Agent Skills
Literature Close Read
AIPOCH
Produce a structured close-reading report from a paper's full PDF-to-Markdown text (with `## Page XX` pagination and image references) when you need to systematically extract background, research questions, methods, results, limitations, and reproducible experimental details.
2
0
FILES
87100Total Score
View Evaluation ReportCore Capability
85 / 100
Functional Suitability
11 / 12
Reliability
9 / 12
Performance & Context
7 / 8
Agent Usability
14 / 16
Human Usability
8 / 8
Security
11 / 12
Maintainability
9 / 12
Agent-Specific
16 / 20
Medical Task
20 / 20 Passed
92When you have a full paper converted from PDF to Markdown and need a structured, in-depth interpretation rather than a brief abstract-style summary
4/4
88When you must extract reproducible experimental details (datasets, settings, controls, metrics, statistics) for replication or reimplementation
4/4
86Reads the entire Markdown paper text, prioritizing Methods and Results for technical fidelity
4/4
86Produces a structured close-reading report in Markdown (UTF-8), following a predefined template
4/4
86End-to-end case for Reads the entire Markdown paper text, prioritizing Methods and Results for technical fidelity
4/4
SKILL.md
Literature Close Reading
When to Use
- When you have a full paper converted from PDF to Markdown and need a structured, in-depth interpretation rather than a brief abstract-style summary.
- When you must extract reproducible experimental details (datasets, settings, controls, metrics, statistics) for replication or reimplementation.
- When you need to map the paper's logical chain (motivation → problem → method → experiments → conclusions) and identify missing links or ambiguities.
- When you want a systematic list of limitations, threats to validity, and follow-up research questions grounded strictly in the text.
- When figures/tables are referenced via Markdown images and you need them incorporated into the interpretation without guessing beyond what is shown.
Key Features
- Reads the entire Markdown paper text, prioritizing Methods and Results for technical fidelity.
- Produces a structured close-reading report in Markdown (UTF-8), following a predefined template.
- Extracts and organizes:
- research background and problem statement
- methodological details and experimental design
- key results and statistical evidence (as explicitly stated)
- limitations and threats to validity
- reproducible points and follow-up questions
- Supports Markdown inputs that include pagination headers like
## Page XXand image references such as. - Enforces a strict constraint: summarize only what is explicitly present in the text/images; do not infer or speculate.
- Uses external guidance and templates:
- Requirements/checklist:
references/guide.md - Output template:
assets/deep_reading_template.md
- Requirements/checklist:
Dependencies
pdf-extract(version: not specified) — used only when the source is PDF and must be converted to Markdown first.
Example Usage
# 1) (Optional) Convert PDF to Markdown if you only have a PDF
# Note: exact command/options depend on your local pdf-extract installation.
pdf-extract paper.pdf > paper.md
# 2) Run the close-reading process (manual or via your orchestration tool):
# Input: paper.md (full text converted from PDF, may include `## Page XX` and images)
# Guidance: references/guide.md
# Template: assets/deep_reading_template.md
# 3) Save the final report as UTF-8 Markdown under outputs/
mkdir -p outputs
# Example output file name:
# outputs/paper_close_reading.md
Minimal expected I/O contract:
- Input: a single
.mdfile containing the full paper text (PDF-to-Markdown), optionally with:- page headers like
## Page 01 - image references like

- page headers like
- Output: one UTF-8 encoded
.mdreport saved tooutputs/, formatted according toassets/deep_reading_template.md. - Language: default output is Chinese; if the user specifies a language, output in that language.
Implementation Details
-
Input reading rules
- Treat the Markdown as the authoritative source of truth.
- Pagination markers (e.g.,
## Page XX) may be used for navigation and citation, but should not alter meaning. - Image references may be used to interpret figures/tables only to the extent that the content is explicitly visible/legible.
-
Extraction and summarization rules
- Focus on Methods and Results first; then connect to background, problem statement, and conclusions.
- Capture experimental details precisely: datasets, splits, baselines, ablations, hyperparameters, training/inference settings, evaluation metrics, and statistical tests—only if stated.
- If a required field in the template cannot be filled from the text, write "Not specified".
-
Quality constraints
- No speculation: do not add assumptions, unstated motivations, or inferred mechanisms.
- Maintain traceability: ensure each claim in the report can be traced back to explicit paper content (text or figure/table).
- Output must be valid Markdown and saved in UTF-8 to avoid encoding issues.
-
Files used
- Requirements and checklist:
references/guide.md - Output template:
assets/deep_reading_template.md - Output directory:
outputs/(create if missing)
- Requirements and checklist:
When Not to Use
- Do not use this skill when the required source data, identifiers, files, or credentials are missing.
- Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
- Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.
Required Inputs
- A clearly specified task goal aligned with the documented scope.
- All required files, identifiers, parameters, or environment variables before execution.
- Any domain constraints, formatting requirements, and expected output destination if applicable.
Recommended Workflow
- Validate the request against the skill boundary and confirm all required inputs are present.
- Select the documented execution path and prefer the simplest supported command or procedure.
- Produce the expected output using the documented file format, schema, or narrative structure.
- Run a final validation pass for completeness, consistency, and safety before returning the result.
Output Contract
- Return a structured deliverable that is directly usable without reformatting.
- If a file is produced, prefer a deterministic output name such as
literature_close_read_result.mdunless the skill documentation defines a better convention. - Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations.
Validation and Safety Rules
- Validate required inputs before execution and stop early when mandatory fields or files are missing.
- Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material.
- Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result.
- Keep the output safe, reproducible, and within the documented scope at all times.
Failure Handling
- If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required.
- If an external dependency or script fails, surface the command path, likely cause, and the next recovery step.
- If partial output is returned, label it clearly and identify which checks could not be completed.
Quick Validation
Run this minimal verification path before full execution when possible:
No local script validation step is required for this skill.
Expected output format:
Result file: literature_close_read_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any