Agent skill

Paper Details

by mathbullet in mathbullet/skills

Produce a detailed Markdown explainer of an academic paper. An agent skill from mathbullet/skills.

MITAuto-check passedDocuments & Office

Install Paper Details

skills CLI
$ npx skills add mathbullet/skills --skill paper-details -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mathbullet/skills paper-details --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mathbullet/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/paper-details/skills/paper-details .claude/skills/paper-details && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
paper-details
GitHub stars
173
Token cost
~3.6k tokens
SKILL.md length
1,929 words
Files
2 (incl. scripts)
Skills in repo
8
Repo updated
First seen
Licence
MIT

At a glance

Produce a detailed Markdown explainer of an academic paper. An agent skill from mathbullet/skills.

  • Works in 7 steps: Deliverable structure → Quotation and source reference → Equations → …
  • The user asks for a detailed paper write-up
  • SKILL.md covers 1. Deliverable structure, 2. Quotation and source…, 3. Equations and 4. Numerical results, plus 3 more sections
  • Runs Python scripts from its folder; calls uv

What it does

Paper Details is an agent skill from mathbullet/skills. Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citations to other works use author-short form. Use when the user asks for a detailed paper write-up, a thorough paper explainer, or invokes "paper details". Depends on documenting-with-sources and writing-quotation.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/extract_images.py`).

It sits in Documents & Office, covering LaTeX and Citation management. It works with LaTeX. The repository describes itself as: mathbullet Agent Skills. The licence is MIT.

When your agent uses it

  • The user asks for a detailed paper write-up
  • A thorough paper explainer
  • Invokes paper details

Example prompts

  • “paper details”
  • “/paper-details”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Deliverable structure
  2. Quotation and source reference
  3. Equations
  4. Numerical results
  5. Describing experiments
  6. Source list
  7. Section-by-section subagent audit

What it can do on your machine

Read from SKILL.md and the folder at commit 5ab997f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Paper Details loads about 3.6k tokens when it runs. Until then it costs about 126 tokens; SKILL.md has 1,929 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~126
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mathbullet/skills at commit 5ab997f, republished under its MIT licence (© mathbullet). 1,929 words, ~3,555 tokens.

Download SKILL.mdSave it as .claude/skills/paper-details/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
paper-details
description
Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citations to other works use author-short form. Use when the user asks for a detailed paper write-up, a thorough paper explainer, or invokes "paper details". Depends on documenting-with-sources and writing-quotation.

Paper Details

Conventions for producing a detailed Markdown explainer of an academic paper. The aim of this skill is to describe the paper accurately, not to critique it.

This skill follows the shared sourced-writing conventions defined in documenting-with-sources. Read documenting-with-sources before drafting.

1. Deliverable structure

1.0 Output location

Write the explainer as a .md file under the project's reports/ directory, where "the project" is the root that contains the source paper PDF. Create the directory if it does not exist.

  • Path: {project-root}/reports/{paper-filename-base}.md
  • Example: if the PDF is at /path/to/project/papers/foo.pdf, the output is /path/to/project/reports/foo.md

Do not write next to the PDF, and do not write at the project root. Do not ask the user for the output path — determine it mechanically by the rule above.

1.1 Opening

In this order:

  1. Title (# {paper title} — Detailed Explainer)
  2. Bibliographic info (authors, affiliations, venue, year, arXiv/DOI, URL)
  3. Full abstract — quote the original in a code block per writing-quotation; if the original is in a non-working language, place the translation alongside as a separate paragraph in the same block.
1.2 Body section structure

The body's section structure follows the paper's. If the paper has Section 1 Introduction, Section 2 Method, Section 3 Results, ..., the explainer uses the same order and the same headings.

Add explainer-only sections (e.g. "Strengths of the paper", "Limitations of the paper", "Source list") after the paper's own section structure.

1.3 Bullet lists vs prose

Pick the form by the nature of the content.

