignoring query and fragment) — read it
directly before considering any search route. Record source_channel as
local_file, local_pdf, or direct_pdf. If the read fails, return not_found
and do not fall back to alphaxiv, Semantic Scholar, bioRxiv, or medRxiv.
The pipeline's sole entry point: retrieves a paper and lands it on disk. It checks
the cache first, reads already-identified sources (local files, direct PDF URLs)
with no search at all, and otherwise uses the fixed fallback (alphaxiv → Semantic
Scholar routing → bioRxiv/medRxiv → not_found).
Decoupled from literature-engine's literature-research/literature-search/
literature-overview — this SOP holds its own retrieval calls rather than delegating.
Why an already-supplied file still enters through this SOP
Skipping straight to a reading SOP with the caller's own path looks like it saves
a step, but 13 downstream SOPs take meta_path and read only section line ranges
(star-awarding reads method + results; first-pass-skim's "headings only, never
bodies" constraint holds because it is handed shallow ranges). A bare path
carries no index, and a bare .pdf carries no extracted text at all.
So the thing to skip is the four-channel search, not the landing and indexing.
That is what Step 1 does: no network lookup, same landing step, same output
contract. Tactics keep passing paper_ref through unchanged and never learn there
was a new input form.
Landed layout
context/papers/<timestamp>-<title-slug>/
source.md the paper, as fetched
source.meta.json metadata + line-number section indexAll landed filenames are lowercase. <title-slug> is lowercased, non-alphanumerics collapsed to hyphens, Windows-illegal characters (: * ? " < > |) stripped, truncated to 60 chars against path-length limits.
Execution
Subagent — spawned via spawn-agent skill.
Why Subagent
Multi-step retrieval with direct-PDF handling and domain-inference judgment calls (is a Semantic-Scholar miss a bio signal or a "just not indexed anywhere" signal?) benefits from a dedicated context that can hold the whole decision tree without the noise of whatever task will consume its output next.
Why it lands files instead of returning text
A paper runs 60-80k tokens. Returning it as full_text means every downstream SOP pays that cost again, and the orchestrating tactic carries it in its own window on top. Landing it once and returning a path means: re-reading the same paper across tactics costs one cache check rather than one fetch; SOPs that need only part of the paper read only that part; the orchestrator holds paths, not text.
Why a section index, not pre-cut slices
source.meta.json records where each section is (line ranges) rather than shipping pre-cut slices. Slicing is a per-consumer concern — unit-segmentation already declares scope: full_text | abstract | intro_only, research-question-appraisal wants intro + abstract, engineering-config-grading wants method + experiments + appendix. Pre-cutting would mean this SOP has to know every downstream SOP's definition of "the part I need", and would need editing every time one is added. An index is neutral: consumers do their own offset reads against source.md.
This also makes first-pass-skim's defining constraint (skim headings and captions, never section bodies) hold by construction rather than by self-restraint — it is handed line ranges for the shallow parts only.
Cache lookup is by identifier, not directory name
Directories are named <timestamp>-<title-slug>, but paper_ref may arrive as an arXiv ID, a DOI, a URL, a local file path, or a title, and matching an arXiv ID against a title-derived directory name fails. So the lookup scans context/papers/*/source.meta.json and matches on identifier or title_slug — both are recorded precisely so any form of paper_ref resolves. For a local file, identifier is its absolute path, so re-running a tactic on the same file is a cache hit rather than a second copy.
Matching tolerates case and punctuation differences in titles but is deliberately not fuzzy: a missed cache hit costs one redundant fetch, while a false hit silently reads the wrong paper for the rest of the pipeline.
Why Not Built on literature-engine
literature-overview/literature-search/literature-research already have an alphaxiv-primary/SS-supplementary pattern, but none has a bioRxiv/medRxiv branch or an explicit "can't retrieve → halt" contract, and this package is deliberately decoupled from that pipeline's scope (see context/2026-08-07-15-15-paper-fetch-sop-design.md and spec §9). Do not refactor this SOP to import those skills later without revisiting that decision explicitly.
Full design reference
context/2026-08-07-15-15-paper-fetch-sop-design.md — the channel decision-flow rationale (why alphaxiv's coverage list is the domain signal, why an SS miss still routes to bio rather than dead-ending). The landing / index / cache design is in context/2026-08-07-23-01-sop-io-contract-simulation.md §4, option C.
<!-- BEGIN available-tables (generated) -->Available SOPs
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| SOP | When to use |
| spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
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Your trail
Files you open will collect here, so you can find them again.
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