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Partner Skill

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LearnPrompt
partner-skill

搭子.skill / Partner — two-direction cost-split workflow between Claude Code and Codex. Direction A (Codex-driven): Codex orchestrates and implements; Claude Code plans (/goal), polishes UI/interaction, and runs final /codex:review in one reused session. Direction B (Claude-driven): Claude plans and splits the work, runs the idea-king adversarial gate, delegates to Codex background jobs, monitors, and full-reviews before accepting. Use on "搭子" / "双向搭子" / "搭子,恢复" (resume from .partner/), "搭子,配置" (first-run setup wizard), "搭子,试跑" (identity tryout report), 分工给 codex / 让 codex 做 / codex 后台跑 / Claude 计划 Codex 实现 / 让 Claude skip 做完, or any request to split coding work between Claude Code and Codex to save quota. Not for ordinary code review without Claude Code; do not trigger on the bare English word "partner" in unrelated contexts.

Overview

PublisherLearnPrompt
Repositorypartner-skill
Skill namepartner-skill
Stars
67
Forks
8
Bundled files
63
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.

  • 63 bundled files

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

  • Open source

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

Installation

Install the Partner Skill 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/LearnPrompt/partner-skill/tree/main
  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/LearnPrompt/partner-skill.git \
  .claude/skills/partner-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Partner Skill 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 Partner Skill 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 Partner Skill 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.

搭子.skill (Partner)

我的 Claude Code 和 Codex 天下第一好。

Overview

Use this skill to run a two-agent coding workflow: Claude Code is the high-value planning, polish, and review agent; Codex is the outer orchestrator and main implementer. Keep Claude Code usage focused because it may be billed through API.

Prefer one long-lived Claude Code session for small and medium tasks: ask Claude for the plan, let Codex implement, then return the diff summary and key files to the same Claude session for UI/interaction polish and final /codex:review. This avoids paying Claude to rebuild the same project context and gives Claude enough continuity to improve the work.

Partner is not a delegation excuse. The user remains the owner, Codex remains accountable for repository evidence, and Claude Code is treated as a high-value collaborator whose output must be verified.

Host Detection

Partner is one Core (this file) plus one host adapter. Decide the host once at the start and say which direction you are in:

  • The host is the runtime that actually loaded this SKILL.md. Self-identification wins: a prompt that merely mentions the other agent ("让 codex 做", "ask Claude") never switches your identity.
  • The host=codex|claude|generic marker in .install-meta (written by install.sh) is a tiebreaker only, for copies whose load path is ambiguous. It never overrides self-identification.
  • A generic install (~/.agents) runs Core-only: state up front that adapter primitives (delegate-codex.sh background jobs, Claude session monitoring) are unavailable instead of pretending they work.
  • Probing the peer CLI (codex --version, check-claude-cli.sh) only tells you what the other side can do; it never decides which adapter you load.

Then load the matching adapter:

  • Direction A — Codex-driven: this file is loaded by Codex; Codex orchestrates and Claude Code is the high-value planning/polish/review agent. Read references/codex-driven.md and follow its Default Flow, Session Strategy, and Permission Policy.
  • Direction B — Claude-driven: this file is loaded inside Claude Code and the user asks to delegate work to Codex ("双向搭子", "分工给 codex", "让 codex 做", "codex 后台跑"). Claude plans and splits the work, delegates to Codex via bash "$PARTNER_DIR/scripts/delegate-codex.sh" background jobs, monitors with a loop, and full-reviews the result before accepting. Read references/claude-driven.md and follow its five phases; the shared prompting rules live in references/fable5-principles.md and the wrap-up memory rules in references/memory-protocol.md.

Both directions end with the same Partner Session Receipt; direction records which flow ran.

Configuration

On "搭子,配置", or when a Partner flow needs an identity with no configuration yet, run the local setup UI in references/setup.md with python3 "$PARTNER_DIR/scripts/partner-setup-ui.py" --host <host> --repo <repo>. Do not collect the matrix through repeated chat questions when a browser is available. The single page shows every backend, concrete model, and effort, then delegates every preview/write to partner-setup.py (preview → atomic apply → automatic smoke test). It uses beginner-safe project defaults instead of asking scope/Git/routing questions in chat; the terminal engine remains available for explicit advanced overrides. Three identities — deep_reasoner, fast_worker, and arbiter (the blind second solver for contentious calls) — each carry their own backend (which CLI executes: claude or codex), model, and effort, freely mixed across vendors. Their values live only in .partner/config.toml (project) or ~/.config/partner/config.toml (global) — schema in docs/config-schema.md; never duplicate them into prompts or docs. On "搭子,试跑", run the identity tryout in references/tryout.md: each identity executes one micro-task and the report proves they are live on the configured models.

Tool Location

The helper scripts referenced below live in this skill's install directory (the directory containing this SKILL.md), not in the target repo. Resolve it once as $PARTNER_DIR — you know it from wherever this file was loaded; otherwise probe ~/.codex/skills/partner-skill, ~/.claude/skills/partner-skill, ~/.agents/skills/partner-skill, or the local clone. All scripts accept running from any cwd; repo-dependent ones take --repo.

Routing Rules

  • Route to Claude Code: architecture planning, implementation strategy, UI/interaction critique, final Codex Review, difficult product tradeoffs.
  • Route to Codex: scaffolding, implementation, long-context code edits, tests, build fixes, repository inspection, monitoring, summaries.
  • Route back to the same Claude Code session when UI quality matters or the first implementation passes technically but still needs product polish.
  • Keep Kimi Work/Kimi Code Goal separate from Claude Code /goal; prior "Goal mode" context may refer to Kimi, not Claude.

