Site Storage Heatmap logo

Site Storage Heatmap

Organization
pnp
site-storage-heatmap

Generates an interactive HTML site map showing storage breakdown and hot/cold activity heatmap across all document libraries, lists, and site pages. Includes site-wide summary stacked bars and per-library click-through drill-downs. Saves the file as {FirstName}-Site-Storage-Heatmap.html in the "storage heatmap" folder inside the site's document library, then navigates the user to it.

Overview

Publisherpnp
Repositorysharepoint-skills
Skill namesite-storage-heatmap
Stars
141
Forks
60
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 pnp on GitHub. Read the source before you install it.

Installation

Install the Site Storage Heatmap 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/pnp/sharepoint-skills/tree/main/Skills/site-storage-heatmap/site-storage-heatmap
  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/pnp/sharepoint-skills.git /tmp/sharepoint-skills
mkdir -p .claude/skills
cp -r /tmp/sharepoint-skills/Skills/site-storage-heatmap/site-storage-heatmap .claude/skills/site-storage-heatmap
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Site Storage Heatmap 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 Site Storage Heatmap 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 Site Storage Heatmap 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.

Site Storage Heatmap

Generate an interactive HTML site map with storage sizes and hot/cold activity heatmaps for a SharePoint site, then navigate the user to the output.

Steps

1. Get the Current User's First Name

  • Call get_user_info(query="@currentUser") to resolve the current user.
  • Extract the first name from the resolved display name (e.g., "Adam Harmetz" → "Adam").
  • The output filename will be: {FirstName}-Site-Storage-Heatmap.html

2. Discover All Containers

  • Call discover_sharepoint_lists with filterType="all" and includeHidden=false to get every list, library, and page library on the site.
  • Categorize each container:
    • Document Libraries: templateType 101
    • Lists: templateType 100
    • Site Pages: templateType 119
    • Calendar: templateType 106
  • Record each container's id, title, itemCount, and serverRelativeUrl.

3. Collect File-Level Data from Each Non-Empty Library

  • For every document library (templateType 101) with itemCount > 0, call list_items with:
    • viewFields: ["FileLeafRef", "File_x0020_Size", "File_x0020_Type", "Modified"]
    • itemType: "files", recursive: true, rowLimit set to the library's itemCount
  • For Site Pages (templateType 119), also query with viewFields: ["FileLeafRef", "Modified"] to get page modification dates.
  • Parallelize all independent list_items calls.
Large sites (≥ 100 libraries or any library with ≥ 5,000 items)

Naïvely parallelising every list_items call against a large site will hit throttling (HTTP 429) and balloon memory because every file row sits in the model state.

  • Batch the parallel fan-out at no more than ~10 concurrent list_items calls. After each batch, aggregate the results into the running totals via execute_code with outputDataRef: true so the raw rows are released from model state. Never hold all libraries' file rows in memory at once.
  • Per-library paging. If a library reports itemCount > 5,000, fetch it in pages of 5,000 (the SharePoint list view threshold) rather than one giant rowLimit. Aggregate per-page into running totals — do not retain the per-page raw arrays after they are summed.
  • On 429 / throttling: back off, retry the affected library once with a smaller rowLimit, and continue. If retry fails, record the library as Partial data — throttled and surface it explicitly in the HTML output (a yellow badge on that library card) so the consumer knows the totals are an undercount, not zero.
  • Progress reporting. Every 10 libraries processed, post a short progress line (Processed 30 of 142 libraries…) so a multi-minute run doesn't look frozen.
  • Never silently drop a library. Every container from Step 2 must appear in the final HTML output, even if its row is annotated (no data) or (throttled).

4. Aggregate Per-Library Stats

For each library, compute:

  • Total size (sum of File_x0020_Size in bytes)
  • File count
  • File type breakdown: group by File_x0020_Type → { ext: { count, size } }
  • Recency buckets based on Modified date relative to today:
    • Hot: modified ≤ HOT_DAYS ago (default 7)
    • Warm: modified ≤ WARM_DAYS ago (default 30)
    • Cool: modified ≤ COOL_DAYS ago (default 90)
    • Cold: modified > COOL_DAYS ago
  • Track both file count AND storage per recency bucket.
Configurable bucket thresholds

The 7 / 30 / 90 day defaults are conventional but not always right — a fast-moving project site may want 1 / 7 / 30, a records archive may want 30 / 180 / 365.

  • Honour explicit overrides in the user's prompt. Examples that must work:
    • "heatmap with hot=1 day, warm=7, cool=30"
    • "use 30/90/365 buckets"
    • "only consider files older than a year as cold"
  • Validate: HOT_DAYS < WARM_DAYS < COOL_DAYS, all positive integers. If the user gives invalid values, ask once for correction; do not silently re-order.
  • Always render the thresholds used in the HTML output — a small legend line under the heatmap such as Hot ≤ 7d · Warm ≤ 30d · Cool ≤ 90d · Cold > 90d — so the consumer can tell at a glance what "hot" means in this report.

