Search: "pass"

Runs an autonomous review-and-fix improvement loop over the current branch's changes until a PR review comes back clean, scoped mechanically to the directories the branch touched. Reviews are performed by an installed PR-review skill (default: pr-review-toolkit's review-pr). Use to fix review findings on a branch before opening or updating a pull request ('clean up this branch', 'fix this PR until review passes', 'run review-and-fix on my changes'). NOT for a one-time review — run the PR-review skill directly.

Runs an autonomous review-and-fix improvement loop over any code target — a skill, plugin, module, or directory — using a reviewer the user names: any installed skill or agent. Keeps a cross-round findings ledger, escalates when fixes stop converging, and guards scope mechanically. Use when asked to 'improve this code until review passes', 'run an improvement loop with <reviewer>', or to iterate review-and-fix with a specific reviewer. For skills prefer the skill-improver entry; for a branch prefer pr-improver.

Triages a repository's open GitHub issues and pull requests via the gh CLI. Optionally reviews and merges ready PRs — incrementally merging passing automated/bot PRs and maintainer-approved ones, and spawning review subagents for never-reviewed ones — then closes already-resolved issues with comments citing the resolving PR or commit, cross-links issues with their pending fix PRs, and assigns local-only priority and change-size estimates for everything outstanding. Use when triaging, grooming, or reviewing a repository's open issues and PRs.

Detects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis, and control-flow verification. Use for auditing C/C++/Rust code handling secrets, keys, passwords, or other sensitive data.

Runs an autonomous review-and-fix improvement loop over a Claude Code skill until a review comes back clean, with a cross-round findings ledger, escalation when fixes stop converging, and a mechanical scope guard. Reviews are performed by the plugin-dev skill-reviewer agent. Use to fix skill quality issues, iteratively refine a skill, or resume a loop after an escalation ('fix my skill', 'improve this skill until it passes review', 'skill improvement loop'). NOT for a one-time review — use the plugin-dev skill-reviewer agent directly.

Validates cryptographic implementations against Project Wycheproof's test vectors, which encode known attacks and edge cases across AES, RSA, ECDSA, ECDH, and more. Covers loading test vectors, mapping result flags onto pass and fail expectations, and reading a failure. Use when testing a crypto implementation against known attacks, checking a library against standard test vectors, or investigating why two implementations disagree on the same input.

Understand a codebase before looking for bugs in it - what each function assumes, what it guarantees, and what it depends on elsewhere. Use when starting an audit, threat model, or architecture review on unfamiliar code, and before any vulnerability-hunting pass.

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work

Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

Supports work with Outpost Bio's open microbiome foundation models - the Waypoint checkpoints (Waypoint-6m, Waypoint-45m, Waypoint-170m), the Atlas pretraining corpus, the Compass eight-task benchmark, or the `waypoint` CLI from the `waypoint-bio` package. Covers embedding microbiome samples, fine-tuning on taxonomic abundance data, benchmarking a checkpoint on Compass, pretraining a GPT-2 model on taxonomic abundance profiles, and converting MetaPhlAn, Kraken2, QIIME 2, or MGnify abundance tables into waypoint format.

Supports PyTorch Geometric (PyG) graph neural networks — node/link/graph classification, message passing (GCN, GAT, GraphSAGE, GIN), heterogeneous graphs, neighbor sampling, and custom datasets. Use when working with torch_geometric, not for general NetworkX analytics or non-graph PyTorch models.

Builds and queries multi-language source and binary code graphs for security analysis. Includes pre-analysis passes for blast radius, taint propagation, privilege boundaries, entry point enumeration, proxy/unresolved-call tracking, type/reference queries, structural traversal, graph diffs, audit augmentation, declared cross-language/FFI/external links via `.trailmark/links.toml`, and SQL schema graphs. Use when analyzing call paths, mapping attack surface, finding complexity hotspots, enumerating entry points, tracing taint propagation, measuring blast radius, importing SARIF/weAudit/binary findings, linking source graphs across language or RPC boundaries, or building a code graph for audit prioritization. Feature-gate version-specific Trailmark APIs before using them; prefer `trailmark.parse.detect_languages()` or `--language auto` when the target language is unknown or polyglot.