Bounded Claude Planning

For repository-heavy planning through a Claude-backed deep_reasoner, especially at xhigh, use references/bounded-planning.md and scripts/run-claude-plan.py. The outer host prepares the required 24,000-character-max evidence packet; Claude runs in safe mode with no tools or subagents, explicit wall/valid-event idle limits, bounded stream/output/log sizes, process-group termination, and a Claude-CLI-enforced API budget. Model and effort come only from .partner/config.toml. Failure preserves a checkpoint, sanitized events, cost metadata, and a same-session recovery command; Partner never substitutes another model silently.

Validation Gate

Use the Darwin-style ratchet in references/darwin-ratchet.md when improving this workflow or applying it to a substantial task:

  • Change one workflow dimension at a time: planning, implementation, UI polish, review, monitoring, permissions, or reporting.
  • Run test prompts or a real miniloop before calling an improvement better.
  • Do not let the same agent be the only maker and only judge for high-risk changes.
  • Keep the change only when repo evidence improves. If it regresses, use a reviewable revert, not git reset --hard.
  • Stop when another prompt loop produces low signal or <1 point expected improvement.

Monitoring

For active Claude Code sessions, read references/monitoring.md. Prefer five signals instead of trusting chat text alone:

  1. PTY output from the running Claude Code session.
  2. claude agents --json --cwd <repo> session status.
  3. Claude JSONL transcript structure, without dumping full message bodies by default.
  4. Optional task files under ~/.claude/tasks/<sessionId>/.
  5. Repo evidence: git status --short, git diff --stat, and relevant test/build checks.

Signals 2-4 depend on Claude Code CLI internals that can drift between versions. Probe them with bash "$PARTNER_DIR/scripts/check-claude-cli.sh" and report the resulting monitoring_level (full, degraded, or none) in the receipt. Never claim a signal the probe says is unavailable. When an anomaly occurs, follow the fixed recovery path in references/failure-playbook.md.

Output Contract

When reporting back to the user, include:

  • Current phase: planning, Codex implementation, Claude polish, review, or final fix.
  • Claude Code session id when one exists.
  • Files changed and checks run.
  • Review findings fixed or still open.
  • Whether the work is ready to commit; do not commit by default.
  • Any monitoring anomaly such as idle session, permission wait, empty review output, no diff, or failed check.
  • A Partner Session Receipt for non-trivial Claude Code workflows:
text
[Partner session receipt]
phase: <planning | codex implementation | claude polish | review | final fix>
claude_session: <sessionId or none>
claude_session_reused: <yes | no | n/a>
new_claude_p_sessions: <0 | count | unknown>
codex_passes: <number of implementation/fix passes>
checks: <commands run or not run>
anomalies: <none | permission wait | idle | empty review | failed check | other>
monitoring_level: <full | degraded | none | unknown>
direction: <codex-driven | claude-driven>
codex_jobs: <0 | count>
host: <claude_code | codex | generic>
scope: <project | global | n/a>
config_source: <session | project | global | default | n/a>
roles_used: <none | JSON array of {role, host, model, effort, verified}>
receipt_schema_version: 2

Generate the receipt with python3 "$PARTNER_DIR/scripts/make-receipt.py" — it auto-fills monitoring_level from the probe and refuses to emit an invalid receipt. Get new_claude_p_sessions from bash "$PARTNER_DIR/scripts/session-snapshot.sh" diff so the count is computed from transcript evidence. In Direction B, set direction: claude-driven and codex_jobs to the number of delegate-codex.sh jobs (including fix rounds); in Direction A they are codex-driven and 0 unless background jobs were used. A written receipt can be re-checked any time with validate-receipt.py against docs/receipt-schema.json.

host is the runtime that loaded this file (see Host Detection above); scope and config_source come straight from partner-setup.py --status or a partner-config.py resolve call (n/a when the run touched no configured role). roles_used lists every role actually invoked this run, each entry's verified taken from the config's verified field, not guessed — an unconfigured or unverified role still gets an entry with verified: false, it is never omitted to make the receipt look cleaner. receipt_schema_version is always 2; a receipt missing the four fields above is a schema v1 receipt from before this contract and will fail validate-receipt.py, which is the intended signal to regenerate it with the current make-receipt.py.

Do not fabricate token savings. When exact token telemetry is unavailable, report verifiable behavior instead: same Claude Code session reused, no fresh claude -p session, bounded handoff used, checks passed.

Bundled files

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

and 3 more files.

Frequently asked questions

What does the Partner Skill AI skill do?

搭子.skill / Partner — two-direction cost-split workflow between Claude Code and Codex. Direction A (Codex-driven): Codex orchestrates and implements; Claude Code plans (/goal), polishes UI/interaction, and runs final /codex:review in one reused session. Direction B (Claude-driven): Claude plans and splits the work, runs the idea-king adversarial gate, delegates to Codex background jobs, monitors, and full-reviews before accepting. Use on "搭子" / "双向搭子" / "搭子,恢复" (resume from .partner/), "搭子,配置" (first-run setup wizard), "搭子,试跑" (identity tryout report), 分工给 codex / 让 codex 做 / codex 后台跑 / Cla...

Why use Partner Skill on TypingMind?

Because you install it once and use it with any model. Partner Skill 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 Partner Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LearnPrompt/partner-skill/tree/main. 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 Partner Skill?

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 Partner Skill?

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

Is the Partner Skill AI skill free?

Yes. It is published on GitHub by LearnPrompt 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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