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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

Examiner la source Voir sur GitHub

Prix non confirmé★ 21 Stars GitHubRegistre mis à jour · 15 sept. 2026agent-skill

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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.

Voir la source ↗

Métadonnées du fichier

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.

Voir le texte 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.

Examiner la source

Prix et coûts d’utilisation

Obtenir le skill: Prix non confirmé
L’utiliser: Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
Licence: MIT
Prix non confirmé: Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.

Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →

Source du skill enregistrée

Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.

Réviser avant installation: Éviter l’installation automatique

Licence: 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
L’approbation de revue IA est absente
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

Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

RépertoriéContrôle statique

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: a-dithya-b/agent-native-cli
Licence: MIT
Version: Unknown
Dernier push GitHub: 9 sept. 2026
Registre mis à jour: 15 sept. 2026
Chemin des instructions: skills/agent-native-cli/SKILL.md @ 92183375fc98

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

52/100

Revue nécessaire

Confiance

57/100

Do not auto-install

Audit

68/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

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
L’approbation de revue IA est absente
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: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

Accès agent

L’API Registry fournit les signaux de décision, confiance, audit, cas d’usage et installation sans analyser l’interface.

Ouvrir l’API d’installation Format texte LLM Ouvrir Manifest Plan de résolution auto

Plus de détails

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Pour le créateur

Source de la fiche

Indexé par Registry

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Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur: a-dithya-b
Source: a-dithya-b/agent-native-cli
Indexé par: Index communautaire OpenAgentSkill

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