  • Bullet lists fit enumerations of parallel items — variable lists, definitions of evaluation metrics, table-column descriptions, comparison points between methods, etc. When the content is genuinely list-shaped, the prose form blurs the boundaries between items.
  • Prose fits relationships, causal flow, contextual explanation. When the reader needs to understand why items appear together or how they form a single argument, prose is what holds it together.
1.4 Figures and tables: extract as images

Never reconstruct a figure or table from scratch (no hand-written HTML tables, no redrawn SVG figures). Extract them as images from the source paper and insert those images into the deliverable (.md or HTML). Hand reconstruction introduces transcription errors and loses the original layout — bold, underline, colour-coded legends — so it is forbidden.

Use the script bundled with this skill:

uv run {this-skill-dir}/scripts/extract_images.py <PDF path>
  • The script finds Figure N / Table N captions in the PDF and clip-renders the figure/table region directly above each caption — the bounding box of vector drawings, rules, and embedded images — at 300 dpi. Figures and tables are usually drawn as vectors with no embedded raster, so extraction is region rendering, not pulling out an embedded image.
  • Output defaults to {project-root}/images-from-papers/ as {paper-filename-base}-fig{N}.png / {paper-filename-base}-table{N}.png. The extraction list is recorded in {base}-manifest.json in the same directory.
  • A multi-panel float (e.g. one Table float that contains panels (a)–(f)) is extracted as a single image, matching the single float in the paper.
  • After extraction, insert the images from images-from-papers/ into the deliverable. In .md, reference them by relative path, e.g. ![Figure N](../images-from-papers/{base}-fig{N}.png). In an HTML deliverable created with html, embedding as a base64 data URI is acceptable.
  • Add the translated caption as a separate paragraph below the image. The caption text inside the image stays in the original language (it is not redrawn), so the translation goes outside the image.
  • If the script occasionally drops a figure/table or includes too much margin, raise --dpi or inspect the output and adjust the pymupdf clip rectangle for that item. Either way, do not abandon the image-extraction approach.

2. Quotation and source reference

2.0 Output is built around translated quotation blocks

Build the explainer primarily out of "original quotation + translation" blocks that cover the paper's body in full. Do not make summarised, paraphrased prose the main act. Keep the reader able to check the original against the translation throughout the document.

Prose — the writer's own text — is a complement to the quotation-led flow, added only when one of the following holds:

  • The connection between quotation blocks is unclear, and the relationship or logical flow between sections or paragraphs needs bridging.
  • A supplementary explanation — variable definitions, prerequisite knowledge, how to read a figure or table, the first-occurrence definition of a term — is genuinely useful to the reader.

Where neither holds, do not re-summarise the original in prose; let the quotation block speak for itself. Do not settle into a "quotes carry the gist, prose summarises" split.

To convey the paper's claims accurately, include direct quotations from the original. A summary alone does not let the reader judge whether the writer's interpretation is correct.

Within the [label (YYYY/MM), location] structure defined in documenting-with-sources, the paper-details skill fills the label slot differently depending on which work is referenced.

Reference to the paper under review

When referring to the paper under review, omit the author and year and cite the position only, in the form [p.X, Section Y.Z]. Since the entire explainer is about a single paper, the author does not need to be repeated each time.

Examples: [p.4, Section 1], [p.21, (15)], [p.31, Figure 2].

Reference to other works

For other works that the paper cites, use [author-short (YYYY)] inline.

  • Pin location with a section, page, table, or figure number.
  • Do not abbreviate the list of cited works with phrases like "...and others". List each work individually with its author and year. The reader of the explainer depends on this list — citing the explainer's host paper alone does not substitute for naming the works the paper references.

3. Equations

3.1 Syntax
  • Equations are written in LaTeX. Inline as $...$, display as $$...$$.
  • Forbidden: putting equations inside a code block. Forbidden: writing equations in plain-text form, pseudo-code form, or anything other than LaTeX syntax.
3.2 Variable definitions

Every symbol in an equation is unknown to the reader until it is defined. Before presenting an equation, define every variable and symbol that appears in it. Do not place an equation without its variable definitions in scope.

In equation-heavy sections (theory, method formalisation), put a variable table at the top of the section. Group variables by role. Each entry includes:

  • The symbol (in LaTeX).
  • What it means (one sentence).
  • A note that helps intuition (concrete example, value range, behaviour in special cases).

