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Spec Splitter

Community
dleerdefi
spec-splitter

Split a bound project manual PDF into individual spec section PDFs and extract searchable text. Triggers: 'split specs', 'break up the project manual', 'separate spec sections', 'extract spec text'. Prerequisite for /submittal-log-generator.

Overview

Publisherdleerdefi
Repositoryclaude-code-construction
Skill namespec-splitter
Stars
55
Forks
14
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Spec Splitter 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/dleerdefi/claude-code-construction/tree/main/skills/spec-splitter
  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/dleerdefi/claude-code-construction.git /tmp/claude-code-construction
mkdir -p .claude/skills
cp -r /tmp/claude-code-construction/skills/spec-splitter .claude/skills/spec-splitter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spec Splitter 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 Spec Splitter 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 Spec Splitter 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.

Spec Splitter

Two functions for specification processing:

  1. Split: Break a bound project manual PDF into individual spec section PDFs — navigable files the project team can use directly
  2. Extract: Pull searchable text from each section PDF into persistent .txt files — enables downstream skills (submittal-log-generator, submittal-review, bid-evaluator) to work from text without re-extracting from PDFs

Pipeline Position

Run after /project-setup identifies bound spec manuals. Produces split PDFs, spec_index.yaml, and extracted text consumed by /submittal-log-generator and /code-researcher.

Either function can run independently. For example, specs may already be split but text has not yet been extracted.

In an AgentCM project, AgentCM has already read the manuals: each section's text is in .construction/specs/, listed in .construction/INDEX.md. There this skill does only what AgentCM could not (Step 1).

Workflow

Spec Split Progress:
- [ ] Step 1: Check the mode, then the current state (split? text extracted?)
- [ ] Step 2: Discover Specifications directory
- [ ] Step 3: Find ALL spec PDFs (bound manuals)
- [ ] Step 4-5: Split PDF into individual section files
- [ ] Step 6: Write spec index
- [ ] Step 7: Extract text from all sections
- [ ] Step 8: Repair degraded/poor text quality

Step 1: Check the Mode, Then the Current State

Read .construction/project.yaml first.

AgentCM mode (the file exists). AgentCM has read the project manuals' text layer: every section it found has its text in .construction/specs/<section>.txt (08-71-00.txt for 08 71 00), each line prefixed with its page. Never split or extract a manual AgentCM has read; the other skills read specs/ here. Read .construction/INDEX.md; if it is missing, say that AgentCM has not written its index for this folder, and stop.

  1. ## Specification sections (N), a table of Section | Title | Division | Agent file | PDF | Pages: report how many sections AgentCM holds, by division and manual, and that their text is in .construction/specs/.
  2. ## Spec manuals not read (N), when present, a table of PDF | Pages | Why (a scan with no text layer, or no SECTION headings): for each manual listed there, and only those, run Steps 2–8 for that manual alone, and say so: "AgentCM could not read {PDF} ({Why}), so I am splitting and extracting it here. Its section PDFs go to the specs folder, which AgentCM's Rescan will index; its text goes to .construction/skills/spec_text/." Its boundaries come from Boundaries without SECTION headings: what AgentCM reports means the split script cannot find them itself.
  3. Section PDFs of a manual AgentCM has read, only when the PE asks for those files: build them from AgentCM's list, as Section PDFs from AgentCM's list says. No text extraction: the text is AgentCM's.

Then stop. Nothing else in this skill runs in AgentCM mode.

Flat-file mode (no .construction/project.yaml). Check what already exists:

Split PDFs present?

  • Look for individual spec section PDFs with CSI section numbers in filenames (e.g., 03 30 00 - Cast-in-Place Concrete.pdf)
  • Check for spec_index.yaml
  • If found, report count and skip to Step 7 (text extraction)

Text already extracted?

  • Check .construction/skills/spec_text/manifest.json. Older versions wrote it to .construction/spec_text/: if only that exists, copy the folder to .construction/skills/spec_text/ and leave the old one as it is
  • If manifest exists and covers all sections, report and skip Step 7

Step 2: Discover Specifications Directory

Determine where split spec PDFs should go. Search for an existing Specifications directory (case-insensitive):

  1. 02 - Specifications/ (numbered project folder convention)
  2. Specifications/
  3. Any folder with "specification" in the name

Output directory resolution:

  • If Specifications directory found → output to {specs_dir}/Specification Sections/
  • If not found → output to Specification Sections/ in project root

Step 3: Find ALL Spec PDFs

Search the project directory for ALL PDFs that are specifications. Many projects have multiple spec PDFs:

  • Multi-volume: Volume 1.pdf, Volume 2.pdf (split by CSI division range)
  • Single bound manual: one large PDF with all sections
  • Attachment-based: Attachment-E-Specs.pdf (government projects)

Search in:

  • The Specifications directory discovered in Step 2
  • The project root (some projects have no folder structure)
  • Look for PDFs > 1MB with keywords: "spec", "manual", "volume", "attachment" + spec-related terms

Process EACH PDF found. All split sections go to the same output directory.

