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Results Backfill

Community
yunshenwuchuxun
results-backfill

Back-fill verified experiment results into an existing empirical paper draft. Resolves placeholders, upgrades hypotheses to factual claims, generates figures/tables, drafts abstract, and runs rhythm refinement + QA.

Overview

Publisheryunshenwuchuxun
Repositorylatex-paper-skills
Skill nameresults-backfill
Stars
270
Forks
23
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yunshenwuchuxun on GitHub. Read the source before you install it.

Installation

Install the Results Backfill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yunshenwuchuxun/latex-paper-skills/tree/main/.codex/skills/results-backfill
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yunshenwuchuxun/latex-paper-skills.git /tmp/latex-paper-skills
mkdir -p .claude/skills
cp -r /tmp/latex-paper-skills/.codex/skills/results-backfill .claude/skills/results-backfill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Results Backfill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Results Backfill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Results Backfill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Results Back-fill

Use this skill after empirical-paper-writer has produced a draft with placeholders and the user has run experiments outside the AI session.

When to Use

  • empirical-paper-writer has completed design + code + placeholder draft.
  • The user has run experiments in their configured conda environment.
  • paper/results/ contains new CSV / data files from completed experiments.

When NOT to Use

  • The paper draft does not yet exist (use empirical-paper-writer first).
  • Experiments have not been run yet.
  • The user only wants experiment design without paper completion.

Inputs

  • paper/main.tex (draft with placeholders)
  • paper/results/*.csv (experiment results)
  • notes/design/experiment-matrix.csv
  • issues/<timestamp>-<slug>.csv
  • brief/contribution-map.yaml
  • paper.config.yaml (runtime configuration)

Outputs

  • Updated main.tex with verified results, real figures/tables, factual claims
  • Updated issues CSV with Result_Status=verified and Status=DONE
  • Generated LaTeX input files under paper/results/
  • Drafted abstract (W0)
  • Refined prose (RF1)
  • Compiled main.pdf

Non-Negotiable Rules

  1. No fabrication: Only use data from actual result files. Never invent numbers.
  2. Verify before claiming: Only upgrade (hypothesis) to factual claim when CSV data confirms the result.
  3. Bounded claims: Factual statements must include specific numbers from verified results (e.g., "reduces violations by 12.3%"), not vague improvements.
  4. Dependency enforcement: Follow the same dependency rules as empirical-paper-writer — never mark DONE if dependencies are not DONE/SKIP.

Workflow

Phase 1: Results Discovery

  1. Read paper.config.yaml for runtime configuration.
  2. Scan paper/results/ for all CSV, JSON, and data files.
  3. Read notes/design/experiment-matrix.csv to identify all planned experiments.
  4. Match result files to experiment-matrix rows:
    bash
    python3 scripts/discover_results.py --project-dir <paper_dir>
    See references/backfill-workflow.md for naming conventions and the assets/result-file-template.csv schema.
  5. If matches look correct, update the matrix in-place:
    bash
    python3 scripts/discover_results.py --project-dir <paper_dir> --update-status
  6. Read the issues CSV and update Result_Status for matched experiment issues (E5-E8).

Phase 2: Paper Back-fill

Execute in order:

  1. Contribution upgrade:

    • Read brief/contribution-map.yaml.
    • For each claim with verified supporting experiments, update main.tex:
      • Replace (hypothesis) with bounded factual statement.
      • Update Introduction contributions list.
  2. Results section fill:

    • Main comparison: populate tables from main_results.csv.
    • Ablation studies: populate ablation table from ablation results and upgrade the surrounding narrative from hypothesis-safe to verified, bounded claims.
    • Robustness/error analysis: populate figures/tables and write verified takeaways tied to specific numbers.
    • Efficiency: populate efficiency comparison table and write a bounded trade-off discussion (accuracy vs cost).
  3. LaTeX table/figure generation:

    • Convert CSV results to LaTeX \input{} files under paper/results/:
      bash
      python3 scripts/generate_results_table.py <results.csv> -o paper/results/<name>.tex --bold-best --caption "..." --label "tab:..."
    • Generate TikZ plots or include matplotlib-exported PDFs where appropriate.
    • Replace \fbox{...placeholder...} with actual \includegraphics or table references.
  4. Figure resolution:

    • For each remaining \fbox{...} placeholder:
      • If data exists → generate the figure.
      • If data is missing → replace with [Figure pending: <reason>].
  5. Abstract (W0):

    • Draft abstract from verified results following the rules in experiment-evidence.md:
      • Problem (1 sentence)
      • Method (1-2 sentences)
      • Setting (1 sentence)
      • Key result (1 sentence, verified only)
      • Implication (1 sentence)
    • Maximum 250 words. No citations. No unexpanded acronyms.
  6. Conclusion upgrade:

    • Update the Conclusion section to remove (hypothesis) / [Pending: ...] tags for any claim now supported by verified results.
    • Ensure every factual takeaway is bounded by specific numbers from verified tables/figures.
  7. Mark issues DONE:

    • Update E5-E8, W0, and any back-filled Writing issues in the issues CSV.
    • Respect dependency enforcement.

Phase 3: Polish

  1. RF1 — Rhythm refinement:

    • Apply latex-rhythm-refiner section-by-section.
    • Preserve all citations and verified numbers.
  2. QA:

    • Run citation audit:
      bash
      python3 ../arxiv-paper-writer/scripts/citation_policy.py --project-dir <paper_dir> audit-bib
      python3 ../arxiv-paper-writer/scripts/citation_policy.py --project-dir <paper_dir> audit-tex --issues <issues.csv>
    • Run issue validation with tex audit:
      bash
      python3 ../empirical-paper-writer/scripts/validate_empirical_paper_issues.py <issues.csv> --audit-tex <paper_dir>/main.tex
  3. Compile:

    bash
    python3 ../arxiv-paper-writer/scripts/compile_paper.py --project-dir <paper_dir> --check-warnings --fail-on-warnings
  4. Deliver main.tex, ref.bib, figures, and main.pdf.

Runtime Environment

Before running any script:

  1. Read paper.config.yaml → runtime.conda_env or runtime.python.
  2. If conda_env is set, activate it: conda activate <env_name>.
  3. If python is set, use that interpreter directly.
  4. If neither is set, ask the user which conda environment to use.

Relationship to Other Skills

paper-from-zero
    ↓ routes to
empirical-paper-writer (design + code + placeholder draft)
    ↓ user runs experiments
results-backfill (this skill: back-fill results + complete paper)

Success Criteria

  • All \fbox placeholders resolved or explicitly marked [Pending: <reason>].
  • No (hypothesis) tags remain for claims with verified evidence.
  • Abstract is non-placeholder, <=250 words, matches verified results.
  • All DONE issues pass dependency check.
  • validate_empirical_paper_issues.py --audit-tex produces zero warnings for verified issues.
  • Paper compiles without Overfull \hbox warnings.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Results Backfill AI skill do?

Back-fill verified experiment results into an existing empirical paper draft. Resolves placeholders, upgrades hypotheses to factual claims, generates figures/tables, drafts abstract, and runs rhythm refinement + QA.

Why use Results Backfill on TypingMind?

Because you install it once and use it with any model. Results Backfill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Results Backfill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yunshenwuchuxun/latex-paper-skills/tree/main/.codex/skills/results-backfill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Results Backfill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Results Backfill?

As many as you like. As long as a model supports skills, you can use Results Backfill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Results Backfill AI skill free?

Yes. It is published on GitHub by yunshenwuchuxun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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