Example:

Inputs:

- $n$: number of characters in the input text. The length of the user's prompt.
- $K$: the model's context-window length in characters. Inputs longer than this cannot be passed to the model directly.

Planner outputs:

- $k^*$: branching factor at each level — how many chunks to split into. With $k^*=5$ the input is split into five chunks at each level.
- $\tau^*$: threshold below which the chunk is sent to the LLM as-is, without further splitting. With $\tau^*=26{,}000$, chunks of 26,000 characters or fewer go straight to the LLM.

In sections with few equations, defining variables in-line before and after each equation is acceptable — but the principle that no equation appears with undefined symbols still holds.

4. Numerical results

  • Present experimental results in tables when possible.
  • Place the proposed method and baselines side by side.
  • Transcribe numbers exactly as they appear in the paper. Do not round or approximate.
  • Before showing the table, define each column and each metric in a preceding bullet list. By the time the reader sees the numbers, the meaning of each column is clear.
  • Do not use generic words like "accuracy" or "performance" loosely. Use the metric name the paper itself defines (classification accuracy, pass rate, pass@1, etc.).
  • If the same word is used in different senses across the paper (e.g. a method name that means "the best single result" in a table but "the entire procedure" in the body), call out the polysemy explicitly.
Show full SKILL.md (756 more words)Show less

5. Describing experiments

5.1 Spell out the procedure

In experiment sections, the reader must be able to follow what was actually done. Do not omit:

  • The concrete task steps (what the input is, what each step produces, what the final output is).
  • The data-split structure (search set / validation set / test set — what each is for, and which result corresponds to which split).
  • The search or optimisation procedure (initial state, number of iterations, what each iteration generates, the criterion under which the final result is selected).
5.2 Independence between sections

Each experiment section reads on its own. Do not refer back to "the method defined in Section X" with an unspecified abbreviation. Even when two experiments share a search procedure, restate it with the experiment-specific parameters in each section.

5.3 Consistent granularity

When the paper has multiple experiment sections, keep the level of detail consistent across them. Do not write the search procedure thoroughly in one section and dismiss it in one sentence in another.

5.4 Define concepts before use

Define every concept the first time it appears in the explainer, before using it. Do not omit concepts the paper itself defines. In particular, before presenting a table or a number, make sure every concept needed to read that number has already been defined.

6. Source list

Place the source list at the end of the explainer, formatted per documenting-with-sources. In addition to the paper under review, include every other work the explainer mentions.

7. Section-by-section subagent audit

A first-pass draft typically contains errors that a single re-read misses: numbers transcribed off by a digit, citation numbers mapped to the wrong reference, sentences whose translated meaning drifts from the original, citations from the paper that never made it into the explainer. Before treating the draft as done, audit it section by section with subagents.

7.1 Output location

Write audit results to {cwd}/subagent-reviews/{NN-section-name}.md, one file per section. Create the directory if it does not exist. Audits are separate artifacts from the explainer; do not put them under reports/.

7.2 Sectioning

Split the explainer into independent units that align with the paper's section structure:

  • Abstract and bibliographic info (one unit)
  • Each top-level section of the paper body (Introduction, Background/Related Work, Method, Experiments, Limitations, Conclusion, etc.)
  • The source list at the end of the explainer

Larger sections (e.g. an Experiments section with multiple sub-experiments and tables) can be split further if a single auditor would face too much material. Keep one auditor per file.

7.3 Auditor brief

Spawn one general-purpose subagent per section, in parallel. The brief tells each subagent to:

  1. Read the paper PDF and the corresponding section of the explainer.
  2. Verify factual accuracy: claims, equations, numbers in tables and inline numbers in prose, citation-number ↔ reference correspondence.
  3. Verify translation accuracy when the explainer is in a non-source language. Look for: dropped words, added implications not in the original, inappropriate word order, untranslated source-language idioms.
  4. Verify formatting compliance: citation form ([p.X, Section Y.Z] for the paper under review, [author-short (YYYY)] for other works), no ** bold decoration, quotation rules from writing-quotation (code-block quotes, original-translation pairing, source reference on its own line outside the block).
  5. Verify terminology consistency across the section.

