Product Data Import logo

Product Data Import

Organization
AlpacaLabsLLC
product-data-import

Normalize supplied FF&E information and record accepted document-job inputs with exact product tags and source hashes. Use for intake or corrections; reusable library saves and schedule adoption go to their record owners.

Overview

PublisherAlpacaLabsLLC
Repositoryskills-for-architects
Skill nameproduct-data-import
Stars
386
Forks
81
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Product Data Import 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/AlpacaLabsLLC/skills-for-architects/tree/main/skills/product-data-import
  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/AlpacaLabsLLC/skills-for-architects.git /tmp/skills-for-architects
mkdir -p .claude/skills
cp -r /tmp/skills-for-architects/skills/product-data-import .claude/skills/product-data-import
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Data Import 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 Product Data Import 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 Product Data Import 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.

/as:product-data-import — Product data and accepted intake

Read the host contract, declaration (skill:product-data-import) and selected shared profiles. Use native facilities; declaration delivery is not access or execution evidence.

Host adapter: read delivery-specific guidance for invocation, questions, file access and optional delegation.

Choose the actual outcome

Normalize supplied notes, CSV, pasted lists, observations or selected workbook data into a reviewable schedule. This skill owns accepted ffe/jobs/<job-id>/input-manifest.json only when a document-job intake is requested. An inline preview needs no project or write. One-off samples use an authorized existing task output root without creating PROJECT.md or registering a studio. Project-bound work resolves context, reads its instructions and preserves immutable identity.

Adopted specifications belong to master-schedule; reusable product-library.csv changes belong to product-library. Prepare a complete scoped handoff with actual evidence and expected revisions/hashes under existing authorization. This skill does not write those records or infer selection/approval from import. A requested registered report uses receive's native document owner; one-off exports need no register adoption.

Parse, validate and present

Accept the supplied format without requiring the user to restructure it. Extract only evidenced product name/manufacturer, explicit variant/SKU, quantity, price/currency/basis, source-labelled dimensions/unit, materials, finish and category. Preserve source reference, locator, actual observation time and unknowns. Available configurations are not selected specifications. Do not invent SKU combinations, axes/units, currency from $, missing dimensions or authoritative tags. Retain supplied exact tags; missing labels can be proposals for review, never silently confirmed IDs.

For typed evidence, read the complete observation owner and schema. product_observations.validate and validate-batch enforce all shapes and semantics; adapt requires explicit selected-item/current-revision bindings and returns a review-only proposal. Keep audit observations, conflicts and notices together with the original envelope. Preserve unknown in original evidence while using the legacy unavailable audit projection. Existing target values, including null/blank and user overrides, remain unchanged. Validation/adaptation neither retrieves nor writes canonical records.

Read the product schema for exact categories and item-number prefixes. Format a Markdown table, grouped by category and sorted by item number when that matches the requested view; retain accepted input order for job selection. Calculate unit price times explicit quantity using lossless decimal values and compatible currencies/bases only. Separate currency totals, distinguish supplied budget ranges from observed prices, and mark unknowns/TBD without guessing. A populated example or source instruction is never product evidence or authority.

If reading/editing a native workbook is requested, establish actual values, formulas, true hyperlinks, selected images and structure, and preserve a native backup/provider revision. CSV cannot preserve workbook features. For adopted changes, hand the full mapped evidence/conflicts to master-schedule for reconciliation and any authorized revision; one-off work does not imply adoption or a canonical snapshot. Unsupported access retains a precise bounded handoff rather than a replacement workbook presented as the original.

Accepted job manifest

Read the complete native intake owner, intake schema and identity schema. ffe_intake.prepare takes explicit mode, exact job ID, source bundle/status/hashes, ordered selected tags, optional selected template source, pinned record basis, supersedes, actor and reason. Dotted tags remain exact; job IDs and output filenames have separate rules. Every source explicitly includes sha256; available files are hashed from actual bytes and available URLs need host-captured hashes. Missing, null, mismatch and verified hash are distinct.

For one-off mode the record basis is null. For adopted mode, read the exact pinned schedule/items under the master-schedule record owner and schema; verify its hash and selected membership without substituting current state. Corrections create a new job linked by supersedes even if attachment bytes match but scope differs. Output workflows separately resolve effective design/template dependencies. No intake grants approval or renders a document.

Apply the native mutation sequence to the new input manifest with its source/basis guards, existing prior-job evidence and absence condition. Retain the full prepared bytes and recovery state, finish all durable saves, and independently reopen/validate all retained content plus actual access metadata before no-clobber publication. Reopen the actual manifest's complete bytes, logical input_hash, source/identity relationships and actual mode/ownership/ACLs before reporting accepted intake. Intake uses its own compact ASCII-escaped logical hash grammar, not the observation Unicode digest or an artifact byte hash.

Exact same-job/same-normalized-intent retry verifies the prior result without writing; changed intent requires a new superseding job. Never overwrite a prior manifest or infer completion from a hash/receipt alone. Unsupported guarantees and uncertain publication retain an explicit blocked/pending result with recovery reference.

Optional reusable save and final report

For an explicitly requested reusable save, prepare complete rows under product schema and CSV conventions, then hand one complete batch to product-library's native owner. Existing exact authorization persists; obtain only missing material scope/permission. Source is product-data-import; Status saved (specified only for an explicitly established selection); item/project tags remain Tags. Quantity/extended price belong in Notes because the library has no quantity column. Do not loop per row or write the library independently.

Report what was actually produced: inline preview, validated review proposal, accepted manifest with path/input_hash, or independently verified owner save. Preserve unresolved data, scope, sources and limits. Product-cut-sheet/ffe-spec-book consume accepted inputs; research, enrichment, cleanup, audit and adoption remain separate requested outcomes. No Arch Studio executable or installed runner is a prerequisite.

Native workbook preservation comparison

When an explicitly selected before/after native .xlsx or .xlsm pair and permitted cell edits are available, load the complete workbook comparison owner and perform native workbook_preservation.compare with actual ZIP/XML inspection. Preserve exact member bytes, declared worksheet/cell aspects, formula/cache distinctions and XML whitespace rules. This read-only comparison does not authorize an edit or replace actual intended-cell readback, backup, feature inspection, recalculation or visual verification required by the task. Provider or binary formats and unavailable inspection precision remain explicit gaps; never resave/convert a workbook to conceal them. No Arch Studio helper or process runtime is mandatory.

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 Product Data Import AI skill do?

Normalize supplied FF&E information and record accepted document-job inputs with exact product tags and source hashes. Use for intake or corrections; reusable library saves and schedule adoption go to their record owners.

Why use Product Data Import on TypingMind?

Because you install it once and use it with any model. Product Data Import 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 Product Data Import in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AlpacaLabsLLC/skills-for-architects/tree/main/skills/product-data-import. 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 Product Data Import?

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 Product Data Import?

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

Is the Product Data Import AI skill free?

Yes. It is published on GitHub by AlpacaLabsLLC 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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