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.
git clone --depth 1 https://github.com/doncheli/don-cheli-sddWrote 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/commands/doncheli/don-cheli-sdd/especificar)<a href="https://agentmods.dev/commands/doncheli/don-cheli-sdd/especificar"><img src="https://agentmods.dev/badge/commands/doncheli/don-cheli-sdd/especificar/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/commands/doncheli/don-cheli-sdd/especificar"><img src="https://agentmods.dev/badge/commands/doncheli/don-cheli-sdd/especificar.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 | $0.00090 | $0.02246 |
| Opus 5 | $0.00045 | $0.01123 |
| Sonnet 5 | $0.00018 | $0.00449 |
| Haiku 4.5 | $0.00009 | $0.00225 |
Grade A, and why
especificar 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 6d 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/dc:especificar
Objetivo
Convertir un requerimiento en lenguaje natural a una especificación Gherkin estructurada (.feature), la fuente de verdad principal de Don Cheli.
Output principal (siempre):
- Gherkin (.feature) — Comportamiento verificable con escenarios P1/P2/P3+
- DBML — Schema de base de datos provisional o ratificado
Output opcional (auto-generado si se detectan endpoints HTTP): 3. OpenAPI 3.1 (openapi.yaml) — Contratos de API extraídos desde los escenarios Gherkin 4. JSON Schema (schemas/*.json) — Estructura de datos derivada del DBML
Mejorado con el ciclo de vida DBML de Specular (constitution.md §I-B) Alineado con spec-kit — prioridades P1/P2/P3+, criterios de éxito y marcadores de clarificación Gherkin es Rey — OpenAPI y JSON Schema son complementos opcionales que se derivan del Gherkin, nunca al revés
Uso
/dc:especificar Tipo: <COMANDO|CONSULTA|EVENTO> Feature: <dominio/Nombre> Dominio: <dominio> Requerimiento: <descripción> Contexto: @<archivo-referencia>
Comportamiento
-
Detectar PRD — Si existe
.dc/prd/prd-*.md:- Leer el PRD y extraer automáticamente:
- User stories (sección 6.1) → base para escenarios Gherkin
- Prioridades MoSCoW → mapear a P1 (Must), P2 (Should), P3+ (Could/Won't)
- Riesgos (sección 8) → agregar escenarios de error/edge case por riesgo
- Requirements no funcionales (sección 6.2) → anotar como constraints
- Data model DBML (sección 6.4) → usar como schema base si existe
- Informar al usuario: "PRD detectado — extrayendo X user stories, Y riesgos"
- Si no hay PRD → continuar con el flujo normal (input del usuario)
- Leer el PRD y extraer automáticamente:
-
Verificar si existe
specs/db_schema/<dominio>.dbml- Si NO existe → Auto-generar DBML
@provisionalcon campos inferidos del requerimiento - Si existe y está ratificado → Usar como referencia, agregar nuevos campos como
@provisional
- Si NO existe → Auto-generar DBML
-
Analizar el requerimiento proporcionado
-
Identificar actores, acciones y resultados esperados
-
Categorizar escenarios por prioridad (P1/P2/P3+)
-
Generar escenarios Gherkin con Dado/Cuando/Entonces
-
Usar los nombres de campo EXACTOS del DBML en los escenarios
-
Marcar requisitos ambiguos con
[NECESITA CLARIFICACIÓN] -
Definir criterios de éxito medibles y agnósticos de tecnología
-
Crear archivo
.featureenspecs/features/<dominio>/con tag@borrador -
Incluir edge cases y escenarios de error
-
Generar checklist de requisitos vacía (
requisitos.md)
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.
- 6d ago First seen · 209 lines · 90 tokens per session scan A 7e7165b1151d
especificar is a command published in the GitHub repository doncheli/don-cheli-sdd (57 stars, last pushed 29d ago), licensed Apache-2.0. It adds 90 tokens to every session and 2,246 once invoked, about $0.0005 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 2026-09-03.
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verify-work
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test
A command for running a project's automated tests, which are checks that software behaves as expected. It detects common project types and chooses a matching test command.
qa
A quality-assurance testing agent that checks whether software meets its stated acceptance requirements. It tests normal use, edge cases, failures, and regressions, then classifies the problems it finds.