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.
npx skills add dleerdefi/claude-code-construction --skill submittal-log-generatorgit clone --depth 1 https://github.com/dleerdefi/claude-code-constructionWrote 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.
[](https://agentmods.dev/skills/dleerdefi/claude-code-construction/submittal-log-generator)<a href="https://agentmods.dev/skills/dleerdefi/claude-code-construction/submittal-log-generator"><img src="https://agentmods.dev/badge/skills/dleerdefi/claude-code-construction/submittal-log-generator/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.
<a href="https://agentmods.dev/skills/dleerdefi/claude-code-construction/submittal-log-generator"><img src="https://agentmods.dev/badge/skills/dleerdefi/claude-code-construction/submittal-log-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.06018 |
| Opus 5 | $0.00028 | $0.03009 |
| Sonnet 5 | $0.00011 | $0.01204 |
| Haiku 4.5 | $0.00006 | $0.00602 |
Grade A, and why
submittal-log-generator 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 505 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Submittal Log Generator
Long-running skill that processes every specification section to extract submittal requirements and produces a professional submittal register in Excel. Designed to handle full project manuals (200+ spec sections) with state persistence.
Prerequisite: This skill depends on /spec-splitter to provision clean, per-section .txt files at .construction/spec_text/. The spec-splitter owns all text quality — extraction, repair, and quality assessment. This skill reads those .txt files and focuses on identifying submittal items.
Design Philosophy: Scripts for Structure, Claude for Judgment
This skill uses a three-tier approach:
- RIGID (scripted, deterministic): File I/O, Excel output schema, directory structure, naming conventions. Follow these exactly. Do not improvise.
- GUIDED (decision tree, Claude picks the branch): Confidence scoring, output location discovery. Follow the decision logic; adapt thresholds to what you observe.
- FLEXIBLE (domain knowledge, Claude thinks): Identifying what is actually a submittal item, distinguishing submittals from boilerplate, resolving ambiguity, handling non-standard spec formats. Use your understanding of construction documents and the domain knowledge below.
Allowed scripts — exhaustive list. Only execute these scripts during this skill:
export_submittal_log.py— Excel output from assembled JSON (Step 4)write_finding.py— graph entry (Step 5) Do not create, generate, or write any.py,.sh, or other script files. All data assembly is Claude writing JSON directly.
Current Submittal State
!cat .construction/submittal_extraction_state.yaml 2>/dev/null || echo "No prior extraction — starting fresh"
Output Schema — RIGID
The Excel submittal log MUST contain these columns in this order. Do not rename, reorder, or omit columns.
| Column | Description | Example |
|---|---|---|
| Spec Section | CSI section number | 03 30 00 |
| Spec Title | Section title | Cast-in-Place Concrete |
| Submittal No. | Sequential ID: [Section]-[###] |
03 30 00-001 |
| Submittal Type | Categorized type (see taxonomy below) | Product Data |
| Submittal Description | The actual submittal requirement, cleaned | Submit product data for each concrete mix design including compressive strength, admixtures, and mix proportions |
| Article Reference | Where found in the spec | 1.3.A.1 |
| Action/Informational | Action or Informational | Action |
| Confidence | HIGH / MEDIUM / LOW / FLAGGED | HIGH |
| Flag Reason | Empty if HIGH; explanation otherwise | Possible boilerplate — verify intent |
| Extraction Method | pdfplumber / vision / hybrid | pdfplumber |
| Notes | Any context Claude thinks the PE should know | Referenced mix designs in 03 31 00 |
What ships with it
4 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.
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.
- 9d ago First seen · 505 lines · 55 tokens per session scan A ed5b7c64d5f5
submittal-log-generator is a skill published in the GitHub repository dleerdefi/claude-code-construction (37 stars, last pushed 4mo ago), licensed MIT. It adds 55 tokens to every session and 6,018 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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