smart-test
Intelligent test selection based on code changes. Maps source files to tests via import analysis, implements tiered testing (fast < 1 min, impacted < 5 min, full suite), and tracks test reliability. Use when running tests after code changes to optimize feedback loops and CI time.
What this skill does
# Smart Test Selection Skill
## Purpose
Optimizes test execution by intelligently selecting which tests to run based on code changes. Instead of running the full test suite every time, this skill:
1. Maps code changes to affected test files using import dependency analysis
2. Provides tiered testing strategies for different feedback loop needs
3. Tracks test reliability to prioritize stable tests in fast runs
## When I Activate
I automatically load when you mention:
- "run affected tests" or "run impacted tests"
- "smart test" or "intelligent testing"
- "which tests to run" or "test selection"
- "fast tests" or "quick tests"
- "tests for changes" or "tests for this PR"
## Core Concepts
### Test Tiers
**Tier 1: Fast Tests (< 1 minute)**
- Directly affected unit tests (imports changed file)
- High-reliability tests only (no flaky tests)
- Run on every save or pre-commit
- Command: `pytest -m "not slow and not integration" [selected_tests]`
**Tier 2: Impacted Tests (< 5 minutes)**
- All tests affected by changes (direct + transitive dependencies)
- Includes integration tests for changed modules
- Run before commit or on PR draft
- Command: `pytest [selected_tests]`
**Tier 3: Full Suite**
- Complete test suite
- Run on PR ready-for-review or CI
- Command: `pytest`
### Import Dependency Analysis
The skill builds a dependency graph by analyzing Python imports:
```
source_file.py
|
+-- Imported by: module_a.py, module_b.py
| |
| +-- Tested by: test_module_a.py, test_module_b.py
|
+-- Tested by: test_source_file.py (direct test)
```
**Direct Tests**: Files matching pattern `test_{module}.py` or `{module}_test.py`
**Indirect Tests**: Tests that import modules which import the changed file
### Reliability Tracking
Tests are scored on reliability (0.0 to 1.0):
- **1.0**: Always passes (stable)
- **0.5-0.9**: Occasional failures (investigate)
- **< 0.5**: Frequently fails (flaky - excluded from Tier 1)
Reliability is tracked in `~/.amplihack/.claude/data/test-mapping/reliability.yaml`
## Usage
### Analyze Changes and Get Test Commands
```
User: What tests should I run for my changes?
Claude (using smart-test):
1. Analyzes git diff or staged changes
2. Maps changed files to test dependencies
3. Returns tiered test commands
Example Output:
------------------------------------------
Smart Test Analysis
------------------------------------------
Changed Files:
- src/amplihack/core/processor.py
- src/amplihack/utils/helpers.py
Tier 1 (Fast - 45s estimated):
pytest tests/unit/test_processor.py tests/unit/test_helpers.py -v
Tier 2 (Impacted - 3m estimated):
pytest tests/unit/test_processor.py tests/unit/test_helpers.py \
tests/integration/test_pipeline.py -v
Tier 3 (Full - 12m estimated):
pytest
Recommendation: Start with Tier 1 for quick feedback.
```
### Build or Refresh Mapping Cache
```
User: Build the test mapping for this project
Claude:
1. Scans all Python files
2. Builds import dependency graph
3. Maps source files to test files
4. Saves to .claude/data/test-mapping/code_to_tests.yaml
```
### Check Test Reliability
```
User: Show flaky tests
Claude:
1. Reads reliability.yaml
2. Lists tests with reliability < 0.8
3. Suggests investigation or quarantine
```
## Process
### Step 1: Identify Changed Files
```bash
# For staged changes
git diff --cached --name-only --diff-filter=ACMR
# For all uncommitted changes
git diff --name-only --diff-filter=ACMR
# For PR changes (vs main)
git diff main...HEAD --name-only --diff-filter=ACMR
```
Filter to only Python source files (exclude tests themselves for mapping).
