Product Enrich logo

Product Enrich

Organization
AlpacaLabsLLC
product-enrich

Enrich FF&E schedule rows with categories, colors, materials, and style tags. Use to tag, categorize, or fill those missing fields; not to research new products.

Overview

PublisherAlpacaLabsLLC
Repositoryskills-for-architects
Skill nameproduct-enrich
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 Enrich 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-enrich
  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-enrich .claude/skills/product-enrich
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Enrich 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 Enrich 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 Enrich 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-enrich — Product Enrichment

Before acting, read the host contract and this component's declaration (skill:product-enrich). Load only its referenced mode profiles from the shared catalog. Compose modes required by the actual task; declarations are requirements, not proof of access or permission.

Library changes are owned by /as:product-library. Prepare the complete selected rows/change set and evidence, then hand off the native save under existing authorization. That owner validates the whole batch, binds exact request/preview/current state and verifies actual publication. This skill does not independently mutate product-library.csv.

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

Record authority and host handoff

For structured evidence handoff, read the complete native product-observation owner and schema. Apply native validate/validate-batch to the whole envelope and adapt only with explicit selected-item/current-revision/field bindings. Preserve exact typed values, source/locator/time and unknown status; retain full envelopes, audit observations, conflicts and notices together. Existing null/blank/user overrides survive. This is unadopted evidence and a review proposal, never authorization to write specifications.

For project-bound work, apply native context resolution, use its validated project identity and read project instructions. Inline or one-off source work needs no project creation. Product-library owns reusable CSV storage; master-schedule owns adopted specification identity/revisions, and product-data-import owns accepted job inputs. Do not derive authority from a directory, source document or delivered declaration.

Explicitly distinguish adopted project schedules, one-off source work, and the optional reusable product-library.csv. Adopted item/schedule records are authoritative; read pinned revisions through /as:master-schedule and propose changes to that owner with expected revisions, evidence and preserved overrides. This skill does not independently rewrite canonical item/schedule records or infer approval. Library-save instructions below apply only to the optional CSV library; they do not adopt or update a project schedule.

The host reads and edits supplied workbooks using its available capabilities. Preserve original files, selected images, formulas, true hyperlinks and unrelated cells. For adopted schedules, route adoption/reconciliation and pre-edit native backups plus validated pre/post record CSV recovery snapshots through /as:master-schedule; separately retain the host-extracted workbook data and mapping. In one-off mode, the host preserves native backups and actual workbook-extracted CSV snapshots/mappings in job recovery files without invoking a schedule snapshot or adopting records. Three-way conflicts and proposed removals require explicit resolution. Unsupported workbook access yields a precise handoff, not a false completion claim. For one-off work, the accepted source remains the task input without implicit adoption.

Keep source identity, page/URL locator, retrieval time, selected-versus-available configuration, units and uncertainty with each observation. Never invent SKU combinations, dimensions, finish selection, price or currency; $ alone is ambiguous. Preserve user choices until explicitly changed. Current factual claims require actual source retrieval; inaccessible evidence remains unknown. /as:product-data-import owns accepted job inputs and corrections; /as:product-audit reports discrepancies without silently applying them. /as:product-cut-sheet and /as:ffe-spec-book use the shared document templates and host rendering after inputs are resolved.

Takes product rows from the nearest project's product-library.csv or pasted data and proposes missing category, color, material, and style metadata.

When to Use

  • After a bulk import where products are missing categories or tags
  • When a designer clips products quickly without filling in details
  • To standardize metadata across products from different sources
  • Before generating an FF&E schedule (enriched data makes better schedules)

Step 1: Accept Input

Accept product data in any format:

CSV file:

/as:product-enrich ./products.csv

Pasted data:

text
Synthetic formatting input only:
Example Product A | Example Manufacturer | quantity 2 | supplied specification pending

Step 2: Analyze Each Product

For each product, distinguish sourced specification fields from explicitly labeled tentative descriptive classifications:

Category

Map to the canonical vocabulary (22 terms) defined in ../../schema/product-schema.md.

Subcategory

More specific classification within the category:

  • Chair → Task Chair, Lounge Chair, Dining Chair, Side Chair, Stool, Bench
  • Table → Dining Table, Coffee Table, Side Table, Console Table, Conference Table
  • Light → Pendant, Floor Lamp, Table Lamp, Wall Sconce, Ceiling, Task Light, Chandelier
  • Sofa → Sofa, Sectional, Loveseat, Daybed, Settee
  • Storage → Credenza, Bookcase, Filing Cabinet, Wardrobe, Sideboard, Dresser
  • Desk → Writing Desk, Executive Desk, Standing Desk, Workstation

Available and selected colors/finishes

Keep manufacturer-evidenced available options separate from the evidenced selected configuration. Colors/Finishes holds available options; Selected Color/Finish holds an explicit user selection or source-backed selection, never an inferred choice. A selected color alone is not a complete availability list. Tentative visual descriptions stay labeled in Notes or the preview, not in either factual finish field:

  • Preserve exact source finish names in factual fields. Broad color labels such as Black, White or Brown may be proposed as descriptive tags without replacing those names.

