# Paper Three Pass Extraction > Domain-neutral methodology for autonomously extracting structured notes from a single academic paper via three escalating passes. Pass 1 (inspectional, ~10-15 min) reads title, abstract, intro, section headings, conclusion, references at a glance, then applies the Five Cs framework (Category, Context, Correctness, Contributions, Clarity). Pass 2 (content grasp, ~30-60 min) reads the full paper skipping proofs, answers main-argument / Big-Question / hypotheses / figure-by-figure / references / confusions. Pass 3 (deep understanding, ~1-4 hours, reserved for important papers) virtually re-implements, challenges every assumption, identifies what is NOT said, asks the falsifiability question. The methodology is internal to the agent applying it - questions are answered against the paper's content, never asked of the operator. Use when an extraction agent needs to convert dense academic prose into structured machine-and-human-readable notes - bio papers, CS papers, ML papers, statistics, math, any field. - Skill: `lyndonkl/paper-three-pass-extraction` (Agent Skill) - Install (CLI): `npx skillmds add lyndonkl/paper-three-pass-extraction` - Raw SKILL.md: https://api.skillmd.com/api/skills/lyndonkl/paper-three-pass-extraction/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: lyndonkl (https://skillmd.com/u/lyndonkl) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/lyndonkl/paper-three-pass-extraction --- # paper-three-pass-extraction Three-pass methodology for extracting structured notes from a single academic paper. Each pass escalates from cheap-and-shallow to expensive-and-deep; downstream agents trigger Pass 2 only for relevant papers and Pass 3 only on explicit operator request. The **central principle**: every question in this methodology is answered *by the agent reading the paper*, against the paper's content. Never ask the operator the questions. Never have a Socratic dialogue. The questions are an internal extraction checklist whose purpose is to produce structured output — not user-facing prompts. ## Workflow ``` - [ ] Pass 1: Inspectional reading (always run) - Read title, abstract, intro paragraphs, section headings, conclusion, references at a glance - Apply the Five Cs framework - Produce Pass 1 extraction output - [ ] Pass 2: Content grasp (run when escalated, e.g., paper passed relevance filter) - Acquire full text (PDF or HTML); flag if unavailable - Read linearly, skipping proofs and heavy derivations - Answer the Pass 2 question set - Append Pass 2 extraction output - [ ] Pass 3: Deep understanding (run only on operator request) - Re-read methods + proofs that Pass 2 skipped - Virtually re-implement the core idea - Answer the Pass 3 question set - Append Pass 3 extraction output ``` ## Pass 1 — Inspectional Reading Operates on title, abstract, intro paragraphs, section headings, conclusion, and references at a glance. Never blocks on a missing PDF — abstract-only Pass 1 is acceptable. ### The Five Cs (the agent answers each — not the operator) 1. **Category** — What type of paper is this? Pick the most specific applicable label: empirical study, theoretical analysis, system / architecture description, benchmark, survey / review, meta-analysis, position / perspective, replication, ablation study, dataset release, methods paper, case report. If the paper fits two, pick the dominant frame from the abstract; note the secondary in parentheses. 2. **Context** — What prior work does it build on? What field or debate does it sit within? Look for: explicit citations in the abstract, "extends [X]" / "improves on [X]" phrasing, the institutional / lab pattern in the author list, key references repeated. Output: 1-2 sentences naming the closest prior work and the active debate. 3. **Correctness (first-glance)** — Do the assumptions seem reasonable at first glance? This is *not* a deep critique — Pass 1 hasn't read the methods. Flag obvious issues: claims that contradict well-known results, conclusions that overreach the evidence cited in the abstract, missing comparisons. If nothing obvious, write "no first-glance concerns." 4. **Contributions** — What does the paper claim to add? Quote or paraphrase the abstract's contribution claim. Distinguish: new method / new result / new dataset / new framing / negative result / replication. **Preserve hedging**: if the abstract says "suggests" or "is consistent with," the extraction says the same. Never promote a "suggests" to a "shows." 5. **Clarity** — Is the abstract / structure well-written? Does the title match the abstract? Are claims specific or vague? Output: short signal — `clear`, `dense-but-clear`, `vague`, `mismatched-title`, `acronym-soup`. This guides whether Pass 2 will be easy or painful. ### Pass 1 also produces a `one_line` summary A single-sentence compression of "what the paper does." Used by upstream callers for quick reporting. Shouldn't exceed ~30 words. ## Pass 2 — Content Grasp Runs when the caller escalates (typical trigger: paper passed a relevance filter as KEEP). Reads the full paper, skipping proofs and heavy derivations. Needs the full-text source. ### Pass 2 question set (the agent answers — not the operator) 1. **Main argument and supporting evidence — 3 to 5 sentences.