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Guide development through test-driven development (TDD): write a test, confirm it fails, add minimal code, then clean up. Apply this cycle to features and bug fixes while checking that existing behavior still passes its tests.

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• Adding features with tests written first
• Reproducing a bug in a test before fixing it
• Refactoring while keeping tests passing

Investigate Python bugs by pausing execution, inspecting variables, and stepping through code. Choose pdb for local inspection or debugpy for remote connections, with guidance for failing tests, crash analysis, and already running processes.

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• Inspecting variables in a failing test
• Tracing changes to a collection inside a function
• Examining local variables at a crash site

Create or revise skills shipped within the Hermes Agent repository. The guidance covers file placement, required metadata, clear procedures, platform checks, tests, and documentation updates so contributions follow the repository’s authoring standards.

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• Add a reusable skill to the Hermes Agent repository
• Revise an existing in-repository SKILL.md
• Choose bundled or optional placement

Turn specifications and designs into working code using test-driven development: write failing tests, implement enough code to pass, then refactor. The workflow also covers error handling, performance improvements, documentation, and coordinated changes across components.

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• Implementing a feature from specifications and designs
• Developing authentication or APIs with tests
• Refactoring code while checking test results

Set up a realtime evaluation folder with a suitable shared harness, the framework that runs evaluations. Prepare prompts, tool definitions, realistic starter data, and a README, then validate the setup with evaluation and test runs.

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• Start a single-turn text-to-speech evaluation
• Set up evaluations using saved audio
• Build multi-turn simulations with mock tools

Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.

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• Compare agent quality before and after prompt changes
• Test tool selection and multi-step task execution
• Evaluate resistance to prompt injection

Update Flask-AppBuilder in Apache Airflow’s FAB provider, including pinned versions, dependency locks, and documentation. Check compatibility tests and review copied security code, with asset and database migration updates when the upstream release requires them.

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• Upgrade the FAB provider to a newer Flask-AppBuilder release
• Review upstream authentication fixes against Airflow's copied code
• Resolve compatibility failures after a dependency update

Set up Python projects with uv, manage required packages, and create isolated environments for each project. Covers Python versions, dependency updates, migration from existing tools, and lockfiles that record package versions for repeatable installations.

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• Set up a new Python project
• Migrate existing package management to uv
• Manage Python versions and isolated environments

Set up Python APIs with FastAPI using organized project templates. Build routes, asynchronous database operations, authentication, and error handling, with examples for sharing dependencies and testing endpoints through a separate test database.

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• Set up a new FastAPI project
• Build a Python API with asynchronous database access
• Organize authentication and shared dependencies

Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.

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• Adding self-review and revision loops to AI outputs
• Separating generation from evaluation
• Refining generated code using test results

Plan features before coding by defining requirements, acceptance criteria, and scope. Validate the specification, generate starter tests from expected behaviors, and guide implementation and review so each delivered feature can be checked against its agreed requirements.

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• Define requirements before implementing a feature
• Turn acceptance criteria into starter tests
• Check specifications for missing requirements

Configure OpenTelemetry to send AI application traces, records of execution steps, to Confident AI. Reuse existing exporters, choose the regional endpoint, and add AI-specific attributes without requiring the deepeval Python package.

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• Send existing AI application traces to Confident AI
• Configure tracing for a non-Python AI application
• Choose the US or EU endpoint for an API key

Add DeepEval tracing to Python AI applications so execution steps appear in Confident AI. Choose a supported integration or manual @observe instrumentation, then capture inputs, outputs, step types, tags, and metadata.

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• Trace execution steps in a Python AI application
• Select tracing integrations for an existing framework
• Add manual tracing with @observe

Merge GitHub pull requests while preserving credit for the original contributors. Covers straightforward merges, conflict resolution, selective file inclusion, duplicate PR closure, and checks that confirm both attribution and working code after integration.

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• Merge external contributions while preserving author credit
• Resolve conflicts in a pull request
• Merge selected files from a pull request

Build Python APIs with FastAPI and Pydantic V2, including input validation, database operations that avoid blocking other work, and token-based authentication. Implement REST or WebSocket endpoints, then check their behavior with automated tests and generated API documentation.

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• Build REST APIs with FastAPI
• Validate input data with Pydantic V2
• Implement JWT authentication and authorization

Checks cryptographic code against known attacks and edge cases using Project Wycheproof test vectors, predefined inputs and expected results. Helps load cases, interpret failures, compare implementations, and integrate checks into automated testing.

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• Testing cryptographic code against known attacks
• Comparing results across cryptographic libraries
• Investigating rejected or incorrectly accepted signatures

Set up Python projects and standalone scripts with uv for dependencies, ruff for code checks and formatting, and ty for type checking. Configure tests and project files, or migrate existing workflows when requested.

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• Starting a Python project or reusable package
• Writing a standalone script with external dependencies
• Configuring formatting, type checks, and tests

Wrap high-verbosity shell commands with RTK to reduce token consumption. Use when running git log, git diff, cargo test, pytest, or other verbose CLI output that wastes context window tokens.

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Run the test suite for the current repo, auto-detecting Python (pytest/uv), Node (vitest/pnpm), or Rust (cargo test)

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Implement and maintain Nexent's traditional Python UT for product changes and bugfixes under test/backend, test/sdk, and test/ext_components. Cover unit behavior, boundaries, errors and regression assertions with isolated pytest tests. Formal D1-D5 assets in test-e2e use nexent-test-assets.

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ALWAYS USE for test work that requires changes: write, add, generate, repair, or strengthen tests for existing code in xUnit, MSTest, NUnit, pytest, Vitest/Jest, Go, or another framework. Includes regression cases, failing or flaky tests, coverage-driven additions, and audit-then-fix requests. Focused work stays direct; broad or multi-stage work invokes test-engineer. DO NOT USE for only running tests, analysis-only audits, framework/platform migrations, a test blocked on a missing production seam (testability-obstacle), or MSTest API/configuration corrections that do not design new cases (writing-mstest-tests). Within an active test-engineer pipeline, reuse supplied guidance and do not re-enter this skill.

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Provides file paths to language-specific reference files for the test ANALYSIS skills (assertion-quality, test-anti-patterns, test-gap-analysis, test-smell-detection, test-tagging). Read this catalog to discover available extension files (e.g., dotnet.md for .NET/MSTest/xUnit/NUnit/TUnit, python.md for pytest/unittest, typescript.md for Jest/Vitest/Mocha, java.md for JUnit/TestNG, etc.). Do not invoke this reference-only skill — its bundled files are read by the quality agent and analysis skills needing framework-specific lookup tables (test markers, assertion APIs, skip annotations, sleep patterns, mystery guest indicators, integration markers, setup/teardown, tag-support capability).

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Analyze assertion quality, depth, variety, and false confidence in existing tests. ALWAYS USE when asked about weak, shallow, trivial, always-true, self-referential, assertion-free, presence/truthiness-only, or insufficiently diverse assertions, including MSTest, Jest, pytest, and Go. DO NOT USE for direct fixes: writing-mstest-tests owns supplied MSTest assertions; code-testing owns new cases. Use test-gap-analysis when asked whether tests would catch a production change, and test-anti-patterns for general severity-ranked audits.

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Run claude-tap end-to-end tests with pytest

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