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 ai-agent-orchestrationgit 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/ai-agent-orchestration)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/ai-agent-orchestration"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/ai-agent-orchestration/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/ai-agent-orchestration"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/ai-agent-orchestration.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 | $0.00050 | $0.00657 |
| Opus 5.5 | $0.00020 | $0.00263 |
| Sonnet 5.5 | $0.00010 | $0.00131 |
| Haiku 4.5 | $0.00005 | $0.00066 |
Grade A, and why
ai-agent-orchestration 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.
How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Orchestration for Construction (2026)
Why agents now
2026 construction automation is agentic: not single prompts, but specialized agents that own a domain (estimating, scheduling, documents, QA, safety), share a common data spine (the ERP + CWICR cost bases), and are coordinated by a supervisor with human checkpoints.
Agent roles
| Agent | Owns | Tools it calls |
|---|---|---|
| Estimator agent | BOQ + cost | CWICR search, QTO, market catalogs, costs API |
| Scheduler agent | Time (4D) | task graph, dependencies, critical path, resource leveling |
| Document agent | Specs & contracts | PDF/OCR extraction, clause NER, submittal/RFI routing |
| QA agent | Quality | validation rule packs (DIN276/NRM/GAEB), reconciliation checks |
| Safety agent | HSE | checklist generation, incident classification, regulations lookup |
| Supervisor agent | Orchestration | routes tasks, resolves conflicts, escalates to humans |
Coordination patterns
Supervisor ──► Estimator ──► BOQ draft ──► human approves
│ ▲
├──► Document ──► scope extracted (specs) ─┘
├──► Scheduler ──► draft schedule from BOQ quantities
└──► QA ──► validate BOQ + schedule, report violations
- Data spine first — all agents read/write the same ERP data (BOQ, tasks, cost items); no agent keeps private state.
- Human checkpoints — binding numbers (prices, contracts) always pass a human gate.
- Deterministic validation — QA uses arithmetic and rules, not LLM judgement, for reconciliation (e.g.
qty × price = cost, markup conventions). - Idempotent actions — every agent action is re-runnable (the ERP import is idempotent on
(code, region); use it as the model).
Guardrails
- Never let an agent invent a price: unpriced bases stay rate 0 until a market sheet exists.
- Confidence-scored matches below threshold go to a human.
- Log every agent decision with its inputs (the ERP's usage ledger pattern).
- EU AI Act (2024/1689): construction estimation assistance is low/limited risk, but keep human oversight for safety-critical decisions.
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 · 50 lines · 50 tokens per session scan A 3d741203ef80
ai-agent-orchestration 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 50 tokens to every session and 657 once invoked, about $0.0002 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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