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agent-hard-constraint-author

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Mis à jour15 juin 2026 à 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.

Installation

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Source

voku

voku/agent-learning

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·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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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.