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 pipeline-ground-truthgit 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/pipeline-ground-truth)<a href="https://agentmods.dev/skills/danielgap/openclaw-planitor/pipeline-ground-truth"><img src="https://agentmods.dev/badge/skills/danielgap/openclaw-planitor/pipeline-ground-truth.svg" alt="Measured on agentmods" 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 | $0.00031 | $0.00915 |
| Opus 5 | $0.00015 | $0.00458 |
| Sonnet 5 | $0.00006 | $0.00183 |
| Haiku 4.5 | $0.00003 | $0.00092 |
Grade A, and why
pipeline-ground-truth 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 8d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pipeline Ground Truth — GroundTruthAgent
Input
| Archivo | Descripción |
|---|---|
BRIEF.json |
Brief de la Fase 1 |
Output
projects/{proyecto}/v{n}/GROUND-TRUTH.json con supuestos clasificados ✅/⚠️/❓.
Categorías:
- Supuestos de Mercado (tamaño, segmento, tendencia)
- Supuestos de Producto/Servicio (precio, coste, volumen)
- Supuestos de Costes (fijos, inversión, CAC)
- Supuestos de Ventas (timeline, crecimiento, LTV)
- Datos Reales (si negocio existe: revenue, clientes, ticket medio)
Cada supuesto: { valor, fuente, estado: "✅"|"⚠️"|"❓" }
Frameworks
- Business Plan Validation — Problem-Solution Fit
- Financial Modeling — unit economics assumptions
Prompt Guía
Lee projects/{proyecto}/v{n}/BRIEF.json y genera el Ground Truth.
Crea un documento con 5 categorías de supuestos:
1. Supuestos de Mercado (tamaño, segmento, tendencia)
2. Supuestos de Producto/Servicio (precio, coste, volumen)
3. Supuestos de Costes (fijos, inversión, CAC)
4. Supuestos de Ventas (timeline, crecimiento, LTV)
5. Datos Reales (si el negocio existe: revenue, clientes, ticket medio, estacionalidad)
6. **Estacionalidad Local** (OBLIGATORIO): ¿La ciudad es turística o emisora? ¿Se vacía en verano? Buscar datos INE de población de hecho vs derecho. NO asumir patrones genéricos — cada ciudad tiene su propia estacionalidad.
Cada supuesto tiene: valor, fuente, estado (✅/⚠️/❓)
Si el negocio ya existe, los datos históricos SON el ground truth.
### Análisis de Competencia con Reseñas Reales (OBLIGATORIO)
Para cada competidor identificado:
1. Extraer reseñas de Google Maps usando el script de Extractor:
xvfb-run -a --server-args="-screen 0 1920x1080x24" python3 ~/.openclaw/workspace/agents/extractor/skills/google-reviews-extract/google_reviews_camoufox.py "Nombre Competidor" "Ciudad" --max 10
2. Datos societarios del competidor (registro-empresas-es):
Buscar en Infocif, LibreBOR, etc.
Ver skills/registro-empresas-es/SKILL.md
Útil para: CIF, fecha constitución, administradores, capital social, estado
3. Incluir en GROUND-TRUTH.json los datos extraídos:
- `rating_google_maps`: nota media
- `total_reviews_google_maps`: total de reseñas
- `reviews_sample`: array con las reseñas extraídas (autor, rating, fecha, texto)
- `insights_competencia`: análisis cualitativo (fortalezas, debilidades, quejas recurrentes)
- `datos_societarios`: CIF, administradores, estado, capital (si disponible)
4. Fuentes adicionales para reseñas (si Google Reviews falla):
- TripAdvisor: scraping con fallback chain
- SearXNG: `python3 skills/searxng-search/searxng-search.py "[competidor] review [ciudad]"`
- Reddit: `node skills/reddit-readonly/reddit-readonly.mjs "[competidor] [ciudad]"`
- Foros sectoriales (Escapistas.CLUB, TodoEscapeRooms, etc.)
5. NO inventar reseñas ni puntuaciones — solo datos extraídos
Guarda como GROUND-TRUTH.json en projects/{proyecto}/v{n}/
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.
- 8d ago First seen · 86 lines · 31 tokens per session scan A 65fa0a13f237
pipeline-ground-truth is a skill published in the GitHub repository danielgap/openclaw-planitor (5 stars, last pushed 4mo ago), licensed MIT. It adds 31 tokens to every session and 915 once invoked, about $0.0002 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 2026-08-31.
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