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Collaborating With Claude

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yunshenwuchuxun
collaborating-with-claude

Use the Claude Code CLI as a depth-analysis co-pilot for paper-from-zero. Primary role: claim tree compression, logical hole detection, evidence sufficiency audit, and review/empirical routing judgment. Also supports generic code collaboration as fallback.

Overview

Publisheryunshenwuchuxun
Repositorylatex-paper-skills
Skill namecollaborating-with-claude
Stars
270
Forks
23
Bundled files
3
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.

  • 3 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 Collaborating With Claude 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/collaborating-with-claude
  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/collaborating-with-claude .claude/skills/collaborating-with-claude
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Collaborating With Claude 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 Collaborating With Claude 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 Collaborating With Claude 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.

Collaborating with Claude Code (Codex)

Use Claude Code CLI as a depth-analysis collaborator for paper-from-zero, or as a generic code collaborator in fallback mode.

This skill provides a lightweight bridge script (scripts/claude_bridge.py) that returns structured JSON and supports multi-turn sessions via SESSION_ID.

Shared rules: This skill inherits all shared rules from ../_shared/collaboration-base.md (core rules, candidates-only constraint, fallback code-collaboration mode, safety guardrails, and collaboration state capsule format). Only Claude-specific additions are listed below.

When to Use

  • contribution-map initial draft needs claim-level stress testing.
  • evidence-matrix needs sufficiency audit (does evidence match claim strength?).
  • Routing decision (review vs empirical) needs adversarial challenge.
  • A specific claim needs deep falsification analysis.
  • Fallback: generic code collaboration (analysis, patch, review).

When NOT to Use

  • Broad literature expansion or adjacent-area scanning (use Gemini).
  • Generating candidate framing directions (use Gemini).
  • Generating ref.bib entries or verified citations.
  • Tasks requiring breadth exploration rather than depth analysis.

Claude-Specific Rules

  • Always run the bridge script with --help first if you are unsure of parameters.
  • Prefer file/line references; run the bridge with --cd set to the repo root. Use --add-dir when Claude needs access to additional directories.
  • For automation, prefer --SESSION_ID (resume). Session selectors are mutually exclusive: choose one of --SESSION_ID, --continue, or --session-id.
  • Ensure Claude Code is logged in before running headless commands (run claude and /login once if needed).
  • Streamed JSON requires --verbose; the bridge enables this automatically.

Model Selection

Claude Code supports model aliases, so you can use --model sonnet / --model opus instead of hard-coding versioned model IDs.

  • If you omit --model, Claude Code uses its configured default (typically from ~/.claude/settings.json, optionally overridden by .claude/settings.json and .claude/settings.local.json).
  • If you need strict reproducibility, pass a full model name via --model <full-name>.
  • For claim-stress-test and route-review-vs-empirical templates, prefer opus.
  • For evidence-sufficiency-audit, sonnet is sufficient.

Quick Start (shell-safe)

If your prompt contains Markdown backticks, use a heredoc; see references/shell-quoting.md.

bash
PROMPT="$(cat <<'EOF'
Review src/auth.py around login() and propose fixes.
OUTPUT: Unified Diff Patch ONLY.
EOF
)"
python3 .codex/skills/collaborating-with-claude/scripts/claude_bridge.py --cd "." --model sonnet --PROMPT "$PROMPT" --output-format stream-json

Output: JSON with success, SESSION_ID, agent_messages, and optional error / all_messages.

Multi-turn Sessions

bash
# Start a session
PROMPT="$(cat <<'EOF'
Analyze the bug in foo(). Keep it short.
EOF
)"
python3 .codex/skills/collaborating-with-claude/scripts/claude_bridge.py --cd "." --PROMPT "$PROMPT" --output-format stream-json

# Continue the same session
PROMPT="$(cat <<'EOF'
Now propose a minimal fix as Unified Diff Patch ONLY.
EOF
)"
python3 .codex/skills/collaborating-with-claude/scripts/claude_bridge.py --cd "." --SESSION_ID "<SESSION_ID>" --PROMPT "$PROMPT" --output-format stream-json

Prompting Patterns (Paper Mode)

Use assets/prompt-template.md as a starter when crafting --PROMPT. Paper-mode templates (top of the file):

  • claim-stress-test — adversarial review of contribution-map (use --model opus)
  • route-review-vs-empirical — routing recommendation with adversarial self-challenge (use --model opus)
  • evidence-sufficiency-audit — per-claim evidence sufficiency assessment (use --model sonnet)

Each template specifies its input sources, output artifact mappings, and constraints.

Verification

  • Cross-check both collaborators: use the check-collaborators skill
  • Smoke-test the bridge: python3 .codex/skills/collaborating-with-claude/scripts/claude_bridge.py --help
  • For session testing: run one prompt with --output-format stream-json and confirm JSON contains success: true and a SESSION_ID.

References

  • ../_shared/collaboration-base.md (shared rules)
  • assets/prompt-template.md (prompt patterns)
  • references/shell-quoting.md (shell quoting/backticks)

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 Collaborating With Claude AI skill do?

Use the Claude Code CLI as a depth-analysis co-pilot for paper-from-zero. Primary role: claim tree compression, logical hole detection, evidence sufficiency audit, and review/empirical routing judgment. Also supports generic code collaboration as fallback.

Why use Collaborating With Claude on TypingMind?

Because you install it once and use it with any model. Collaborating With Claude 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 Collaborating With Claude in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yunshenwuchuxun/latex-paper-skills/tree/main/.codex/skills/collaborating-with-claude. 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 Collaborating With Claude?

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 Collaborating With Claude?

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

Is the Collaborating With Claude 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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