Indexado en Registry

agent-native-cli

Design and improve CLI, scripts, Make targets, and tool interfaces for AI coding agents. Use when a repository exposes repetitive shell workflows, noisy command output, fragile multi-step operations, or tooling that could be made more deterministic, discoverable, structured, and

Revisar el código fuente Ver en GitHub

Precio sin confirmar★ 21 Estrellas de GitHubRegistro actualizado · 15 sept 2026agent-skill

Resumen

Leer documentación completa

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

Agent-Native CLI

Treat the command/tool boundary as an interface for an agent, not just a way to expose shell commands.

Core approach

When designing or reviewing a workflow:

Inspect the repository's existing commands, scripts, CI, and development conventions first.
Identify repeated multi-step operations and mechanical work.
Move deterministic work out of the model and into scripts, Make targets, or higher-level CLI commands.
Give the agent a small, semantic command surface.
Minimize output crossing the tool → model boundary.
Return only information needed for the agent's next decision.
Use structured output when the agent needs to inspect fields or make decisions.
Use exit codes and explicit failure semantics for machine-detectable outcomes.
Make side effects, required inputs, and destructive behavior explicit.
Keep commands discoverable and composable.
Preserve human usability; do not optimize for agents by making normal development harder.

Important distinction

Optimize both sides of the boundary:

Action compression:
agent → one semantic command → many deterministic operations

Observation compression:
many lines of tool output → small decision-relevant result → agent

Short commands alone are not enough. A command that returns thousands of irrelevant lines can still be expensive for an agent.

Agent vs deterministic tooling

Keep the agent responsible for decisions that require context or judgment:

Agent:
what should happen?
when should it happen?
which option is appropriate?

Deterministic tooling:
how should the known procedure execute?
what exact commands are required?
how should known results be summarized?

Do not hide meaningful decisions inside scripts merely to reduce model interaction.

Output contract

Prefer a deliberate separation between the primary agent-facing result and diagnostics:

stdout → compact result / next-decision information
exit code → machine-detectable success or failure
stderr/logs → detailed diagnostics when needed

Do not discard actionable errors merely to reduce tokens. Preserve a path to full diagnostics without forcing them into every successful tool response.

Design checklist

For each candidate command, consider:

Is the operation deterministic enough to move outside the model?
Can several low-level commands become one semantic operation?
What is the smallest useful input surface?
What decisions still belong to the agent?
What output does the agent actually need?
Can stdout be reduced without hiding important failures?
Should the result be structured?
Are exit codes meaningful?
Are side effects explicit and safe?
Can detailed diagnostics be retrieved separately?
Can the agent discover how to use it without reading implementation details?
Does the interface remain convenient for humans?

Working method

When asked to improve a repository:

Inspect before changing.
Reuse existing repository mechanisms where practical.
Identify a small set of high-value workflows.
Implement the smallest useful command surface.
Preserve existing behavior and CI unless the task explicitly calls for a change.
Document the command contract and discoverability path.
Measure the result separately; do not claim token savings without an actual measurement.

References

Read these when relevant:

references/command-abstraction.md
references/compact-output.md
references/structured-output.md
references/deterministic-workflows.md

Do not over-engineer

Prefer existing repository mechanisms first. A Make target, shell script, package script, or small CLI command is often enough. Do not introduce a framework merely to satisfy this skill.

Ver fuente ↗

Metadatos del archivo

name: agent-native-cli
description: Design and improve CLI, scripts, Make targets, and tool interfaces for AI coding agents. Use when a repository exposes repetitive shell workflows, noisy command output, fragile multi-step operations, or tooling that could be made more deterministic, discoverable, structured, and token-efficient.

Ver texto original

---
name: agent-native-cli
description: Design and improve CLI, scripts, Make targets, and tool interfaces for AI coding agents. Use when a repository exposes repetitive shell workflows, noisy command output, fragile multi-step operations, or tooling that could be made more deterministic, discoverable, structured, and token-efficient.
---

# Agent-Native CLI

Treat the command/tool boundary as an interface for an agent, not just a way to expose shell commands.

