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result-figure-consistencycheck

Checks consistency between paper result descriptions and figure legends (text-only) when the input is a PDF-to-Markdown full text containing page breaks (e.g., `## Page XX`) and legend text; outputs a Markdown consistency report and a UTF-8 CSV issue list.

Install / Use

npx skills add aipoch/medical-research-skills --skill result-figure-consistencycheck

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

91/100

Supported Platforms

Universal

Our assessment of result-figure-consistencycheck

result-figure-consistencycheck scores 91/100 on our quality scale, 140th of 413 Data & Analytics skills we index (top 34%).

Its SKILL.md is 6.7 KB long, well organised into 16 sections with 2 code examples: a thorough specification that gives an agent plenty to work with.

With 1,916 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
18/20
Description
15/15
Adoption
14/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 12 days ago, so result-figure-consistencycheck is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-09-30. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

result-figure-consistencycheck compared with similar skills

All 4 of these similar skills score higher than result-figure-consistencycheck; compare them before choosing.

SkillScoreStarsUpdatedFormat
result-figure-consistencycheck (this skill)by aipoch911.9k12d agoSKILL.md
Agent-Reachby Panniantong10086.2k14d agoCLAUDE.md
algorithmic-artby anthropics100177.9k7d agoSKILL.md
pptxby anthropics100177.9k7d agoSKILL.md
designby nextlevelbuilder100130.2k8d agoSKILL.md

Frequently asked questions

How do I install result-figure-consistencycheck?
Run npx skills add aipoch/medical-research-skills --skill result-figure-consistencycheck. The install tabs above show the steps for each supported agent.
Which AI agents does result-figure-consistencycheck work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is result-figure-consistencycheck safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is result-figure-consistencycheck still maintained?
The repository was last updated 12 days ago, so result-figure-consistencycheck is actively maintained.

name: result-figure-consistencycheck description: Checks consistency between paper result descriptions and figure legends (text-only) when the input is a PDF-to-Markdown full text containing page breaks (e.g., ## Page XX) and legend text; outputs a Markdown consistency report and a UTF-8 CSV issue list. license: MIT author: AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You converted a paper PDF to Markdown and need to verify that Results text matches figure legends (without inspecting the images).
  • You want to detect missing figure references in the Results section (e.g., a legend describes an analysis not mentioned in text).
  • You need to find numerical/label mismatches (e.g., group names, time points, units, n-values) between Results paragraphs and legends.
  • You are preparing a revision and want an actionable discrepancy list down to the panel/sub-figure level.
  • You need standardized outputs (Markdown report + CSV) for editorial or QA workflows.

Key Features

  • Compares Results descriptions vs figure legend text using the PDF-to-Markdown source (including ## Page XX markers).
  • Produces:
    • A Markdown consistency report (UTF-8).
    • A CSV issue list (UTF-8) with structured fields for tracking and revision.
  • Enforces a template-based report format using assets/consistency_template.md.
  • Uses a rule/checklist reference from references/guide.md.
  • Text-only validation: does not read figure images and does not infer visual content.

Dependencies

  • None (no external runtime dependencies specified).
  • Input prerequisite (if starting from PDF): a PDF-to-Markdown conversion step (e.g., pdf-extract) must be completed before running this check.

Example Usage

Input

Place the converted full text Markdown in your working location (example: inputs/paper_fulltext.md). The file should include page headers like:

## Page 12
... Results text ...

Figure 3. ...
(A) ...
(B) ...

Run (conceptual workflow)

  1. Read the full Markdown input (PDF conversion output).
  2. Identify:
    • Result paragraphs describing findings.
    • Figure legend blocks (including panel labels such as A/B/C).
  3. Compare legend statements against Results statements and record discrepancies.
  4. Write outputs to outputs/:
    • outputs/consistency_report.md (UTF-8)
    • outputs/consistency_issues.csv (UTF-8)

Output files

outputs/consistency_issues.csv (UTF-8) columns:

Figure Number,Location/Reference,Issue Description,Suggested Revision,Priority

Notes:

  • Location/Reference must contain only Page XX.
  • Issues should be granular to the panel/sub-figure level when applicable.

outputs/consistency_report.md (UTF-8) must follow:

  • Template: assets/consistency_template.md
  • If no issues are found, write "None found" in the relevant sections.

Implementation Details

  • Scope of comparison

    • Only compare main text Results descriptions and figure legend text present in the Markdown input.
    • Do not inspect images or infer information not explicitly stated in text.
  • Rules and checklist

    • Follow the specific checking rules and required output points defined in:
      • references/guide.md
  • Report formatting

    • The Markdown report must be generated by filling:
      • assets/consistency_template.md
    • Ensure all outputs are saved under:
      • outputs/ (within the skill directory)
  • Granularity and actionability

    • Record discrepancies at the most specific level possible (e.g., Figure 2B vs Figure 2 overall).
    • Provide a concrete Suggested Revision whenever feasible (e.g., align terminology, correct numbers/units, add missing reference).
  • Language

    • Default output language is Chinese.
    • If the user explicitly specifies another language, output in that language.

When Not to Use

  • Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.
  • Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.
  • Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.

Required Inputs

| Field | Required | Format/Source | Example | If Missing | |---|---|---|---|---| | User task description | Yes | Text | Research question, writing goal, analysis objective | Stop and ask user to provide | | Primary input material | Depends on task | Text, file path, ID, table, or literature | PMID, PDF, CSV, DOCX, keywords, etc. | Specify which material type is missing | | Output preference | No | Text | Language, format, target journal, template | Use skill default format |

Output Contract

  • Primary output: Structured result or target file aligned with this skill's objective.
  • Optional output: Intermediate check notes, issue list, supplementary suggestions, or generated file paths.
  • Format requirement: Unless the user specifies otherwise, prefer stable, reviewable Markdown or JSON; if the skill's bundled script requires a fixed format, use that format.
  • If partially complete: Must explicitly mark as PARTIAL and state which steps are completed and which remain.

Failure Handling

  • Missing critical input: Explicitly state which fields, files, or identifiers are missing and pause.
  • Script, template, or resource execution failure: Report the failing step, likely cause, and recovery suggestions — do not silently degrade.
  • Partial completion only: Return the verified portion first, then list remaining blockers and suggested next steps.

User Checkpoints

  • Before executing batch processing, overwriting files, long-running searches, or multi-stage generation, confirm scope and output format with the user.
  • Before proceeding when a key judgment is ambiguous, evidence is insufficient, or the workflow is entering the next stage, confirm with the user.

Input Validation

This skill accepts requests that match the documented purpose of result-figure-consistencycheck and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

result-figure-consistencycheck only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Quick Validation

  • Check that key scripts, templates, or reference file paths this skill depends on exist.
  • Check that the final output contains the core fields, sections, or files specified for this task.
  • Check that results clearly mark assumptions, limitations, and incomplete items.

Related Skills

View on GitHub
GitHub Stars1.9k
CategoryData
Updated12d ago
Forks175

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions