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Limitation Weaver

Community
WILLOSCAR
limitation-weaver

Rewrite limitation passages so the paper keeps limitations without falling into count-based slot phrases (e.g., \"Two limitations…\") across many H3s.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill namelimitation-weaver
Stars
513
Forks
39
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Limitation Weaver 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/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/limitation-weaver
  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/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/limitation-weaver .claude/skills/limitation-weaver
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Limitation Weaver 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 Limitation Weaver 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 Limitation Weaver 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.

Limitation Weaver (keep caveats, lose the slot phrase)

Triggers & routing

  • Trigger: limitation weaver, rewrite limitations, remove two limitations, 去Two limitations, 局限改写, caveat rewrite.

Purpose: keep survey-grade intellectual honesty without triggering a strong generator-voice tell:

  • repeated count-based openers ("Two limitations…", "Three takeaways…")

This is not about removing limitations. It is about expressing them in a paper-like way that varies naturally across sections.

Inputs

Required:

  • output/WRITER_SELFLOOP_TODO.md (Style Smells section)
  • the referenced sections/S<sub_id>.md files

Optional (helps keep limitations grounded):

  • outline/writer_context_packs.jsonl (use failures_limitations / limitation_hooks / verify_fields when present)

Workflow (explicit inputs)

  • Start from output/WRITER_SELFLOOP_TODO.md (Style Smells) to locate the exact sections/S*.md files to rewrite.
  • Use outline/writer_context_packs.jsonl to keep limitations grounded in the subsection's evidence boundary (no guessing).

Outputs

  • Updated sections/S<sub_id>.md files (still body-only; no headings)

Role prompt: Caveat Editor (paper voice)

text
You are editing the limitation content of a survey subsection.

Goal:
- preserve the subsection-specific limitation(s)
- remove count-based opener slots and repetitive cadence
- keep limitations tied to the protocol/evidence boundary (what changes interpretation)

Constraints:
- do not invent facts
- do not add/remove/move citation keys
- do not weaken the section by deleting real limitations

Anti-pattern (rewrite immediately)

  • Two limitations stand out. First, ... Second, ...
  • Three key takeaways are ...

Why it hurts: it creates a reusable template slot that repeats across H3s and reads auto-generated.

Rewrite moves (choose one; vary across H3s)

  1. Fold caveat into a contrast paragraph (preferred)
  • Put one caveat sentence as the last sentence of the A-vs-B paragraph.
  • Shape: “However, …; this matters because …”
  1. Single caveat paragraph without counting
  • Start with a natural opener (rotate across H3s; avoid repeating the same stem):
    • “These results hinge on …”
    • “Interpretation depends on …”
    • “Evidence is thin when …”
    • “A caveat is that …” (use sparingly)
  • Then add one sentence that explains why it changes interpretation.
  1. Verification-target framing (when evidence is abstract-only / underspecified)
  • Convert the limitation into a checkable condition:
    • “To make this comparison robust, evaluations need to report …”
  • Keep it concrete (budget/tool access/logging/threat model), and do not repeat this pattern across many H3s.

Mini examples (paraphrase; do not copy)

Bad:

  • Two limitations temper strong conclusions. First, budgets differ. Second, ablations are missing.

Better (folded into contrast):

  • ...; however, reported budgets and retry policies vary widely, which makes head-to-head comparisons fragile unless those constraints are normalized.

Better (single caveat paragraph):

  • These results hinge on under-specified verification and retry policies; this matters because success rates can shift substantially along the success–cost frontier.

Done checklist

  • No rewritten subsection uses count-based limitation openers as a default structure.
  • Limitations still exist and remain subsection-specific.
  • Citation keys are unchanged.
  • writer-selfloop remains PASS and Style Smells shrink.

Frequently asked questions

What does the Limitation Weaver AI skill do?

Rewrite limitation passages so the paper keeps limitations without falling into count-based slot phrases (e.g., \"Two limitations…\") across many H3s.

Why use Limitation Weaver on TypingMind?

Because you install it once and use it with any model. Limitation Weaver 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 Limitation Weaver in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/limitation-weaver. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Limitation Weaver?

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 Limitation Weaver?

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

Is the Limitation Weaver AI skill free?

It is published on GitHub by WILLOSCAR. Check the repository for licensing terms. 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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