Review — filosofia.animal

Agent declares `community_building` template in meta.md. system.md contains all required sections per template spec: Identidad (Identity), Voz (Voice), Flujo (Flow), Calibracion (Calibration), Principios (Principles), Herramientas (Tools), and NO_RESPONSE rules. Content aligns with template intent (community engagement, no sales funnel).

examples.md contains 30 examples (exceeds 20-cap), but agent has keywords.json (non-empty), triggering keyword-trigger conditional. Keyword examples present (e.g., Example 24 shows FILAN mordedores keyword flow). All 7 base scenarios covered: happy path (Ex 1), resource delivery (Ex 3, 12), objection handling (Ex 16), graceful disqualification (Ex 20), emotional/sensitive (Ex 2, 25, 27), re-engagement (Ex 23), off-topic (Ex 7, 8, 9). However, count exceeds cap significantly. Recommend merging or retiring duplicates (e.g., Ex 14, 15 are near-duplicates of Ex 1 pattern).

All examples follow the flow defined in system.md: Saludo → Clasificar → Recomendar Comprender para Educar or derivar → Cerrar. Each example reaches a terminal state (link sent, NO_RESPONSE, or conversation closed). No examples end mid-flow.

system.md contains one potential tension: 'Saludo es sagrado' (greeting always first) vs. 'UNICA excepcion: si pregunta si sos bot/IA'. This is an explicitly named exception, not a contradiction. All other rules (Comprender para Educar as default, clases only if asked, no selling) are internally consistent with no shared precondition producing conflicting outputs.

All SDK files align on behavior and data. system.md's 'Comprender para Educar como recomendación principal' applies to general behavior; program.json's eligibility.NOT_for explicitly carves out 'convivencia entre perros' (inter-dog conflict) as a case requiring professional evaluation. Example 26 demonstrates this: convivencia conflictiva → derivar a equipo, NOT Comprender para Educar. Different preconditions (general behavior vs. inter-dog conflict), no shared precondition producing conflicting outputs. Keywords, resources, and examples all align with system.md rules.

All examples use consistent persona: warm, brief DM style (max 2 sentences), Spanish with informal register ('Dale', 'Que bueno!', 'Un gusto 💚'), no formal or clinical tone shifts. Emoji use consistent (💚 for closings, max 1-2 per message). Vocabulary and formality level uniform across all 30 examples.

All risky actions carry guards. 'Recomendar Comprender para Educar' is guarded by context (behavior questions). 'Derivar a clases' is guarded: 'Clases solo si piden' (system.md Flujo step 4). 'Derivar a equipo' for sensitive cases is guarded: 'Caso sensible' classification (step 2). 'Enviar link' is guarded by topic/context. Example 26 demonstrates the guard on convivencia: lead mentions inter-dog conflict → agent does NOT recommend Comprender para Educar, instead derives to equipo. Guards are stated and examples demonstrate correct firing.

All examples use current flow language and CTAs matching system.md. Comprender para Educar link is current (e-learning.filosofiaanimal.com/comprender-para-educar-2026-filo/). Clases link is current (filosofiaanimal.com/servicios/clases-adiestrador-perros-gatos-presencial/). WhatsApp links are current (wa.me/5491125235673 for community, wa.me/5492255551640 for carrera). No stale phrases or outdated CTAs detected.

Behavioral guidance (tone, engagement style, conversation flow) is expressed via Principios and examples. Hard rules are reserved for factual constraints (zone coverage, pricing, eligibility) and safety boundaries (no selling, no aversive tools, no minors). Example: 'Mensajes cortos, preferentemente ~30 palabras' is a Principle, not a hard rule; demonstrated in all examples. 'No selling' is a hard rule (Tier 1 boundary). Balance is appropriate.