If the user specifies a single file (/spec-splitter path/to/specific-volume.pdf), process only that file.

Steps 4-6: Split and Index

Run the split script with the resolved output directory:

bash
"${CLAUDE_PLUGIN_ROOT}/bin/construction-python" "${CLAUDE_SKILL_DIR}/scripts/split_spec_manual.py" \
  "{project_manual.pdf}" \
  --output-dir "{resolved_spec_sections_dir}"

The script:

  1. Scans ALL pages for SECTION XX XX XX headers to find exact page boundaries — this is the primary method and does NOT depend on a Table of Contents
  2. Scans all pages for Table of Contents entries to enrich section titles (optional, best-effort)
  3. For sections without ToC titles, extracts titles directly from the section header page
  4. Splits into individual PDFs named {section_number} - {SECTION TITLE}.pdf
  5. Writes spec_index.yaml with section metadata

ToC edge cases handled:

  • ToC located deep in the document (e.g., page 60+): Common when front matter (transmittals, addenda) precedes the project manual. The script scans all pages, not just the first few.
  • No ToC at all: Section boundaries are found by scanning every page for SECTION headers. Titles are extracted directly from each section's title page. The split still succeeds — titles may be slightly less polished than ToC-enriched versions.

If the script stops with No SECTION headers found (a scan, or headings in another form), find the boundaries as Boundaries without SECTION headings says, and split from a sections file.

Step 7: Extract Text

After splitting (or if specs are already split), extract searchable text from every section:

bash
"${CLAUDE_PLUGIN_ROOT}/bin/construction-python" "${CLAUDE_SKILL_DIR}/scripts/extract_spec_text.py" \
  --specs-dir "{resolved_spec_sections_dir}" \
  --output-dir ".construction/skills/spec_text"

The script:

  • Extracts text from each section PDF via pdfplumber
  • Assesses extraction quality (GOOD / DEGRADED / POOR)
  • Writes one .txt file per section to .construction/skills/spec_text/
  • Writes manifest.json with quality metadata per section
  • Incremental: skips sections that already have .txt files (use --force to re-extract all)

Step 8: Text Repair — GUIDED

After extraction, check manifest.json for sections rated DEGRADED or POOR. Spec-splitter owns text quality — downstream skills (submittal-log-generator, submittal-review, bid-evaluator) expect clean, repaired text.

For DEGRADED sections — attempt repair:

  1. Read the .txt file and identify failure modes from the manifest
  2. Split word repair: Scan for sequences of short tokens (≤2 chars) not in known abbreviation lists (GC, CM, PE, QA, SF, LF, etc.). Attempt progressive concatenation of adjacent tokens. Validate against construction vocabulary. Merge if valid; leave as-is if not.
  3. Merged word repair: Tokens >25 characters that contain multiple dictionary words — insert spaces at word boundaries
  4. Garbled character repair: Replace known encoding artifacts (e.g., é → é, ligature breakage)
  5. After repair, re-assess quality. If improved, overwrite the .txt file and update the manifest with "repair_attempted": true and the new quality rating.
  6. If repair made things worse, discard repairs and fall back to vision.

For POOR sections — vision extraction fallback. A scanned manual is POOR on every page, and this reads every page of it: before reading the pages of a scan, tell the PE how many pages that is and the rough cost (each page image at 200 DPI is about 1,600 tokens), offer to extract only the sections they need, and wait for a yes.

  1. Render each page of the section PDF as an image:
    bash
    "${CLAUDE_PLUGIN_ROOT}/bin/construction-python" "${CLAUDE_PLUGIN_ROOT}/scripts/pdf/rasterize_page.py" "{section.pdf}" {page} --dpi 200 --output ".construction/skills/spec-splitter/spec_page.png"
  2. Process each page image through vision:
    Extract all text from this construction specification page.
    Preserve paragraph structure, numbering (A, B, C, 1, 2, 3),
    and indentation hierarchy. This is CSI-formatted specification
    section [SECTION NUMBER] - [SECTION TITLE].
  3. Concatenate extracted text in page order
  4. Write the vision-extracted text to .construction/skills/spec_text/, overwriting the POOR pdfplumber output
  5. Update manifest: "extraction_method": "vision", "repair_attempted": true, new quality rating

Known abbreviation preservation list (do not merge these during repair):

  • Standard: A, I, or, an, as, at, be, by, do, if, in, is, it, no, of, on, so, to, up, we
  • Construction: GC, CM, PE, QA, QC, SF, LF, CY, EA, LS, GA, MIL, PSI, KSI, CFM, GPM
  • Section refs: A, B, C, D (as paragraph identifiers)

Sections Files

A sections file tells the split script exactly where each section is, so it reads nothing from the pages. It is tab-separated text, the same format the script's docstring defines:

# manual: 02 - Specifications/Project Manual VOL-2.pdf
# pages: 1-based
section	title	first	last
08 71 00	DOOR HARDWARE	412	431
09 65 13	RESILIENT BASE AND ACCESSORIES	455	460
  • # manual: names the PDF being split; # pages: 1-based states the page base. Page 1 is the manual's first page, as INDEX.md and specs/ give pages; convert nothing.
  • One line per section: number, title, first page, last page (inclusive). Gaps are allowed.
  • Write it with the Write tool to .construction/skills/spec-splitter/sections-{manual file stem}.tsv. It is a data file, not a script.
  • The script refuses a file whose pages overlap, run backwards or fall outside the manual, or that lacks the page base, and says why: correct the file.

