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Ejecuta cualquier Skill en Manus con un clic

agent-hard-constraint-author

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Actualizado15 de junio de 2026 a las 08:53

Turn validated agent-learning findings into human-approved executable constraints such as PHPStan rules, PHP-CS-Fixer rules, tests, or CI checks, with generation packages, manifests, fixtures, and validation evidence.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

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Fuente

voku

voku/agent-learning

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Desarrolladores de softwareOcupaciones informáticas y matemáticas·SOC 15-1252

SKILL.md

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name	agent-hard-constraint-author
description	Turn validated agent-learning findings into human-approved executable constraints such as PHPStan rules, PHP-CS-Fixer rules, tests, or CI checks, with generation packages, manifests, fixtures, and validation evidence.

Agent Hard Constraint Author

Use this skill when repeated learning should become an executable check. The point is to move stable behavior from prose into a rule that can fail validation.

Fast Path

Start from validated findings. Require several independent findings or one clearly documented critical incident.
Define the objective violation, scope, allowed boundaries, detectability, false-positive risk, validation commands, target rule path, registration files, and example rule paths.
Export a generation package before writing code:

vendor/bin/agent-learning constraint-export \
  --root infra/doc/agent-learning \
  --proposal proposal.2026-06-15.001

Generate or implement the rule in the consuming project, not in this package, unless the rule is package-generic.
Include valid, invalid, boundary, and false-positive fixtures where the engine supports fixtures.
Register the rule in the consuming project's analyzer configuration.
Validate both directions: the invalid fixture must fail with the intended rule identifier, and the valid fixture plus real target files must pass.
Record validation evidence and content hashes before marking the proposal applied.
Activate the manifest only after implementation and validation:

vendor/bin/agent-learning constraint-loop \
  --root infra/doc/agent-learning \
  proposal.2026-06-15.001 \
  --by agent \
  --commit working-tree \
  --validation infra/doc/agent-learning/validation-results/proposal.2026-06-15.001.json

Quality Bar

Prefer PHPStan or PHP-CS-Fixer when the violation is statically detectable.
Keep rule identifiers stable and engine-valid.
Make selection scope explicit; recall uses scope overlap, not semantic similarity.
Do not activate generated constraints silently.
Do not accept "file exists" or exit code alone as proof; inspect generated artifacts and fixture behavior.

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Agent Hard Constraint Author

Use this skill when repeated learning should become an executable check. The point is to move stable behavior from prose into a rule that can fail validation.

Fast Path

Start from validated findings. Require several independent findings or one clearly documented critical incident.

Define the objective violation, scope, allowed boundaries, detectability, false-positive risk, validation commands, target rule path, registration files, and example rule paths.

Export a generation package before writing code:

vendor/bin/agent-learning constraint-export \ --root infra/doc/agent-learning \ --proposal proposal.2026-06-15.001

Generate or implement the rule in the consuming project, not in this package, unless the rule is package-generic.

Include valid, invalid, boundary, and false-positive fixtures where the engine supports fixtures.

Validate both directions: the invalid fixture must fail with the intended rule identifier, and the valid fixture plus real target files must pass.

Record validation evidence and content hashes before marking the proposal applied.

Activate the manifest only after implementation and validation:

vendor/bin/agent-learning constraint-loop \ --root infra/doc/agent-learning \ proposal.2026-06-15.001 \ --by agent \ --commit working-tree \ --validation infra/doc/agent-learning/validation-results/proposal.2026-06-15.001.json

Quality Bar

Prefer PHPStan or PHP-CS-Fixer when the violation is statically detectable.

Keep rule identifiers stable and engine-valid.

Make selection scope explicit; recall uses scope overlap, not semantic similarity.

Do not activate generated constraints silently.

Do not accept "file exists" or exit code alone as proof; inspect generated artifacts and fixture behavior.