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 danielgap/openclaw-planitor --skill data-extractorgit clone --depth 1 https://github.com/danielgap/openclaw-planitorWrote 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/danielgap/openclaw-planitor/data-extractor)<a href="https://agentmods.dev/skills/danielgap/openclaw-planitor/data-extractor"><img src="https://agentmods.dev/badge/skills/danielgap/openclaw-planitor/data-extractor/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/danielgap/openclaw-planitor/data-extractor"><img src="https://agentmods.dev/badge/skills/danielgap/openclaw-planitor/data-extractor.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 | $0.00037 | $0.01366 |
| Opus 5.5 | $0.00015 | $0.00546 |
| Sonnet 5.5 | $0.00007 | $0.00273 |
| Haiku 4.5 | $0.00004 | $0.00137 |
Grade A, and why
data-extractor scanned grade A with 1 finding 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 2mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**Fallback chain:** curl → Jina AI → SearXNG → Lightpanda → Scrapling stealthy How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Data Extractor para Planitor
Descripción
Coordina la extracción de datos reales delegando a Extractor (rápido) y Scout (profundo).
No ejecuta scraping directamente — prepara prompts y delega vía sessions_spawn.
Ver skills/web-research/SKILL.md para el arsenal completo de herramientas disponibles.
Arsenal de Investigación (resumen)
| Categoría | Herramientas | Qué obtienes |
|---|---|---|
| Búsqueda | Tavily, SearXNG, Jina AI | Información general, URLs, artículos |
| Scraping | Lightpanda, Scrapling, Camoufox | Datos de webs protegidas, reseñas |
| Empresas ES | registro-empresas-es, company-research | CIF, cuentas, administradores, BORME |
| Redes | twitter-extract, reddit-readonly, Google Reviews | Actividad competidores, opinión |
| Tendencias | trends-checker, market-analysis | Demanda Google Trends |
| OSINT | Sherlock, theHarvester | Perfiles sociales, emails, dominios |
| Subvenciones | subvencion-watcher | Ayudas disponibles |
Fallback chain: curl → Jina AI → SearXNG → Lightpanda → Scrapling stealthy
Extracciones por Fase
Fase 2: Ground Truth
Delegar a Extractor (3 queries rápidas):
1. "INE padrón municipal [ciudad] población hecho derecho"
→ Herramienta: Tavily / SearXNG
2. "competidores [tipo negocio] en [ciudad] URLs precios"
→ Herramienta: SearXNG (URLs verificables) + Google Reviews (reseñas)
→ Complementar con: registro-empresas-es (datos societarios)
3. "precio alquiler local comercial [m2] [ciudad] idealista fotocasa"
→ Herramienta: Tavily / SearXNG + Jina AI (artículos)
Delegar a Scout (investigación profunda):
"Benchmarks revenue [tipo negocio]: ticket medio, ocupación media,
márgenes típicos, costes estándar"
→ Herramientas: SearXNG + web research + fuentes sectoriales
Fase 3: Market Analysis
Delegar a Extractor:
1. SearXNG: "[tipo negocio] [ciudad]" → URLs competidores
2. trends-checker: Google Trends para demanda del sector
3. reddit-readonly: "[sector] [ciudad] opiniones" → insights del sector
4. subvencion-watcher: subvenciones disponibles
What ships with it
1 file 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.
- 2mo ago First seen · 147 lines · 37 tokens per session scan A fa1d476b16a9
data-extractor is a skill published in the GitHub repository danielgap/openclaw-planitor (5 stars, last pushed 6mo ago), licensed MIT. It adds 37 tokens to every session and 1,366 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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