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 semantic-search-cwicrgit 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/semantic-search-cwicr)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/semantic-search-cwicr"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/semantic-search-cwicr/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/semantic-search-cwicr"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/semantic-search-cwicr.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 | $0.00063 | $0.01817 |
| Opus 5 | $0.00032 | $0.00908 |
| Sonnet 5 | $0.00013 | $0.00363 |
| Haiku 4.5 | $0.00006 | $0.00182 |
Grade A, and why
semantic-search-cwicr 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 13d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semantic Search in DDC CWICR Database
Business Case
Problem Statement
Construction cost estimation requires finding relevant work items from large databases. Traditional keyword search fails when:
- Users describe work in natural language
- Terminology varies across regions and languages
- Similar work items have different naming conventions
Solution
DDC CWICR provides pre-computed embeddings (BAAI/bge-m3, 1024 dimensions) enabling multilingual semantic search across 8 national bases (78,228 positions) plus the 30-market global base in 26 languages, with 48 PPP-repriced market catalogs per national base.
Business Value
- 90% faster work item lookup compared to manual search
- Multi-language: Arabic, Bulgarian, Chinese, Croatian, Czech, Danish, Dutch, English, Finnish, French, German, Hindi, Indonesian, Italian, Japanese, Korean, Mongolian, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Thai, Turkish, Vietnamese
- Higher accuracy by finding semantically similar items, not just keyword matches
Data landscape (2026)
| National base | Region id | Positions |
|---|---|---|
| Turkey (Birim Fiyat) | TR_NATIONAL |
22,704 |
| China (Beijing Dinge + Bole) | ZH_CHINA |
11,312 |
| Brazil (SINAPI) | BR_NATIONAL |
9,723 |
| Spain (BCCA Andalucía) | ES_ANDALUCIA |
6,453 |
| Italy (Prezzario Toscana) | IT_TOSCANA |
5,836 |
| Vietnam (Dinh Muc) | VN_NATIONAL |
4,299 |
| Indonesia (AHSP) | ID_NATIONAL |
2,784 |
| Greece (GGDE) | GR_NATIONAL |
2,647 |
Each base ships the 95-column CWICR master schema (rate_code, rate_original_name, rate_final_name, rate_unit, total_cost_per_position, classification hierarchy collection/department/section/subsection/category, resource_* component lines with is_material/is_machine/is_labor flags) plus 26 language editions and 48 markets/*.csv catalogs.
Latest data release: v0.4.0 (see releases).
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.
- 13d ago First seen · 149 lines · 63 tokens per session scan A 0ec7d70ac68b
semantic-search-cwicr 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 63 tokens to every session and 1,817 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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