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 datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill email-constructiongit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_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/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/email-construction)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/email-construction"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/email-construction/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/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/email-construction"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/email-construction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 3 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
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 | $0.00035 | $0.03067 |
| Opus 5.5 | $0.00014 | $0.01227 |
| Sonnet 5.5 | $0.00007 | $0.00613 |
| Haiku 4.5 | $0.00003 | $0.00307 |
Grade A, and why
email-construction 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- email-construction — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 497 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Generation for Construction
Overview
Generate professional construction emails with proper formatting, context, and attachments handling. Templates for common construction communication workflows.
Construction Use Cases
1. RFI Response Email
Generate professional RFI response emails.
from dataclasses import dataclass
from datetime import datetime
from typing import Optional, List
@dataclass
class RFIResponse:
rfi_number: str
project_name: str
subject: str
question: str
response: str
responder_name: str
responder_title: str
attachments: List[str] = None
cc_list: List[str] = None
def generate_rfi_response_email(rfi: RFIResponse) -> dict:
"""Generate RFI response email."""
subject = f"RE: RFI #{rfi.rfi_number} - {rfi.subject}"
body = f"""Dear Project Team,
Please find below our response to RFI #{rfi.rfi_number}.
**Project:** {rfi.project_name}
**RFI Number:** {rfi.rfi_number}
**Subject:** {rfi.subject}
**Date:** {datetime.now().strftime('%B %d, %Y')}
---
**QUESTION:**
{rfi.question}
---
**RESPONSE:**
{rfi.response}
---
Please proceed accordingly. If you have any questions regarding this response, please contact us.
{"**Attachments:**" + chr(10) + chr(10).join(f"- {a}" for a in rfi.attachments) if rfi.attachments else ""}
Best regards,
{rfi.responder_name}
{rfi.responder_title}
"""
return {
'subject': subject,
'body': body,
'cc': rfi.cc_list or [],
'attachments': rfi.attachments or []
}
2. Submittal Transmittal Email
Generate submittal transmittal emails.
@dataclass
class SubmittalTransmittal:
submittal_number: str
project_name: str
spec_section: str
description: str
items: List[dict]
action_required: str
due_date: str
sender_name: str
sender_company: str
def generate_submittal_email(submittal: SubmittalTransmittal) -> dict:
"""Generate submittal transmittal email."""
subject = f"Submittal {submittal.submittal_number} - {submittal.spec_section} - {submittal.description}"
items_list = "\n".join(
f" {i+1}. {item['description']} ({item.get('copies', 1)} copies)"
for i, item in enumerate(submittal.items)
)
body = f"""Dear Design Team,
Please find attached Submittal {submittal.submittal_number} for your review.
**Project:** {submittal.project_name}
**Submittal No:** {submittal.submittal_number}
**Spec Section:** {submittal.spec_section}
**Description:** {submittal.description}
**Items Transmitted:**
{items_list}
**Action Required:** {submittal.action_required}
**Response Requested By:** {submittal.due_date}
Please review and return with your comments at your earliest convenience.
If you have any questions, please don't hesitate to contact us.
Best regards,
{submittal.sender_name}
{submittal.sender_company}
"""
return {
'subject': subject,
'body': body,
'priority': 'normal'
}
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.
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.
- 1mo ago First seen · 497 lines · 35 tokens per session scan A 505ad0760dee
email-construction is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (374 stars, last pushed 8d ago), licensed MIT. It adds 35 tokens to every session and 3,067 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 2026-09-03.
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