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Shipping Artifacts

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phuryn
shipping-artifacts

The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.

Overview

Publisherphuryn
Repositorypm-skills
Skill nameshipping-artifacts
Stars
26.4K
Forks
2.8K
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Shipping Artifacts 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/phuryn/pm-skills.git /tmp/pm-skills
mkdir -p .claude/skills
cp -r /tmp/pm-skills/pm-ai-shipping/skills/shipping-artifacts .claude/skills/shipping-artifacts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Shipping Artifacts 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 Shipping Artifacts 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 Shipping Artifacts 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.

Shipping Artifacts: The Docs That Make AI-Built Code Reviewable

Purpose

AI agents write code fast, but they leave no durable record of intent — what the system is supposed to do, who is allowed to do what, where the secrets live, which rules are actually verified. Without that record, no human (and no auditing agent) can tell whether the code is safe to ship. This skill defines the small set of documents that restore reviewability.

These docs live in documentation/ at the repo root and are written for two readers: a human reviewer and the next AI coding agent. They are the intended-state half of every later audit — a security or performance review is only as good as the intent it can compare the code against.

How the set is organized

The set is not a fixed list — it is a small core plus conditional docs you add only when the capability exists.

  • Core docs — every reviewable app has these surfaces, so always produce them.
  • Conditional docs — include one only if the app actually has that capability. If it doesn't, write a single line in architecture.md ("No scheduled work — no cron.md.") rather than inventing an empty document. Reviewability comes from an honest map, and "we don't do X" is part of the map.
  • Most docs are reverse-engineered from code by /document-app. The one exception is tests.md, which is derived from the other docs by /derive-tests — it is the verification map, not a description of a subsystem.

Be brutally honest about the current state without being paranoid. The job is an accurate map, not a clean bill of health. Each doc is short, table-and-bullet heavy, and skips generic theory.

Core documents

Each entry: file · one-line purpose · what it must capture · how a reviewer uses it.

  1. architecture.md — what the system is and how it hangs together.

    • Must capture: product overview + key assumptions; tech stack; how auth/sessions/claims flow end to end; the trust boundaries (e.g. service-role vs. client); a short Known risks / assumptions list (each entry backed by where it shows up in the code, not a generic checklist); a "Related Documents" index of every other doc produced.
    • Reviewer use: the root document — everything else is cross-referenced from here.
  2. flows.md — the journeys where permissions and side effects are actually exercised.

    • Must capture: each load-bearing flow as actor + precondition + success outcome; the step-by-step sequence across UI → server → data → jobs → providers → agents; the authz check at each protected step (which claim/role/scope, on which resource, and the expected deny case); the trust-boundary crossings (browser→server, server→provider, job→app, agent→tool, webhook→app); the state changes and side effects each step causes (writes, emails queued, jobs triggered, outbound calls).
    • Reviewer use: the runtime view a static permissions.md matrix can't show — where and in what order authorization is enforced, and where it can be skipped.
    • Anti-PRD rule: a flow that doesn't touch permissions, data integrity, external side effects, money, privacy, or operational safety does not belong here. This is a security/operations map, not a feature spec.
  3. permissions.md — who is allowed to do what.

    • Must capture: roles/claims; where scope is derived (token vs. DB); a resource × operation × role matrix; which tables have row-level security and which rely on code-enforced checks.
    • Reviewer use: the baseline an access-control audit compares the code against. flows.md shows it in motion; this is the static reference.
  4. variables.md — configuration and secrets, mapped to risk.

    • Must capture: a table of Name · used-by · scope (server/client) · source · rotation · risk; explicit confirmation that no secret is bundled client-side; a pre-go-live checklist.
    • Reviewer use: the secrets/PII-leak surface and the rotation plan during incident response.
  5. tests.md — the verification map: which documented rules are actually checked, which are only proposed, and which are checked by nothing.

    • Must capture, in three clearly separated sections so the map can't read falsely green:
      • Existing coverage — tests that are in the repo today, each tied to the rule it pins (so the map reflects reality, not a wish-list).
      • Proposed tests — recommended cases not yet written, marked by test type (automated unit/integration · guarded live · manual review).
      • Gaps — documented rules with no verification at all, ranked by what crossing them exposes.
    • Each row carries: use-case → rule → expected behavior (including the deny/negative case) → evidence source (doc + code) → status (existing / proposed / none). It also notes which checks are CI-required and gate merges to main.
    • Reviewer use: the operational form of "documented == implemented" — it shows whether each rule the other docs claim is actually pinned by a test today, only proposed, or unverified.
    • Produced by /derive-tests (not /document-app), because it is derived from the other docs and the existing test suite rather than read off a subsystem.

Conditional documents (include only when the capability exists)

  1. emails.md — every notification the system sends. Include only if the app sends transactional or automated email.

    • Must capture: the queue → processor → provider path; templates and the variables they accept; retry/backoff behavior; where to look when a send fails.
    • Reviewer use: spotting unvalidated template inputs and PII exposure boundaries.
  2. cron.md — all scheduled work and how to operate it safely. Include only if scheduled or background jobs exist.

    • Must capture: an inventory table (job → schedule → function → secrets → limits → retry); how each job stays idempotent; how internal calls authenticate; where to see last runs.
    • Reviewer use: finding forgeable triggers and unbounded background jobs.
  3. seo.md — how a single-page app handles SEO and social previews. Include only if there are public/indexable or bot-facing routes.

    • Must capture: the preview approach (static meta / prerender / edge HTML); a route → needs-SEO → public-data-only table; how dynamic metadata is sanitized; bot-vs-human routing.
    • Reviewer use: catching public-data-only violations and metadata injection on bot routes.
  4. automation.md — embedded agents and other automation paths. Include only if the app embeds AI agents, LLM workflows, tool-calling, webhooks, or external automation.

    • Must capture, per automation/agent: trigger + owner + whether it runs automatically or only after approval; the inputs it may read and the exact tools/APIs it may call (the tool surface is itself a hard guardrail); where steering lives (the prompt) vs. the non-prompt hard guardrails; the output contract back to the app (schema, validation, failure handling); app-owned side effects vs. agent-owned suggestions; and the controls — approval gates, audit/timeline logging, rate limits, retries, kill switch.
    • Reviewer use: makes hidden automation paths visible and draws the line between what an agent proposes and what the app enforces — the highest-risk surface in modern AI-built apps.

Notes

  • Each produced doc adds a reference to itself in architecture.md under a "Related Documents" section, so the set stays discoverable.
  • Skip any conditional document that doesn't apply, and say so in one line rather than inventing content.
  • Keep examples and finished templates out of these docs — they describe this system, not the general method.
  • The agent operating-context file (CLAUDE.md / AGENTS.md) is a different artifact — instructions derived from these docs, not system documentation. It is produced at the handoff step by /ship-check, not here.
  • tests.md is produced by /derive-tests; the rest are produced by /document-app.
  • Do not include an "updated date" line; the file's history is the source of truth.

Frequently asked questions

What does the Shipping Artifacts AI skill do?

The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.

Why use Shipping Artifacts on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/phuryn/pm-skills/tree/main/pm-ai-shipping/skills/shipping-artifacts. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Shipping Artifacts?

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 Shipping Artifacts?

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

Is the Shipping Artifacts AI skill free?

Yes. It is published on GitHub by phuryn 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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