athola

Computes DORA delivery-performance metrics from git and GitHub API. Use when assessing deployment frequency, lead time, or change failure rate.

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Assesses architecture decisions, ADR compliance, and coupling. Use when evaluating design changes or validating structural decisions before merging.

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Audits shell scripts for correctness, portability, and common pitfalls. Use when reviewing shell scripts or before committing shell changes.

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Extracts writing style patterns from exemplar text into a reusable profile. Use when creating a style guide or learning a specific author's voice.

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Detects shared stack membership and iterates a command across all PRs in base-to-tip order. Use when a command supports --stack flag for multi-PR iteration.

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Evaluates and improves skills, agents, commands, and hooks after a workflow slice. Use when execution felt slow, confusing, repetitive, or fragile.

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Applies NASA Power of 10 rules for safety-critical verifiable code. Use when auditing financial, medical, or high-reliability system code.

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Reviews pull requests with scope validation, requirements compliance, and line comments. Use when reviewing GitHub or GitLab PRs.

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Audits Rust code for unsafe blocks, ownership issues, and Cargo dependency risks. Use when reviewing Rust code or before merging Rust changes.

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Verifies workspace state and staged changes as a read-only preflight. Use before commits or PRs to confirm staged set is clean and correct.

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Runs a three-tier codebase audit (git history, targeted scans, full review) with gating. Use when auditing a codebase before release or after incidents.

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Orchestrates multi-domain review (code, arch, tests, security) in a single pass. Use when thorough pre-release review is needed.

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Python testing patterns with pytest, fixtures, TDD, mocking, async and integration tests. Use when writing or auditing a Python test suite.

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Automates desktop GUI workflows via computer use API with screenshot capture. Use when scripting GUI interactions or recording browser sessions for tutorials.

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Verifies math-heavy code for algorithmic correctness and numerical stability. Use when reviewing scientific algorithms, ML models, or numerical code.

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Detects AI-generated writing patterns in prose. Use when reviewing docs for slop, vague language, or identity leaks before publishing.

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Prepares pull requests by running quality gates, drafting descriptions, and validating tests. Use when completing a feature and ready for review.

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Merges ephemeral report and analysis artifacts into permanent documentation. Use when LLM-generated markdown files have accumulated and need consolidation.

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Profiles Python code for performance bottlenecks and memory issues. Use when Python code is slow or when profiling for optimization before a release.

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Applies an ASD-STE100-derived register to operator and procedural text. Use when writing runbooks or steps. Do not use for docstrings, ADRs, or rationale.

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Bumps versions, updates changelogs, and coordinates version changes across files for releases. Use when preparing a release or bumping the project version.

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Updates, generates, and validates tests using git-workspace context and TDD/BDD methodology. Use when code changes require new or updated test coverage.

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Applies NIST/CWE security hardening to Python and Rust code. Use when auditing code for vulnerabilities or proposing concrete security remediations.

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Decompose a session into typed handoff units and recall them later. Use when ending a session or resuming work whose prior state must be recovered.

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