If this codebase is production, handles money, or touches sensitive data: treat this audit loop as a high-risk operation. Run with least privilege, avoid exporting long-lived credentials in your shell, and keep the agent in read-only mode.
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| --- | |
| name: lum-execute | |
| polytoken: | |
| tools: | |
| - ask_user_question | |
| - file_read | |
| - file_edit_search_replace | |
| - file_write | |
| - glob | |
| - grep |
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| --- | |
| name: code-review | |
| description: Review a pull request (or the current branch) with TWO parallel reviewer subagents on gpt-5.5(high) — a standard reviewer and an adversarial reviewer. Merge their findings only after both return; deliver an in-chat summary always and post to the PR only with consent. When reviewing your own branch, iterate on the feedback and re-invoke until no findings remain. | |
| disable-model-invocation: false | |
| tags: | |
| - productivity | |
| polytoken: true | |
| --- | |
| # Code Review |
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| { | |
| "did": "did:plc:s53e6k6sirobjtz5s6vdddwr" | |
| } |
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| openssl rand -base64 32 |
Use a blurry placeholder for images. It's a little bit harder when the images are unknown in advance, so we generate the blurhash string at runtime, and use several caches for the HTML and blurhash string.
We use the @substrate-system/blur-hash web component to do
the blur-up technique.
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| # Conformance suites: what they are, when they help, how to build one | |
| A conformance suite is a third axis of testing — distinct from unit tests | |
| and integration tests. Where unit tests prove that a function does what | |
| its body says, and integration tests prove that subsystems compose, a | |
| conformance suite proves that **what the public API claims it does | |
| matches what it actually does, against an external ground truth**. | |
| This document explains the pattern, when it earns its keep, and how to | |
| set one up. The case study is ferrotorch (a pure-Rust PyTorch |
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