### Step 2: Build Import Graph
For each Python file, extract imports:
```python
# Patterns to detect:
import module
from module import item
from package.module import item
from . import relative
from ..parent import item
```
Build bidirectional mapping:
- Forward: file -> what it imports
- Reverse: file -> what imports it
### Step 3: Map to Tests
For each changed file, find tests via:
1. **Direct test match**: `test_{filename}.py` or `{filename}_test.py`
2. **Import-based**: Tests that import the changed module
3. **Transitive**: Tests that import modules that import changed module (1 level)
### Step 4: Apply Reliability Filter
For Tier 1 only, exclude tests with reliability < 0.8.
### Step 5: Generate Commands
Output pytest commands with appropriate markers:
```bash
# Tier 1
pytest -m "not slow and not integration" tests/a.py tests/b.py
# Tier 2
pytest tests/a.py tests/b.py tests/c.py
# Tier 3
pytest
```
## Data Storage
### code_to_tests.yaml
```yaml
# .claude/data/test-mapping/code_to_tests.yaml
version: 1
last_updated: "2025-11-25T10:00:00Z"
mappings:
src/amplihack/core/processor.py:
direct_tests:
- tests/unit/test_processor.py
indirect_tests:
- tests/integration/test_pipeline.py
transitive_tests:
- tests/e2e/test_full_workflow.py
src/amplihack/utils/helpers.py:
direct_tests:
- tests/unit/test_helpers.py
indirect_tests:
- tests/unit/test_processor.py # processor imports helpers
```
### reliability.yaml
```yaml
# .claude/data/test-mapping/reliability.yaml
version: 1
last_updated: "2025-11-25T10:00:00Z"
tests:
tests/unit/test_processor.py::test_basic:
passes: 98
failures: 2
reliability: 0.98
last_failure: "2025-11-20"
tests/integration/test_api.py::test_timeout:
passes: 45
failures: 15
reliability: 0.75
last_failure: "2025-11-24"
flaky_reason: "Network dependent"
```
## Integration with Workflow
This skill integrates with DEFAULT_WORKFLOW.md:
**Step 12: Run Tests and Pre-commit Hooks**
- Use Tier 1 (fast) for pre-commit
- Quick feedback on changed code
**Step 13: Mandatory Local Testing**
- Use Tier 2 (impacted) before commit
- Ensures affected code paths are tested
**CI Pipeline**
- Use Tier 2 on draft PRs
- Use Tier 3 (full) on ready-for-review PRs
## Markers Integration
Works with existing pytest markers from pyproject.toml:
- `slow` - Excluded from Tier 1
- `integration` - Excluded from Tier 1
- `e2e` - Excluded from Tier 1 and 2
- `neo4j` - Requires special environment
- `requires_docker` - Requires Docker daemon
## Quick Reference
| Scenario | Tier | Time Budget | Command Pattern |
| ---------- | ---- | ----------- | --------------------------------- |
| Pre-commit | 1 | < 1 min | `pytest -m "not slow" [affected]` |
| Pre-push | 2 | < 5 min | `pytest [affected + transitive]` |
| Draft PR | 2 | < 5 min | `pytest [affected + transitive]` |
| Ready PR | 3 | Full | `pytest` |
| CI main | 3 | Full | `pytest` |
## Philosophy Alignment
### Ruthless Simplicity
- Simple tier system (1, 2, 3)
- YAML storage over database
- Import analysis over complex AST parsing
### Zero-BS Implementation
- Real pytest commands (copy-paste ready)
- Actual time estimates based on test count
- No placeholder data or mock reliability scores
### Testing Pyramid
- Tier 1 prioritizes unit tests (60%)
- Tier 2 adds integration tests (30%)
- Tier 3 includes E2E tests (10%)
## Complementary Skills
- **test-gap-analyzer**: Identifies missing tests
- **qa-team**: Creates E2E and parity test scenarios (`outside-in-testing` alias supported)
- **tester agent**: Writes new tests for gaps
- **pre-commit-diagnostic**: Fixes pre-commit failures
## Common Patterns
### Pattern 1: Quick Iteration
```
[Developer makes small change]
Claude: Run affected tests (Tier 1)
[45 seconds later]
Claude: 3/3 tests passed. Ready for commit.