## Core approach

When designing or reviewing a workflow:

1. Inspect the repository's existing commands, scripts, CI, and development conventions first.
2. Identify repeated multi-step operations and mechanical work.
3. Move deterministic work out of the model and into scripts, Make targets, or higher-level CLI commands.
4. Give the agent a small, semantic command surface.
5. Minimize output crossing the tool → model boundary.
6. Return only information needed for the agent's next decision.
7. Use structured output when the agent needs to inspect fields or make decisions.
8. Use exit codes and explicit failure semantics for machine-detectable outcomes.
9. Make side effects, required inputs, and destructive behavior explicit.
10. Keep commands discoverable and composable.
11. Preserve human usability; do not optimize for agents by making normal development harder.

## Important distinction

Optimize both sides of the boundary:

```text
Action compression:
agent → one semantic command → many deterministic operations

Observation compression:
many lines of tool output → small decision-relevant result → agent
```

Short commands alone are not enough. A command that returns thousands of irrelevant lines can still be expensive for an agent.

## Agent vs deterministic tooling

Keep the agent responsible for decisions that require context or judgment:

```text
Agent:
what should happen?
when should it happen?
which option is appropriate?

Deterministic tooling:
how should the known procedure execute?
what exact commands are required?
how should known results be summarized?
```

Do not hide meaningful decisions inside scripts merely to reduce model interaction.

## Output contract

Prefer a deliberate separation between the primary agent-facing result and diagnostics:

```text
stdout → compact result / next-decision information
exit code → machine-detectable success or failure
stderr/logs → detailed diagnostics when needed
```

Do not discard actionable errors merely to reduce tokens. Preserve a path to full diagnostics without forcing them into every successful tool response.

## Design checklist

For each candidate command, consider:

- Is the operation deterministic enough to move outside the model?
- Can several low-level commands become one semantic operation?
- What is the smallest useful input surface?
- What decisions still belong to the agent?
- What output does the agent actually need?
- Can stdout be reduced without hiding important failures?
- Should the result be structured?
- Are exit codes meaningful?
- Are side effects explicit and safe?
- Can detailed diagnostics be retrieved separately?
- Can the agent discover how to use it without reading implementation details?
- Does the interface remain convenient for humans?

## Working method

When asked to improve a repository:

1. Inspect before changing.
2. Reuse existing repository mechanisms where practical.
3. Identify a small set of high-value workflows.
4. Implement the smallest useful command surface.
5. Preserve existing behavior and CI unless the task explicitly calls for a change.
6. Document the command contract and discoverability path.
7. Measure the result separately; do not claim token savings without an actual measurement.

## References

Read these when relevant:

- `references/command-abstraction.md`
- `references/compact-output.md`
- `references/structured-output.md`
- `references/deterministic-workflows.md`

## Do not over-engineer

Prefer existing repository mechanisms first. A Make target, shell script, package script, or small CLI command is often enough. Do not introduce a framework merely to satisfy this skill.

Revisar el código fuente

Precio y costes de ejecución

Obtener el skill: Precio sin confirmar
Ejecutarlo: Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
Licencia: MIT
Precio sin confirmar: No hemos confirmado el precio. Los enlaces existentes al código y a la instalación siguen disponibles.

Obtener gratis no significa ejecutar gratis. El precio no es una evaluación de seguridad. Enviar información de precio →

Fuente del skill registrada

La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.

Revisar antes de instalar: Evitar instalación automática

Licencia: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Low GitHub adoption signal
Falta aprobación de revisión por IA
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 1 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Abrir auditoría completa

Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.

Empieza con una tarea pequeña

1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.

Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

IndexadoRevisión estática

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente: a-dithya-b/agent-native-cli
Licencia: MIT
Versión: Unknown
Último push de GitHub: 9 sept 2026
Registro actualizado: 15 sept 2026
Ruta de instrucciones: skills/agent-native-cli/SKILL.md @ 92183375fc98

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

52/100

Requiere revisión

Confianza

57/100

Do not auto-install

Auditoría

68/100

Requiere revisión

Abrir auditoría completa Ver informe de evaluación

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Low GitHub adoption signal
Falta aprobación de revisión por IA
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 1 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Resultados: —

Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.

Acceso para agentes

La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.

Abrir API de instalación Formato de texto LLM Abrir Manifest Plan de resolución automática

Más detalles

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Creador: a-dithya-b
Fuente: a-dithya-b/agent-native-cli
Indexado por: Índice comunitario de OpenAgentSkill

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