Lighting Report Extract logo

Lighting Report Extract

Organization
AlpacaLabsLLC
lighting-report-extract

Check reported lighting quantities and results in supplied simulation PDFs without adding repeated calculation surfaces. Use for scoped luminaire inventories and illuminance results; not simulation or compliance.

Overview

PublisherAlpacaLabsLLC
Repositoryskills-for-architects
Skill namelighting-report-extract
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 Lighting Report Extract 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/lighting-report-extract
  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/lighting-report-extract .claude/skills/lighting-report-extract
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lighting Report Extract 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 Lighting Report Extract 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 Lighting Report Extract 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:lighting-report-extract — Extract reported lighting results

Before acting, read the host contract and declaration (skill:lighting-report-extract), using its profiles from the catalog. Host access must be demonstrated, not inferred.

Use the host adapter and available native tools or ordinary task-specific code. Operation names identify semantic procedures, not installed functions. No Arch Studio runner, package installation or executable reconstruction is required. Existing exact authorization persists; ask only for material missing information or permission.

Input: supplied report PDF(s), requested rooms/surfaces, metrics and inventory scope. Output: separately sourced inventory observations and calculation results with a coverage ledger. No simulation, design adequacy determination or regulatory target is produced. One-off extraction requires no studio initialization or schedule adoption.

  1. Read exact source bytes through the host; retain hash/revision. Read owning project instructions when applicable. If access is missing, request an exposed native grant or give the precise handoff.
  2. Read PDF evidence contracts. Inventory every physical page separately from printed labels. Search actual text and inspect table images using host PDF/OCR capabilities. A TOC referring beyond the physical page count can reflect an excerpt with original pagination; it does not prove that results are absent. Check summary and individual calculation-surface tables before marking a requested metric unavailable.
  3. Extract exact room/surface name, metric label, value, unit, calculation condition/height where reported, document hash, physical page, printed page and table/region locator. Keep average, minimum, uniformity, glare and daylight metrics distinct. Preserve reported decimal notation and unknown units; no target values or compliance conclusions may be supplied from memory.
  4. Keep the report's room/surface labels. A mezzanine is not a named laboratory without explicit mapping evidence. Separate multiple surfaces and scenarios; never select the most favorable value or merge them silently. Inspect requested values against rendered tables and retain conflicts.
  5. When quantities are requested, read the lighting observation contract and schema. A room's luminaire inventory repeated under two calculation surfaces is one inventory, not two additive counts. Establish inventory identity from the report's room/list/scope evidence; matching quantities or labels alone cannot establish it. Keep room, scene, variant and source version separate; unknown identity remains unresolved. A report without a scene label uses not-reported, never an invented scenario. Apply lighting_report.build to the explicitly prepared observations using native computation and the complete grouping rules. The normalized result describes supplied identities; it does not establish source verification.
  6. Return extracted metrics plus per-request status: observed, unresolved association, unparsed or not located after inspection. Preserve inspected page coverage and OCR limitations. Only claim absence within the supplied and inspected pages, not the full originating report. Save requested derived outputs and read them back; do not edit simulation files or adopted schedules. Luminaire product specifications belong to /as:product-spec-pdf-parser; plan counts to /as:drawing-quantity-extract. Compliance assessment is outside this skill's scope. Requested quantity comparison goes to /as:schedule-quantity-reconcile with all locators and unresolved/conflicting groups retained; extraction does not authorize schedule adoption.

Current document placement

Save only authorized derived outputs and verify their actual content, source hashes and complete requested scope. Inline answers and one-off files need no project setup or adoption. For a requested registered project report, resolve confirmed coordinates using the receive-owned native documents.resolve/register semantics in the workspace model, including its complete document/register mutation sequence. Find existing records with documents.query; never guess a folder. This conditional handoff does not authorize schedule or library changes.

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 Lighting Report Extract AI skill do?

Check reported lighting quantities and results in supplied simulation PDFs without adding repeated calculation surfaces. Use for scoped luminaire inventories and illuminance results; not simulation or compliance.

Why use Lighting Report Extract on TypingMind?

Because you install it once and use it with any model. Lighting Report Extract 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 Lighting Report Extract in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AlpacaLabsLLC/skills-for-architects/tree/main/skills/lighting-report-extract. 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 Lighting Report Extract?

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 Lighting Report Extract?

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

Is the Lighting Report Extract 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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