```
### Pattern 2: Pre-Push Validation
```
[Developer about to push]
Claude: Run impacted tests (Tier 2)
[3 minutes later]
Claude: 12/12 tests passed including integrations.
```
### Pattern 3: Flaky Test Investigation
```
User: Tests keep failing randomly
Claude: Checking reliability data...
Found 2 flaky tests (< 0.8 relRelated in Cloud & DevOps
appbuilder-action-scaffolder
IncludedCreate, implement, deploy, and debug Adobe Runtime actions with consistent layout, validation, and error handling. Use this skill whenever the user needs to add actions to an App Builder project, understand action structure (params, response format, web/raw actions), configure actions in the manifest, use App Builder SDKs (State, Files, Events, database), deploy and invoke actions via CLI, debug action issues, or implement patterns such as webhook receivers, custom event providers, journaling consumers, large payload redirects, action sequence pipelines, and Asset Compute workers. Also trigger when users mention serverless functions in Adobe context, action logging, IMS authentication for actions, or cron-style scheduled actions.
orchestrating-datacloud
IncludedSalesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the user needs a multi-step Data Cloud pipeline, cross-phase troubleshooting, or data space and data kit management. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase sf data360 workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching phase-specific skill), the task is STDM/session tracing/parquet telemetry (use observing-agentforce), standard CRM SOQL (use querying-soql), or Apex implementation (use generating-apex).
github-project-automation
IncludedAutomate GitHub repository setup with CI/CD workflows, issue templates, Dependabot, and CodeQL security scanning. Includes 12 production-tested workflows and prevents 18 errors: YAML syntax, action pinning, and configuration. Use when: setting up GitHub Actions CI/CD, creating issue/PR templates, enabling Dependabot or CodeQL scanning, deploying to Cloudflare Workers, implementing matrix testing, or troubleshooting YAML indentation, action version pinning, secrets syntax, runner versions, or CodeQL configuration. Keywords: github actions, github workflow, ci/cd, issue templates, pull request templates, dependabot, codeql, security scanning, yaml syntax, github automation, repository setup, workflow templates, github actions matrix, secrets management, branch protection, codeowners, github projects, continuous integration, continuous deployment, workflow syntax error, action version pinning, runner version, github context, yaml indentation error
sf-datacloud
IncludedSalesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase `sf data360` workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching sf-datacloud-* skill), the task is STDM/session tracing/parquet telemetry (use sf-ai-agentforce-observability), standard CRM SOQL (use sf-soql), or Apex implementation (use sf-apex).
fabric-cli
IncludedUse this skill for Fabric.so CLI workflows with the `fabric` terminal command: diagnose/install/login, search or browse a Fabric library, save notes/links/files, create folders, ask the Fabric AI assistant, manage tasks/workspaces, generate shell completion, check subscription usage, produce JSON output, and use Fabric as persistent agent memory. Do not use for Microsoft Fabric/Azure/Power BI `fab`, Daniel Miessler's Fabric framework, Python Fabric SSH, Fabric.js, or textile/fashion fabric.
lark
IncludedLark/Feishu CLI skills: lark-cli operations for docs, markdown, sheets, base, calendar, im, mail, task, okr, drive, wiki, slides, whiteboard, apps, approval, attendance, contact, vc, minutes, event. Use when the user needs to operate Lark/Feishu resources via lark-cli, send messages, manage documents, spreadsheets, calendars, tasks, OKRs, deploy web pages, or any Feishu/Lark workspace operations.