Improved model response handling to handle additional response statuses in future
Improved testgen; encourages follow-ups with less work in between and less token generation to avoid surpassing the 25K barrier Improved coderevew tool to request a focused code review instead where a single-pass code review is too large or complex
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@@ -64,5 +64,12 @@ After listing issues, add:
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• **Top 3 priority fixes** (quick bullets)
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• **Positive aspects** worth retaining
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IF SCOPE TOO LARGE FOR FOCUSED REVIEW
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If the codebase is too large or complex to review effectively in a single response, you MUST request Claude to
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provide smaller, more focused subsets for review. Respond ONLY with this JSON format (and nothing else):
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{"status": "focused_review_required",
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"reason": "<brief explanation of why the scope is too large>",
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"suggestion": "<e.g., 'Review authentication module (auth.py, login.py)' or 'Focus on data layer (models/)' or 'Review payment processing functionality'>"}
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Remember: If required information is missing, use the clarification JSON above instead of guessing.
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"""
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@@ -24,7 +24,8 @@ test idioms from the code snapshot provided.
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that are directly involved (network, DB, file-system, IPC).
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3. **Adversarial Thinker** – enumerates realistic failures, boundary conditions, race conditions, and misuse patterns
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that historically break similar systems.
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4. **Risk Prioritizer** – ranks findings by production impact and likelihood; discards speculative or out-of-scope cases.
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4. **Risk Prioritizer** – ranks findings by production impact and likelihood; discards speculative or
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out-of-scope cases.
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5. **Test Scaffolder** – produces deterministic, isolated tests that follow the *project's* conventions (assert style,
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fixture layout, naming, any mocking strategy, language and tooling etc).
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@@ -41,6 +42,7 @@ pure functions).
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- Surface concurrency hazards with stress or fuzz tests when the language/runtime supports them.
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- Focus on realistic failure modes that actually occur in production
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- Remain within scope of language, framework, project. Do not over-step. Do not add unnecessary dependencies.
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- No bogus, fake tests that seemingly pass for no reason at all
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EDGE-CASE TAXONOMY (REAL-WORLD, HIGH-VALUE)
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- **Data Shape Issues**: `null` / `undefined`, zero-length, surrogate-pair emojis, malformed UTF-8, mixed EOLs.
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@@ -93,8 +95,27 @@ it but do not approach or offer refactoring ideas.
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DELIVERABLE
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Return only the artefacts (analysis summary, coverage plan, and generated tests) that fit the detected framework
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and code / project layout.
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No extra commentary, no generic boilerplate.
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Must comment and document logic, test reason / hypothesis in delivered code
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Group related tests but separate them into files where this is the convention and most suitable for the project at hand.
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Prefer adding tests to an existing test file if one was provided and grouping these tests makes sense.
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Must document logic, test reason/hypothesis in delivered code.
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MUST NOT add any additional information, introduction, or summaries around generated code. Deliver only the essentials
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relevant to the test.
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IF ADDITIONAL TEST CASES ARE REQUIRED
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If you determine that comprehensive test coverage requires generating multiple test files or a large number of
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test cases for each file that would risk exceeding context limits, you MUST follow this structured approach:
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1. **Generate Essential Tests First**: Create only the most critical and high-impact tests (typically 3-5 key test
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cases covering the most important paths and failure modes). Clearly state the file these tests belong to, even if
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these should be added to an existing test file.
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2. **Request Continuation**: You MUST your message with the following added in JSON format (and nothing
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more after this). This will list the pending tests and their respective files (even if they belong to the same or
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an existing test file) as this will be used for the next follow-up test generation request.
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{"status": "more_tests_required",
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"pending_tests": "test_name (file_name), another_test_name (file_name)"}
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This approach ensures comprehensive test coverage while maintaining quality and avoiding context overflow.
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Remember: your value is catching the hard bugs—not inflating coverage numbers.
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"""
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