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glyphtrail
Perfil de creador de GitHub

glyphtrail

Vista por repositorio de 3 skills recopiladas en 1 repositorios de GitHub.

skills recopiladas
3
repositorios
1
actualizado
2026-06-18
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Repositorios principales por número de skills recopiladas, con su participación en este catálogo del creador y su variedad ocupacional.

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Repositorios y skills representativas

glyphtrail
Desarrolladores de software

Query this repo's code graph instead of ls/grep/reading files blindly. Use whenever you are understanding the codebase or its architecture, tracing how something works, or searching for a symbol or term; before changing a symbol, to get its blast radius (dependent symbols, tests to run, API surface, and other indexed repos affected); and when wiring two repos together across an API boundary (resolving unmatched client calls against a producer's OpenAPI/proto/GraphQL spec into precise link hints). Reach for this over grep even when the task does not name glyphtrail: code understanding, "where is X", "what calls X", "what breaks if I change X", and cross-repo/API questions all belong here.

2026-06-18
glyphtrail
Desarrolladores de software

Query this repo's code graph instead of ls/grep/reading files blindly. Use whenever you are understanding the codebase or its architecture, tracing how something works, or searching for a symbol or term; before changing a symbol, to get its blast radius (dependent symbols, tests to run, API surface, and other indexed repos affected); and when wiring two repos together across an API boundary (resolving unmatched client calls against a producer's OpenAPI/proto/GraphQL spec into precise link hints). Reach for this over grep even when the task does not name glyphtrail: code understanding, "where is X", "what calls X", "what breaks if I change X", and cross-repo/API questions all belong here.

2026-06-18
glyphtrail-atlas
Desarrolladores de software

Use when working with the glyphtrail atlas: the personal, cross-repo commit history graph and its embeddings (semantic repo/commit similarity). Covers syncing, embedding (local or OpenAI), similarity search, and - importantly - how the (paid) embeddings are persisted, survive upgrades, and are backed up to Parquet for recovery or external analysis.

2026-06-12
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