code-review-context

$npx mdskill add openai/codex/code-review-context

Inject bounded context fragments for model inference.

  • Enables incremental history building without cache misses.
  • Requires struct definitions in core/context implementing ContextualUserFragment trait.
  • Flags fragments exceeding 1k tokens for mandatory manual review.
  • Enforces hard size caps preventing items larger than 10K tokens.
SKILL.md
.github/skills/code-review-contextView on GitHub ↗
---
name: code-review-context
description: Model visible context
---

Codex maintains a context (history of messages) that is sent to the model in inference requests.

1. No history rewrite - the context must be built up incrementally.
2. Avoid frequent changes to context that cause cache misses.
3. No unbounded items - everything injected in the model context must have a bounded size and a hard cap. 
4. No items larger than 10K tokens.
5. Highlight new individual items that can cross >1k tokens as P0. These need an additional manual review.
6. All injected fragments must be defined as structs in `core/context` and implement ContextualUserFragment trait
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