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 bim-to-schedule-4dgit 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/bim-to-schedule-4d)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/bim-to-schedule-4d"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/bim-to-schedule-4d/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/bim-to-schedule-4d"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/bim-to-schedule-4d.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.01612 |
| Opus 5 | $0.00014 | $0.00806 |
| Sonnet 5 | $0.00006 | $0.00322 |
| Haiku 4.5 | $0.00003 | $0.00161 |
Grade A, and why
bim-to-schedule-4d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bim-to-schedule-4d — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BIM to Schedule 4D Integration
Technical Implementation
import pandas as pd
from datetime import date, timedelta
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum
class LinkStatus(Enum):
LINKED = "linked"
UNLINKED = "unlinked"
PARTIAL = "partial"
@dataclass
class ScheduleActivity:
activity_id: str
activity_name: str
start_date: date
end_date: date
duration_days: int
wbs_code: str
predecessors: List[str] = field(default_factory=list)
@dataclass
class BIMElement:
element_id: str
element_name: str
category: str
level: str
zone: str
volume: float = 0
area: float = 0
@dataclass
class BIMScheduleLink:
link_id: str
activity_id: str
element_ids: List[str]
link_type: str # install, remove, temporary
status: LinkStatus
class BIMSchedule4D:
def __init__(self, project_name: str):
self.project_name = project_name
self.activities: Dict[str, ScheduleActivity] = {}
self.elements: Dict[str, BIMElement] = {}
self.links: Dict[str, BIMScheduleLink] = {}
self._link_counter = 0
def import_schedule(self, schedule_data: List[Dict[str, Any]]):
for act in schedule_data:
activity = ScheduleActivity(
activity_id=act['id'],
activity_name=act['name'],
start_date=act['start'],
end_date=act['end'],
duration_days=(act['end'] - act['start']).days,
wbs_code=act.get('wbs', ''),
predecessors=act.get('predecessors', [])
)
self.activities[activity.activity_id] = activity
def import_elements(self, element_data: List[Dict[str, Any]]):
for elem in element_data:
element = BIMElement(
element_id=elem['id'],
element_name=elem['name'],
category=elem['category'],
level=elem.get('level', ''),
zone=elem.get('zone', ''),
volume=elem.get('volume', 0),
area=elem.get('area', 0)
)
self.elements[element.element_id] = element
def create_link(self, activity_id: str, element_ids: List[str],
link_type: str = "install") -> BIMScheduleLink:
if activity_id not in self.activities:
return None
self._link_counter += 1
link_id = f"LNK-{self._link_counter:05d}"
# Verify elements exist
valid_elements = [eid for eid in element_ids if eid in self.elements]
status = LinkStatus.LINKED if valid_elements else LinkStatus.UNLINKED
if valid_elements and len(valid_elements) < len(element_ids):
status = LinkStatus.PARTIAL
link = BIMScheduleLink(
link_id=link_id,
activity_id=activity_id,
element_ids=valid_elements,
link_type=link_type,
status=status
)
self.links[link_id] = link
return link
def auto_link_by_level(self, level: str, activity_id: str):
"""Auto-link all elements on a level to an activity."""
level_elements = [e.element_id for e in self.elements.values()
if e.level == level]
if level_elements:
return self.create_link(activity_id, level_elements)
return None
def get_elements_for_date(self, target_date: date) -> List[BIMElement]:
"""Get elements that should be visible on a specific date."""
visible_elements = []
for link in self.links.values():
activity = self.activities.get(link.activity_id)
if activity and activity.start_date <= target_date <= activity.end_date:
for elem_id in link.element_ids:
if elem_id in self.elements:
visible_elements.append(self.elements[elem_id])
return visible_elements
def get_unlinked_elements(self) -> List[BIMElement]:
linked_ids = set()
for link in self.links.values():
linked_ids.update(link.element_ids)
return [e for e in self.elements.values() if e.element_id not in linked_ids]
def get_unlinked_activities(self) -> List[ScheduleActivity]:
linked_activities = {link.activity_id for link in self.links.values()}
return [a for a in self.activities.values() if a.activity_id not in linked_activities]
def get_link_summary(self) -> Dict[str, Any]:
total_elements = len(self.elements)
linked_elements = len(set(
eid for link in self.links.values() for eid in link.element_ids
))
return {
'total_activities': len(self.activities),
'total_elements': total_elements,
'linked_elements': linked_elements,
'unlinked_elements': total_elements - linked_elements,
'total_links': len(self.links),
'link_coverage': round(linked_elements / total_elements * 100, 1) if total_elements > 0 else 0
}
def export_links(self, output_path: str):
data = []
for link in self.links.values():
activity = self.activities.get(link.activity_id)
data.append({
'Link ID': link.link_id,
'Activity': activity.activity_name if activity else '',
'Start': activity.start_date if activity else None,
'End': activity.end_date if activity else None,
'Elements': len(link.element_ids),
'Type': link.link_type,
'Status': link.status.value
})
pd.DataFrame(data).to_excel(output_path, index=False)
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.
- 9d ago First seen · 202 lines · 28 tokens per session scan A 1b8d2362619c
bim-to-schedule-4d is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (310 stars, last pushed 21d ago), licensed MIT. It adds 28 tokens to every session and 1,612 once invoked, about $0.0001 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 2026-09-03.
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