** What does the paper argue, and what does it adduce as support? Resist re-stating the abstract; this should reflect the actual structure of the paper as you read it. 2. **The Big Question.** Not what the paper is about — what *problem* the field is trying to solve, of which this paper is one move. Often inferable from the introduction's framing of related work. 3. **Specific hypotheses tested.** List them. If the paper is a systems / engineering paper without explicit hypotheses, list the design claims being defended instead. 4. **Unfamiliar terms / concepts requiring gloss for a non-specialist reader.** These will become candidates for inline glossing in any downstream summary. 5. **Figure-by-figure analysis.** For each figure or table that carries the argument: what is being shown, what's the takeaway, are axes / error bars / sample sizes clean. Skip ornamental figures. 6. **References worth follow-up.** Distinguish *load-bearing* references (the result depends on them) from background citations. 7. **Confusions or unresolved points.** What did not land for you on this read? Mark them — Pass 3 (if escalated) will return to them. ### Pass 2 acquisition recipe The agent has to actually fetch the full text — not just say it tried. WebFetch is HTML→markdown only; it does not handle PDFs. The reliable recipe uses the calling agent's Bash and Read tools together: `curl` the PDF to a local cache path, then read it with the Read tool's `pages` parameter (max 20 pages per call). ``` 1. Construct a local cache path: pdf_cache = {output_root}/.cache/pdfs/{paper_slug}.pdf (or whatever cache convention the calling workflow uses; the cache should be gitignored so PDFs don't get committed) 2. Ensure the cache directory exists: Bash: `mkdir -p {output_root}/.cache/pdfs` 3. Try sources in order. STOP at the first one that succeeds. Source 1 (preferred) — direct PDF download via Bash + curl: `curl -L --max-time 60 --fail -sS -o {pdf_cache} "{pdf_url}"` Non-zero exit code → curl failed (404, network, redirect loop). Move on. Zero exit → continue to step 4. Source 2 (HTML fallback) — WebFetch on the abstract page URL: WebFetch returns markdown. Accept only if length > ~5K chars and the result clearly contains methods + results sections (not just the abstract). Otherwise treat as "abstract page only" and move on. Source 3 (PubMed PMC OA fallback): Only attempts when source == "pubmed" and a PMC id is present in the paper_record. Apply Source 1's curl recipe to the PMC PDF URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC{pmc_id}/pdf/ 4. Read the full text: For PDF cache files, use Read({file_path: pdf_cache, pages: "1-20"}). Continue with pages "21-40", "41-60" etc. until you reach the references or the file ends. Most papers fit in one call. For Source 2 HTML, use the WebFetch result directly. 5. Failure mode — if all three sources fail: Mark full_text_available=false and proceed in reduced-confidence mode. Pass 2 still runs on abstract + (when accessible) intro paragraphs, but each affected answer is tagged "(abstract-only; full text unavailable)" and the figure-by-figure question is skipped entirely. Degraded Pass 2 still beats no Pass 2 for downstream synthesis. ``` The calling agent's required tools for this recipe: `Bash` (for `mkdir -p` and `curl`), `Read` (for PDF ingestion with the `pages` parameter), and `WebFetch` (for the HTML fallback). Skill callers without one of these cannot execute the recipe and should fall back to abstract-only mode explicitly. ### When `full_text_available=false` is the honest answer Most PubMed records are paywalled outside PMC OA. arXiv, bioRxiv, and medRxiv all serve open PDFs. If a calling workflow is reporting `full_text_available=false` for arXiv or preprint records, something is wrong — verify the recipe was actually run (curl exit code observed; Read attempted on the cached file) before accepting the false flag. ## Pass 3 — Deep Understanding Runs only on explicit operator request. Time investment: 1-4 hours of agent compute equivalent. Reserved for high-priority papers. **Never auto-trigger.** ### Pass 3 question set 1. **Reconstruct the structure and argument from memory.** Force a from-memory write: the structure of the paper, the argument's flow, the key results, in your own organization. If you cannot reconstruct, you don't yet understand it — return to Pass 2. 2. **Strongest points; weakest points.** Be specific. "The benchmark is standard" is weak praise; "the multi-seed protocol with seeds disclosed in the appendix is unusually rigorous" is real. Same for weaknesses. 3. **Falsifiability — what would have to be true for the conclusions to be wrong?