Each audit file uses three headings: overall assessment, findings, suggested edits. Findings point at specific lines or quoted passages in the explainer; for translation issues, quote both the source and the explainer's rendering so the synthesis step can compare them side by side.

7.4 Synthesis and edits

After all audits return:

  1. Read each audit file in full.
  2. Cross-check audit claims against the paper itself before applying them. Auditors can be wrong, and two auditors may disagree on the same fact (for example, two reviewers may map a numeric citation [16] to different references). When that happens, consult the paper's References list and resolve the conflict from the source.
  3. Apply confirmed edits to the explainer.
  4. When applying overlapping edits across sections (e.g. a citation-label change that recurs throughout), make the change consistently in every occurrence.
  5. Move on once findings have been resolved — there is no need to write a separate "responses to audit" document; the audited explainer itself is the artifact.
7.5 Vocabulary

The artifact this skill produces is a detailed explainer, not a review. Audit outputs are reviews. Keep these terms separate in conversation with the user and in titles: do not call the explainer a "review", and do not call the audit a "detailed explainer". This separation prevents ambiguity when the user asks "update the review" partway through the workflow.

© mathbullet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (scripts) in plugins/paper-details/skills/paper-details of mathbullet/skills.

  • SKILL.md
  • scripts/extract_images.py

Open the folder on GitHubat commit 5ab997f

Compare with similar skills

Paper Details next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Paper Details compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Paper Details this skillmathbullet/skills173—~3.6kAutomated safety check: PassMIT
Literature Surveyai4s-research/ai4s-skills2372 repos~2kAutomated safety check: PassMIT
Latex To Word Workflowhajimi-kun/latex-to-word-workflow128—~2.3kAutomated safety check: PassMIT
Latex Paper Enbrycewang-stanford/Auto-Empirical-Research-Skills4.6k1 repos~2.2kAutomated safety check: PassCustom licence
Typst Paperbahayonghang/academic-writing-skills500—~3.6kAutomated safety check: PassNone
Light TypesettingLight0305/Light-skills640—~3.3kAutomated safety check: PassMIT

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Works with

Questions about Paper Details

What does Paper Details do?

Produce a detailed Markdown explainer of an academic paper. An agent skill from mathbullet/skills. Paper Details is an agent skill from mathbullet/skills. Produce a detailed Markdown explainer of an academic paper.

When should I use Paper Details?

Paper Details fits situations like: the user asks for a detailed paper write-up; A thorough paper explainer; invokes paper details.

How do I install Paper Details in Claude Code?

Run `npx skills add mathbullet/skills --skill paper-details -a claude-code`. Or copy the skill folder (plugins/paper-details/skills/paper-details in mathbullet/skills) into .claude/skills/paper-details in your project. Claude Code loads it when a task matches its description.

How do I install Paper Details in Codex?

Run `npx skills add mathbullet/skills --skill paper-details -a codex`. Or copy the skill folder (plugins/paper-details/skills/paper-details in mathbullet/skills) into .agents/skills/paper-details in your project. Codex loads it when a task matches its description.

Can I use Paper Details in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mathbullet/skills --skill paper-details -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/paper-details, .gemini/skills/paper-details, .github/skills/paper-details and .opencode/skills/paper-details in your project.

What does Paper Details need to run?

Going by SKILL.md and its folder, Paper Details needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Paper Details access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Paper Details safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Paper Details use?

Paper Details is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Paper Details use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Paper Details?

Skills that share tags, products or a category with Paper Details: Literature Survey (ai4s-research/ai4s-skills, 237 stars), Latex To Word Workflow (hajimi-kun/latex-to-word-workflow, 128 stars), Latex Paper En (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars) and Typst Paper (bahayonghang/academic-writing-skills, 500 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Paper Details?

mathbullet (a GitHub user) maintains it in mathbullet/skills, which has 173 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 9, 2026.

Source: mathbullet/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.