ship-gate
Skill0 starsChanged 15 days agoMITView on GitHub
Reviews an AI agent for production readiness across evaluation, cost, observability, guardrails, human oversight, reliability, and governance. Use when someone is preparing an agent for production or a go-live, asking whether an agent is ready to ship, reviewing an agent's design, prompt, tools, or deployment, or hardening a working prototype into a dependable system. Produces an evidence-based scorecard and a prioritized list of the gaps to close.
11 other projects use a version of this.
Adopted by
Most recently taken up by
- darkweb19/skillstheir own version, first seen Nov 8, 2026
- kazemcodes/skill-treetheir own version, first seen Nov 8, 2026
- tderoussel/agent-skillstheir own version, first seen Oct 20, 2026
- edenbuilds/agentlooptheir own version, first seen Oct 13, 2026
- sunnybhara/claude-startertheir own version, first seen Aug 14, 2026
- Hassaan146/claude-skillstheir own version, first seen Jul 25, 2026
Other versions11 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 11
- unlearndev/skills★ 101same name
- Hassaan146/claude-skills★ 18same name
- csiddhant796-blip/claude-skills-collection★ 2same name
- Hussain-Elfalah/Agentic-Skills★ 2same name
- kazemcodes/skill-tree★ 1same name
- mahiunfiltered/claude-code-skills★ 1same name
- orkait/hyperstack★ 1same name
- tderoussel/agent-skills★ 1same name
- darkweb19/skills★ 0same name
- edenbuilds/agentloop★ 0same name
1 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository2 other files it gives its agents.
- design-gateSkill
- incident-to-evalSkill
The file
We split this skill into the rules it gives an agent
7 rules. 0 are common, 5 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 11 edited versions we read keep these.
5
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 15
Run this when the user is:
- Distinctive: line 22
Do not run it against a throwaway prototype the user has said will never reach production.
- Distinctive: line 30
Before assessing anything, establish the basics, either by asking the user or by reading the material they provide:
- Distinctive: line 37
Keep this short. You are calibrating how strict the rest of the review should be.
- Distinctive: line 43
Look for evidence. A claim that evals exist is not evidence.
- Very specific: line 47
Use references/failure-modes.md for the specific production failures each dimension is meant to catch.
- Very specific: line 59
Use the format in assets/review-template.md.
“Common” is measured: kept by at least 6 in 10 of the 11 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Agent production readiness review
A demo that works in a keynote is not an agent that works at 2am. This skill runs the review that tells the difference. It scores an agent across seven dimensions and returns the gaps that stand between a promising prototype and a dependable production system.
It is the ship-time gate. Its sibling, the agent-or-workflow skill, is the design-time gate. This one decides whether an agent is ready to run; that one decides what to build in the first place.
When to run this
Run this when the user is:
- Preparing an agent for production or a go-live.
- Asking whether an agent is ready, or good enough to ship.
- Reviewing an agent's design, prompt, tools, or deployment.
- Turning a working prototype into something they can operate.
Do not run it against a throwaway prototype the user has said will never reach production. Ask if you are unsure.
How to run the review
Work through four phases in order. Do not skip to a score.
Phase 1: Understand the agent
Before assessing anything, establish the basics, either by asking the user or by reading the material they provide:
- What the agent does, and for whom.
- Where it runs, and what it is connected to: tools, data, systems of record.
- What actions it can take, and which of those are hard to reverse.
- What "wrong" looks like, and who is harmed when it happens.
Keep this short. You are calibrating how strict the rest of the review should be. An agent that can only read is judged differently from one that can move money.
Phase 2: Assess each dimension
Assess all seven dimensions in references/rubric.md. For each check:
- Look for evidence. A claim that evals exist is not evidence; the eval set is. If you cannot find evidence, treat the item as not met, and say so.
- Record what you saw, not just pass or fail.
- Flag anything worse than not-met: a guardrail that exists but is bypassable, an eval set that only covers happy paths, an audit log nobody can read.
Use references/failure-modes.md for the specific production failures each dimension is meant to catch.
Phase 3: Score
Report a count per dimension and a total out of 28. Name the strongest dimension and the weakest. Do not round up. A system that is strong in six dimensions and absent in the seventh is not nearly ready if the seventh is the one that gets someone hurt.
Phase 4: Recommend
The score is the start of the conversation, not the end. Return a short, prioritized list of the gaps that matter most, ordered by risk rather than by how easy they are to fix. For each gap: what is missing, why it matters for this specific agent, and the smallest change that would close it.
Output
Use the format in assets/review-template.md. Lead with the gaps, not the score. A filled example is in assets/example-review.md.
Principles
- Production over demos. Judge the agent by how it behaves on its worst day, not its best.
- Evidence over assertion. If it is not shown, it is not done.
- The gaps are the work. A low score is not a failure; it is a plan.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