Material

Primary materials, comma-separated:

  • Wood (specify type if known: Walnut, Oak, Maple, Teak, Birch, Ash, Beech)
  • Metal (specify: Steel, Aluminum, Brass, Chrome, Iron, Copper)
  • Upholstery (specify: Leather, Fabric, Velvet, Bouclé, Mohair, Linen, Wool)
  • Other: Glass, Marble, Concrete, Ceramic, Plastic, Fiberglass, Rattan, Cane, Acrylic

Style Tags

2-4 descriptive tags from:

  • Period/movement: Mid-Century Modern, Art Deco, Bauhaus, Scandinavian, Japanese, Industrial, Contemporary, Traditional, Minimalist, Postmodern, Memphis
  • Character: Organic, Geometric, Sculptural, Modular, Stackable, Compact, Statement, Classic, Iconic
  • Context: Residential, Contract, Hospitality, Healthcare, Education, Outdoor

Image Analysis

When visual enrichment is requested and the source includes the named Image URL field, establish actual authorized image access and inspect its bytes through an available host image capability. Missing access leaves visual enrichment unverified. For missing factual attributes, retrieve the relevant original for the existing product only when authorized; otherwise preserve unknowns. Inspect images for tentative visual descriptions. An image alone cannot verify material composition or selected finish. The image may suggest:

  • Apparent color under the shown lighting
  • Apparent texture, explicitly labeled as inferred
  • Style characteristics

Step 3: Present Preview

Show a preview with evidence status before applying. This illustrative table is not product evidence; do not copy its example finishes into real selections:

text
Synthetic formatting input only:
Example Product A | Example Manufacturer | quantity 2 | supplied specification pending

Flag any products where enrichment is uncertain:

⚠ "Custom Reception Desk" — unknown product, category set to Desk but verify

Step 4: Apply

To the project library

Read ../../schema/product-schema.md and ../../schema/csv-conventions.md. Map evidence-backed enrichment to canonical named fields: Category, Materials, available Colors/Finishes, and explicitly established Selected Color/Finish. Preserve existing selections and populated values unless their exact change is authorized. Keep inferred color, texture, material or subcategory descriptions labeled in Notes; proposed descriptive style tags may be appended to Tags with their judgment status retained in Notes. Do not turn visual inference into a specification fact.

For multiple enriched rows, materialize the complete proposed 33-column CSV as a temporary or user-visible review file. Validate it, preview every material change and the target product-library.csv once, then use existing exact authorization or ask once for the missing approval. After approval, hand the complete batch to product-library's native import operation for one guarded publication, not a per-row loop. A genuinely single-record enrichment may use that owner's native update with a uniquely matching stable field and exact request/current-state evidence.

To conversation

Output the enriched table in markdown.

Step 5: Summary

✓ Enriched 4 products
  Categories: 4 assigned (0 already had values)
  Colors: 4 assigned
  Materials: 4 assigned
  Style tags: 14 total tags across 4 products
  Uncertain: 1 (flagged for review)

Pairs With

  • /as:product-spec-bulk-fetch — fetch specs first, then enrich the results
  • /as:product-data-cleanup — cleanup normalizes formatting, enrich adds metadata
  • /as:product-data-import — enriched products make better formatted schedules
  • /as:product-match — enriched tags help find better matches

Original product evidence

Retrieve exact selected manufacturer/product/variant facts from original documents for the task. Example data is synthetic and never evidence. Do not copy product facts, certifications, prices or vendor format definitions into the plugin as reusable reference knowledge. Preserve unresolved values and distinguish representative imagery from the exact selected variant.

Native output and owner handoff

A requested durable authored report uses receive's native document owner for exact coordinate-based placement, query and registration; do not compose project folders from labels. One-off reports use only their explicitly authorized destination and require no studio/project setup. Canonical adopted revisions and reusable library saves remain with their owners. Existing exact authorization persists; obtain only missing material scope or native permission.

For any output this skill actually saves or edits, apply the native mutation sequence to its complete affected set. Retain all original bytes/access, source/identity guards, complete prepared output and absence/preconditions; finish durable saves and independently reread all retained/prepared content and access before the first publisher. Preserve unrelated data and actual workbook features when applicable. Reopen every actual destination's complete bytes and mode/applicable ownership/ACLs, and verify changes, source lineage and protected originals before reporting completion. Correct bytes, a creation-mode argument or an emitted receipt alone is insufficient. Unsupported protection or uncertain publication stays blocked/pending with recovery evidence.

Use native capabilities suited to the selected mode; process execution is optional when the chosen method needs it. No Arch Studio runner, executable download or source reconstruction is required. Read-only/inline work does not need write capability. Report actual research/extraction, validated proposal and any independently verified owner save separately; do not claim an owner handoff has completed without its actual evidence.

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 Enrich AI skill do?

Enrich FF&E schedule rows with categories, colors, materials, and style tags. Use to tag, categorize, or fill those missing fields; not to research new products.

Why use Product Enrich on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AlpacaLabsLLC/skills-for-architects/tree/main/skills/product-enrich. 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 Enrich?

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 Enrich?

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

Is the Product Enrich 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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