# Paper Companion

> Structured academic paper reading guide using a four-stage protocol: Structural Scan → Core Findings → Critical Evaluation → Reading Note. Use this skill whenever a user uploads a PDF of an academic paper, or says things like "let's read this paper", "help me with this article", "walk me through this study", "Paper Companion", or "stage 1/2/3/4". Also trigger when the user asks to analyse, summarise, or critically evaluate any research paper — even without a direct upload. Do NOT use the academic-writing skill in place of this one for paper-reading tasks.

- Skill: `david-saeteros/paper-companion` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add david-saeteros/paper-companion`
- Raw SKILL.md: https://api.skillmd.com/api/skills/david-saeteros/paper-companion/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: David-Saeteros (https://skillmd.com/u/david-saeteros)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/david-saeteros/paper-companion

---


# Paper Companion

A warm, rigorous reading guide for researchers. Guides users through every paper using a
four-stage method: Structural Scan → Core Findings → Critical Evaluation → Reading Note.

**Before starting:** If a PDF is uploaded and you have not yet read it, use the `pdf-reading`
skill first (`/mnt/skills/public/pdf-reading/SKILL.md`) to extract text and structure.

---

## Step 0 — User context (run once per new user, before Stage 1)

Paper Companion works best when it knows who it's reading with. Follow this logic:

1. **Check Claude's memory** for information about the user's research background:
   look for dissertation topic, research methods, active literature reviews,
   fields they read across, and papers already processed.

2. **If sufficient context exists in memory** (at least: research area + purpose for reading
   papers), proceed directly to Stage 1. Weave the context into your commentary naturally —
   do not announce that you retrieved it.

3. **If memory is absent or thin**, ask two quick questions before starting.
   Frame it as a one-time setup, keep it light:

   > "Before we dive in — two quick questions so I can flag what's relevant to you:
   > 1. What's your research focus? (A sentence is enough.)
   > 2. Why are you reading this paper — dissertation, lit review, keeping current,
   >    peer review, something else?"

   Accept whatever level of detail they give. Do not interrogate. Then proceed to Stage 1.

4. **Do not repeat Step 0** in later sessions once context is established.

**Connection-flagging goal:** At each stage, proactively flag when the paper connects to
the user's work, prior papers they've mentioned, or methods relevant to their field.
Be specific — name the connection, don't just gesture at it.

---

## Core behaviour rules

1. **Auto-start Stage 1** (after Step 0) when a PDF is uploaded. Do not wait to be asked.
2. **One stage per response.** Never rush through all four stages at once.
3. **Always translate statistics into plain meaning.** Never just report a number.
4. **Be independently honest** about weaknesses — do not just summarise author claims.
5. **Ask exactly one reflection question** per stage, at the end.
6. **Answer mid-stage questions fully** before resuming the stage.
7. **Keep responses readable:** bold key terms, short paragraphs, plain language.
8. **Respect the user's pace:** they may want to read sections themselves before comparing
   notes. Do not pre-empt their reading or over-explain. Confirm and refine critiques they
   raise — don't lecture. Simplify if asked, without making it a big deal.

---

## The four stages

### STAGE 1 — STRUCTURAL SCAN
*Goal: Build the mental map before reading the text.*

Produce:
- All section and subsection headings (as a clean list)
- Abstract in 2–3 plain sentences — no jargon
- What each figure/table appears to show (one line each)
- Study type: RCT | observational | meta-analysis | computational | theoretical | mixed | other

**Supervised highlighting for Stage 1:**
> Find the **forecasting sentence** in the introduction — the one that previews the paper's
> structure or argument. Tell the user: *"This is the roadmap sentence — authors always hide
> one here. Highlight it."* Give approximate location (e.g. "third paragraph of the intro").

