lighting-report-extract

lighting-report-extract is a skill for Claude Code from AlpacaLabsLLC/skills-for-architects. It costs 37 tokens per session (953 once invoked), scanned A, original, MIT.

A PDF-reading workflow for extracting reported lighting quantities and results from supplied simulation reports. It records luminaire inventories and illuminance results but does not run simulations or decide whether a design meets regulations.

In plain words
What is it for?
Use it to extract scoped room or surface results, lighting metrics and luminaire inventories from lighting-report PDFs.
Why use it?
It separates reported results from new calculations and compliance judgments, reducing the risk of presenting an extraction as a design approval.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the as plugin — 61 skills, 8 agents, 2 hooks shipped together

Good fit Use it to extract scoped room or surface results, lighting metrics and luminaire inventories from lighting-report PDFs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/alpacalabsllc/skills-for-architects/lighting-report-extract
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add AlpacaLabsLLC/skills-for-architects --skill lighting-report-extract
Clone the repo
git clone --depth 1 https://github.com/AlpacaLabsLLC/skills-for-architects

Made for: Claude Code.

Or install as, the plugin that ships this one along with the rest of its 61 skills, 8 agents, 2 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for lighting-report-extract

README.md
[![agentmods](https://agentmods.dev/badge/skills/alpacalabsllc/skills-for-architects/lighting-report-extract/github.svg)](https://agentmods.dev/skills/alpacalabsllc/skills-for-architects/lighting-report-extract)
Your own site
<a href="https://agentmods.dev/skills/alpacalabsllc/skills-for-architects/lighting-report-extract"><img src="https://agentmods.dev/badge/skills/alpacalabsllc/skills-for-architects/lighting-report-extract/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for lighting-report-extract

Your own site · 80×15
<a href="https://agentmods.dev/skills/alpacalabsllc/skills-for-architects/lighting-report-extract"><img src="https://agentmods.dev/badge/skills/alpacalabsllc/skills-for-architects/lighting-report-extract.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 953 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00037 $0.00953
Opus 5.5 $0.00015 $0.00381
Sonnet 5.5 $0.00007 $0.00191
Haiku 4.5 $0.00004 $0.00095

Measured 25d ago against content hash b3f49f023f5a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

lighting-report-extract scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 25d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/lighting-report-extract/SKILL.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/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.

Read the full file on GitHub · 59 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 25d ago First seen · 59 lines · 37 tokens per session scan A b3f49f023f5a

Subscribe to this mod's changes

lighting-report-extract is a skill published in the GitHub repository AlpacaLabsLLC/skills-for-architects (381 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 953 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-21.

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