For lists (no file sizes), classify each list as hot/warm/cool/cold based on the list's lastModified date from the discovery response. Count list items per recency bucket.

5. Compute Site-Wide Rollup

Sum across ALL containers (libraries + lists + pages):

  • Grand total items (files + list items + pages)
  • Total storage (libraries only)
  • Aggregate recency: total hot / warm / cool / cold counts
  • Separate recency breakdowns for: Document Libraries, Lists, Site Pages

6. Generate the Interactive HTML

Build a self-contained HTML file with embedded CSS and JavaScript containing:

Root Banner
  • Site name, total item count, container count, page count
  • "Total Library Storage: X" badge
Site-Wide Heatmap Panel (below root, above branches)
  • 3-column grid: one for Document Libraries, one for Lists, one for Site Pages
  • Each column has a stacked horizontal bar (hot=red #E74856, warm=orange #FF8C00, cool=blue #50E6FF, cold=grey #A0AEC0) showing file/item counts per recency tier
  • Below each bar: storage per tier (for libraries) or counts (for lists/pages)
  • Bottom legend row: grand totals per tier across the entire site
3-Column Site Map (below heatmap)
  • Column 1 (wider): Document Libraries — all libraries merged together (including expense libraries), sorted by total size descending. Each leaf card shows: name, size badge, file count badge. Clickable.
  • Column 2: Lists — each list with item count badge. Not clickable.
  • Column 3: Site Pages — each page name. Not clickable.
  • Empty containers grayed out.
Click-Through Modal (JavaScript)

When a library leaf is clicked, show a modal overlay with:

  • Summary cards: File count, Total size, File type count
  • Activity Heatmap: 4 horizontal bars (hot/warm/cool/cold) showing file counts + storage per tier
  • Storage by File Type: horizontal bars per extension showing % of total, file count, and size
  • Close on Escape, click outside, or ✕ button
Styling
  • Use Segoe UI font, #f5f5f5 background
  • Branch headers: blue gradient for doc libs, green for lists, purple for pages
  • Responsive grid that stacks on mobile
  • Hover effects on clickable leaves (translateX + shadow)
  • Modal with backdrop blur

7. Save the HTML File

  • Save as {FirstName}-Site-Storage-Heatmap.html
  • Target location: the site's document library, inside a folder called "storage heatmap"
    • Use create_text_file with relativeFolderPath="storage heatmap"
    • If the folder doesn't exist, create it first with create_folder

8. Navigate the User

  • After saving, call navigate_to_url to take the user to the document library where the file was saved.
  • Provide the direct file URL so the user can open it.
  • Remind the user: "Download and open in your browser for full interactivity — SharePoint strips JavaScript from inline HTML previews."

Example

User: "Generate a storage heatmap for this site"

Result: File saved as Adam-Site-Storage-Heatmap.html in the "storage heatmap" folder, user navigated to the library, with a note to download for JS interactivity.

Constraints

  • Always get the current user's first name — never hardcode.
  • Always get the current date via get_datetime_info before computing recency buckets — never assume today's date.
  • Parallelize list_items calls for all non-empty libraries to minimize latency. For large sites, follow the batching/throttling rules in Step 3 → Large sites — never fan out unbounded.
  • Use execute_code with outputDataRef: true for all large intermediate data so raw rows are released from model state after aggregation.
  • Keep the HTML fully self-contained (no external dependencies).
  • File type colors: use a rotating palette of Fluent-style colors.
  • Recency colors are fixed: hot=#E74856, warm=#FF8C00, cool=#50E6FF, cold=#A0AEC0.
  • Recency thresholds are configurable per run (see Step 4 → Configurable bucket thresholds) but must be rendered in the HTML so the report is self-explanatory.
  • Every container discovered in Step 2 must appear in the final HTML, including throttled or empty ones — never silently drop.

Frequently asked questions

What does the Site Storage Heatmap AI skill do?

Generates an interactive HTML site map showing storage breakdown and hot/cold activity heatmap across all document libraries, lists, and site pages. Includes site-wide summary stacked bars and per-library click-through drill-downs. Saves the file as {FirstName}-Site-Storage-Heatmap.html in the "storage heatmap" folder inside the site's document library, then navigates the user to it.

Why use Site Storage Heatmap on TypingMind?

Because you install it once and use it with any model. Site Storage Heatmap 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 Site Storage Heatmap in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pnp/sharepoint-skills/tree/main/Skills/site-storage-heatmap/site-storage-heatmap. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Site Storage Heatmap?

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 Site Storage Heatmap?

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

Is the Site Storage Heatmap AI skill free?

Yes. It is published on GitHub by pnp 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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