Ship logo

Ship

OrganizationPopular
evo-hq
ship

Land the winning experiment from an evo run as a clean, mergeable change -- open a PR when the repo has a remote, otherwise merge into the working branch. Distills the best-scoring experiment down to the minimal diff that reproduces its behaviour, shaped for the qualities a maintainer merges on (scope discipline, test integrity, style adherence), then attaches an advisory mergeability report. Use when the user invokes /evo:ship, asks to land/merge/ship the best result, or wants to turn a finished optimization into a pull request.

Overview

Publisherevo-hq
Repositoryevo
Skill nameship
Stars
1.5K
Forks
110
Bundled files
Instructions only
LicenseApache-2.0
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 evo-hq on GitHub. Read the source before you install it.

Installation

Install the Ship 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.
  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/evo-hq/evo.git /tmp/evo
mkdir -p .claude/skills
cp -r /tmp/evo/plugins/evo/npm/skills/ship .claude/skills/ship
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ship 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 Ship 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 Ship 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.

Ship

Turn a finished evo run into a change a maintainer would merge.

The optimize loop leaves a tree of committed experiments. The winning worktree diff is not mergeable as-is: it carries debug prints, search-process churn, over-broad edits, and sometimes a test that was relaxed to clear a gate. Shipping is the step that re-derives the minimal clean change reproducing the winning behaviour, lands it the way the repo expects (PR or merge), and reports how mergeable it is.

Correctness is the floor, not the goal. The score says the behaviour works; this skill decides whether the diff is fit to merge.

Invocation

bash
/evo:ship            # ship the auto-selected winner
/evo:ship exp_0042   # ship a specific experiment instead

Stage 1 -- Select the winner

Pick the experiment to ship, then confirm it with the user before touching their tree.

bash
evo status    # current best valid score + counts
evo report    # top valid experiments table + score chart
  • The default winner is the highest-scoring valid result in the graph history, not the frontier. evo frontier is for choosing where to branch next; it can exclude an exhausted branch whose score is still the right thing to ship. An explicit exp_id argument overrides auto-selection.
  • A shippable winner must be valid: committed, or pruned with prune_kind=exhausted, with a commit and score, no gate_result === false, and no invalid-pruned ancestor. Never select discarded, failed, active, evaluated, legacy-pruned nodes with no prune_kind, prune_kind=invalid, or descendants of invalid-pruned nodes. If no valid candidate exists, stop and report why nothing is safe to ship.
  • Resolve the run's root (baseline) node, then show the cumulative change:
    bash
    evo diff <root_id> <winner_id>   # target-scoped cumulative diff, baseline -> winner
    For changes outside the benchmark target, diff the commits directly (git diff <baseline_commit> <winner_commit>); each node carries .commit.
  • Present a one-screen summary: winner id, score baseline -> winner (delta), the winning hypothesis, and a diffstat. Get a go before proceeding.

Stage 2 -- Distill to a mergeable change

Work on a fresh branch off the user's current HEAD, not in the experiment worktree. Re-derive the change so it stands on its own:

  • Scope restraint. Keep only the files and lines the behaviour needs. Drop experiment scaffolding, debug logging, commented-out attempts, and churn the search introduced and then abandoned. Smaller, local diffs merge; sprawl does not.
  • Test integrity. If the search weakened, skipped, or deleted a test to clear a gate, restore it. New behaviour that changes outputs needs a test that covers it. Never ship a green benchmark that rode on a loosened test -- call it out instead.
  • Mechanical cleanliness. Match the repo's formatter and linter. No stray whitespace, no reordered imports unless the repo does that.
  • Codebase adherence. Match surrounding naming, error handling, and structure. The diff should read like the file it lands in.

Then confirm the behaviour survived the distillation:

bash
evo run <winner_id> --check    # or the project's benchmark / test command

If the distilled change no longer reproduces the winning score, do not paper over it -- report the gap (which part of the experiment diff was load-bearing) and let the user decide. Best-effort means honest about what could not be cleaned up, not silently shipping the raw worktree.

Stage 3 -- Land

Detect how the repo expects changes to arrive:

bash
git remote -v
  • Remote present -> open a pull request. Commit the distilled change on its branch, push, and gh pr create with the mergeability report (Stage 4) as the body. Do not push or open the PR without the user's go.
  • No remote -> merge the distilled change into the user's working branch as a single clean commit. Do not force, do not rewrite existing history.

The landed commit message carries provenance: the winning experiment id, the score delta, and the one-line hypothesis. State what changed and why it is safe; do not narrate the search process.

Stage 4 -- Mergeability report (advisory)

Always produce the report. It never blocks the merge -- it tells the user, and a future reviewer, how mergeable the change is across the axes a maintainer judges on:

  • Technique -- what the change actually does to move the score, named concretely (the algorithm, data structure, or mechanism), not the search story. Distilled from the winning hypothesis: "replaced the O(n^2) dedup with a hash set", not "exp_0042 improved throughput". This is what a reviewer reads first.
  • Behavioural correctness -- score baseline -> shipped (delta); benchmark status after distillation.
  • Regression safety -- full test suite result on the distilled change.
  • Scope -- files touched, diff size, whether the change stays local.
  • Test correctness -- explicit yes/no on whether any test was modified, weakened, or removed, with detail; whether new behaviour is covered.
  • Mechanical cleanliness -- formatter / linter status.
  • Codebase adherence -- a note on style/convention fit.

Lead with a plain-language summary: what changed and why it is safe to merge. On a remote repo this is the PR body. With no remote, print it and save it alongside the run so the user can paste it into a review later.

Guardrails (firm)

Everything above is method you can adapt to the repo. These are not:

  • Never weaken, skip, or delete a test to make the change land. If the experiment did, restore it and report it.
  • Never ship invalid-pruned, legacy-pruned, discarded, failed, active, evaluated, gate-failed, or invalid-lineage nodes. Only exhausted pruned nodes remain normal ship candidates.
  • Never push or open a PR without the user's explicit go.
  • Never rewrite or force-overwrite existing history on the user's branch.
  • Never ship the raw experiment worktree diff as-is when distillation failed -- report the gap instead.

Frequently asked questions

What does the Ship AI skill do?

Land the winning experiment from an evo run as a clean, mergeable change -- open a PR when the repo has a remote, otherwise merge into the working branch. Distills the best-scoring experiment down to the minimal diff that reproduces its behaviour, shaped for the qualities a maintainer merges on (scope discipline, test integrity, style adherence), then attaches an advisory mergeability report. Use when the user invokes /evo:ship, asks to land/merge/ship the best result, or wants to turn a finished optimization into a pull request.

Why use Ship on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/evo-hq/evo/tree/main/plugins/evo/npm/skills/ship. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ship?

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 Ship?

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

Is the Ship AI skill free?

Yes. It is published on GitHub by evo-hq under the Apache-2.0 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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