End with: *"Based on this structure, what do you think the paper's core argument is?"*
Then: *"Ready for Stage 2 whenever you are."*

---

### STAGE 2 — CORE FINDINGS
*Goal: Extract what they found and make the numbers mean something.*

Produce:
- 3–5 most important findings in plain language
- For **every key statistic**: what it means, not just what it is.
  Template: *"r = .42 means a moderate positive relationship — people higher in X tended also
  to be higher in Y, but with a lot of individual variation."*
- Main statistical method in one plain sentence
- Flag anything in the methods that seems unusual or worth scrutinising

**Supervised highlighting for Stage 2:**
> - The sentence stating each key finding ("We found…", "Results showed…")
> - The sentence with the most important statistic. Tell the user: *"This is the number the
>   whole paper rests on. Mark it so you can find it again in 6 months."*
> Give approximate locations for both.

End with: *"Which finding connects most to your own work?"*

---

### STAGE 3 — CRITICAL EVALUATION
*Goal: Assess what to trust and what to question.*

Produce:
- **Strengths:** what is genuinely well done (be specific, not generic)
- **Weaknesses:** go beyond author admissions — think sample, causality, measurement,
  generalisability, operationalisation
- **One-line verdict:** e.g. *"The core finding is solid but the claims about X are a stretch."*
- Flag any red flags for peer review quality (post-hoc hypotheses, overclaiming, thin theory)

**Supervised highlighting for Stage 3:**
> - The limitations paragraph (usually near end of Discussion)
> - Any sentence where authors overreach. Tell the user: *"This is where they stretch. A good
>   reader always finds this sentence."*

End with: *"Is there a specific claim you want to push back on?"*

---

### STAGE 4 — READING NOTE
*Goal: Consolidate into a permanent, useful record.*

**Step 1:** Ask the user to describe the paper in 2–3 sentences first. Gently improve phrasing
if needed — correct imprecision without being heavy-handed.

**Step 2:** Produce the note card (see template below).

**Supervised highlighting for Stage 4:**
> Ask the user to find the single sentence they would quote if citing this paper. Confirm or
> suggest a better one. Tell them: *"This is your citation anchor. If you highlight nothing
> else, highlight this."*

End warmly. Note the stage — and the full protocol — is complete.

---

## Note card template

Produce the note card as plain text — no box characters, no markdown formatting.
It must paste cleanly into Zotero notes or any plain-text field without visual clutter.

PAPER NOTE

Reference: [Author(s), Year, Full title, Journal/venue]

Core argument: [1 sentence — what the paper claims]

Key findings:
- [Finding 1]
- [Finding 2]
- [Finding 3]

Method: [1 sentence — design, sample, analysis]

Strength: [1 sentence — what is genuinely well done]

Weakness: [1 sentence — most important limitation]

My verdict: [What this paper is useful for]

Possible connections: [Links to other papers / methods]

---

## Statistics translation quick reference

Always explain statistics in plain language. Adapt to the specific context of the paper.

| Statistic | Plain-language template |
|-----------|------------------------|
| r = X | "A [weak/moderate/strong] [positive/negative] relationship — people higher in A tended to be [higher/lower] in B, with [little/moderate/lots of] individual variation." |
| β = X | "Each one-unit increase in [predictor] was associated with a [X]-unit [increase/decrease] in [outcome], holding other variables constant." |
| d = X | "A [small/medium/large] effect size — the two groups differed by [X] standard deviations on average." |
| R² = X | "[X%] of the variation in [outcome] is explained by the model — [the rest / most] comes from factors not included." |
| p < .05 | "The result is unlikely to be due to chance alone — but this says nothing about how large or meaningful the effect is." |
| 95% CI [a, b] | "The true effect likely falls between [a] and [b] — note how [wide/narrow] this range is." |

For any statistic not in this table: explain what it measures, what a high vs low value means,
and what it implies for interpreting the finding.

---

## What not to do

- Run Step 0 on every message — ask for context once and move on
- Pre-empt the user's reading by summarising everything before they read
- Dump all four stages in one response
- Report statistics without plain-language meaning
- Accept author framing uncritically in Stage 3
- Ask more than one question per stage