Split from it:

bash
"${CLAUDE_PLUGIN_ROOT}/bin/construction-python" "${CLAUDE_SKILL_DIR}/scripts/split_spec_manual.py" \
  "{manual.pdf}" \
  --sections-file ".construction/skills/spec-splitter/sections-{manual file stem}.tsv" \
  --output-dir "{resolved_spec_sections_dir}"

Section PDFs from AgentCM's List

AgentCM mode, only when the PE asks for section PDFs of a manual AgentCM has read. Build the sections file from the rows of INDEX.md's ## Specification sections table whose PDF is that manual: Section, Title and Pages (412-431, or 412 for a one-page section; 1-based). Take only the sections the PE asked for, or all of them if they asked for all. A row with no Pages cannot be split from the index: list it for the PE and leave it out. Read no pages and use no vision. After the split, tell the PE where the files are and that AgentCM's Rescan will index them.

Boundaries without SECTION headings

For a manual AgentCM lists as not read, or one the split script found no headings in. First see what the manual holds:

bash
"${CLAUDE_PLUGIN_ROOT}/bin/construction-python" "${CLAUDE_PLUGIN_ROOT}/scripts/pdf/pdf_text.py" "{manual.pdf}" --summary
  • Pages marked TEXT (a text layer whose headings take another form, such as SECTION 08710, or only footers like 08 71 00 - 1): dump the text with pdf_text.py "{manual.pdf}" --pages {range} and find each section's first page from its heading, its footer's page 1 or the Table of Contents.
  • Pages marked VISION (a scan): before reading any page image, tell the PE the manual's page count and the rough cost (one footer or header strip per page, each a small image of about 125 tokens at 100 DPI), and wait for a yes. Never read every page of a large scan unasked. With a yes, read the strip where the section number and page number print, usually the footer:
    bash
    "${CLAUDE_PLUGIN_ROOT}/bin/construction-python" "${CLAUDE_PLUGIN_ROOT}/scripts/pdf/rasterize_page.py" "{manual.pdf}" {page} --dpi 100 --crop 0,90,100,100 --output ".construction/skills/spec-splitter/strip.png"
    --crop is x1,y1,x2,y2 in percent of the page (0,0,100,8 for the header). A section starts on its page 1 (08 71 00 - 1), which also carries its title.

Write what you found as a sections file, show the PE the list, and split from it.

Output

{Specifications dir}/Specification Sections/
  01 10 00 - SUMMARY.pdf
  03 30 00 - CAST-IN-PLACE CONCRETE.pdf
  08 71 00 - DOOR HARDWARE.pdf
  ...
  spec_index.yaml

.construction/skills/spec_text/
  01_10_00.txt
  03_30_00.txt
  08_71_00.txt
  ...
  manifest.json

.construction/skills/spec-splitter/
  sections-{manual file stem}.tsv   ← only when a split used a sections file

The spec_index.yaml and manifest.json files are the record of the split and the extraction.

Report to user: number of sections split, total pages, text extraction quality summary (GOOD/DEGRADED/POOR counts), and output locations.

File Safety

Never overwrite existing split spec PDFs or extracted text. The split script skips existing sections. Text extraction overwrites only with --force. The spec_index.yaml merge is additive.


Allowed Scripts

Allowed scripts — exhaustive list. Only execute these scripts during this skill:

  • scripts/split_spec_manual.py — split bound PDF into per-section PDFs, from SECTION headings or a sections file
  • scripts/extract_spec_text.py — extract searchable text from section PDFs
  • ${CLAUDE_PLUGIN_ROOT}/scripts/pdf/pdf_text.py — see which pages have a text layer, and read it, to find section boundaries
  • ${CLAUDE_PLUGIN_ROOT}/scripts/pdf/rasterize_page.py — rasterize pages for the vision fallback and for a scan's footer strips
  • ${CLAUDE_PLUGIN_ROOT}/scripts/issue_manager.py — log a referenced spec section that does not exist to the Issue Registry (command and severity mapping in construction-guide)

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 Spec Splitter AI skill do?

Split a bound project manual PDF into individual spec section PDFs and extract searchable text. Triggers: 'split specs', 'break up the project manual', 'separate spec sections', 'extract spec text'. Prerequisite for /submittal-log-generator.

Why use Spec Splitter on TypingMind?

Because you install it once and use it with any model. Spec Splitter 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 Spec Splitter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dleerdefi/claude-code-construction/tree/main/skills/spec-splitter. 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 Spec Splitter?

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 Spec Splitter?

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

Is the Spec Splitter AI skill free?

Yes. It is published on GitHub by dleerdefi 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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