** This is the highest-value question in deep reading. Surface the load-bearing assumptions whose failure would invalidate the result. 4. **What is *not* said?** Hidden assumptions, missing comparisons, citations that should appear but don't, methodological choices that aren't justified. 5. **Future work this opens up.** Concrete next experiments or follow-up questions, not vague "more research needed." ## Common patterns ### Pattern A — Weekly batch literature scan Pass 1 on every fetched paper (cheap; runs in parallel as subagent batch). Apply the relevance filter on the Pass 1 outputs (richer signal than abstracts alone — the Five Cs answers improve criteria-fit scoring). Pass 2 on the KEEPs. Pass 3 only when the operator points at a specific paper from the digest. ### Pattern B — Single-paper deep read Operator drops a paper into a focused-review workflow. Run all three passes sequentially, returning each pass's output. Pass 3 is on the table because there's only one paper to invest in. ### Pattern C — Reading-list triage Operator has 50 papers in a backlog. Pass 1 on all 50 (fast); rank by Pass 1 signals (Category fit, hedging strength, Clarity); recommend top-N for Pass 2. ### Pattern D — Replication / disagreement check Two papers claim contradictory results. Pass 2 on both with explicit attention to: methodology differences in question 1, hypothesis-testing protocol in question 3, figure protocol differences in question 5. Pass 3 on whichever is the higher-priority claim. ## Guardrails 1. **Never ask the operator any of the methodology questions.** Five Cs, Pass 2 questions, Pass 3 questions — all are answered by the agent against the paper. The questions are an internal extraction checklist, not a dialogue. 2. **Never skip Pass 1 for an unfamiliar paper, even when the caller asked for Pass 2 or Pass 3 directly.** Always run prior passes first; Pass 1's structured frame anchors what follows. 3. **Always preserve hedging.** If a paper says "suggests" or "is consistent with," the extraction says the same. Never promote. 4. **Never invent results not in the paper.** If a number, citation, or figure isn't in the source, don't include it. Write `(not stated)` when uncertain — that's high-information. 5. **Never auto-trigger Pass 3.** Pass 3 is operator-driven. Pass 2 may auto-escalate based on filter results; Pass 3 must not. 6. **Pass 2 without full text is degraded, not skipped.** Mark the file as such and reduce downstream confidence; don't refuse the pass. 7. **Pass 1 stays cheap.** If you're reading the methods section in Pass 1, you've drifted into Pass 2. Stop. ## Quick reference | Pass | Time | Trigger | Input | Output | | ---- | --------- | -------------------------------------- | ---------------------------------------- | ----------------------------------------------- | | 1 | ~10-15min | Always (every paper) | Title, abstract, intro, headings, conclusion | Five Cs answers + one_line summary | | 2 | ~30-60min | Caller escalates (filter KEEP) | Full paper text (PDF or HTML), skip proofs | Pass 2 question-set answers, figure analysis | | 3 | ~1-4h | Operator request only — never auto | Full paper including methods + proofs | Reconstruction, strongest/weakest, falsifiability, what-is-not-said, future work | ## Related skills - [`layered-reasoning`](../layered-reasoning/SKILL.md) — the three passes are themselves a layered structure (inspectional / content / deep). Apply layered-reasoning's consistency checks across passes. - [`scientific-clarity-checker`](../scientific-clarity-checker/SKILL.md) — directly applicable to Pass 1's Clarity assessment and Pass 2's main-argument-vs-evidence check. - [`research-claim-map`](../research-claim-map/SKILL.md) — applicable to Pass 2's reference triage (load-bearing vs background) and Pass 3's source grading. - [`hypotheticals-counterfactuals`](../hypotheticals-counterfactuals/SKILL.md) — drives Pass 3's falsifiability question. - [`negative-contrastive-framing`](../negative-contrastive-framing/SKILL.md) — drives Pass 3's "what is *not* said." - [`skill-creator/resources/inspectional-reading.md`](../skill-creator/resources/inspectional-reading.md) — the kindred-spirit predecessor scoped to skill creation; same Adler-style reading discipline applied to a different artifact (source documents for skill extraction rather than research papers). - [`skill-creator/resources/synthesis-application.md`](../skill-creator/resources/synthesis-application.md) — same lineage; provides the completeness-and-logic check pattern that Pass 3's "what is not said" extends. ## Inputs required - A paper record (id, title, authors, abstract, date, source URL, optional DOI, optional PDF URL) - The pass to run (1, 2, or 3) — caller's escalation choice - An output location for the extraction file (path-agnostic; caller decides) ## Outputs produced A structured markdown file accumulating each pass's output. Multiple passes append; never overwrite a prior pass section. The file is the artifact downstream agents (synthesizer, archiver, search) consume.