
shift-left
Professional, repo-specific quality strategies and risk-based test plans for modern dev teams.
- Create a repo-specific quality roadmap based on current technical debt.
- Generate targeted test cases for a specific feature or archetype.
- Review existing automated tests for brittle logic or coverage gaps.
Secure checkout via Stripe
Included in download
- Create a repo-specific quality roadmap based on current technical debt.
- Generate targeted test cases for a specific feature or archetype.
- terminal, file_read, file_write automation included
- Includes example output and usage patterns
Sample Output
A real example of what this skill produces.
I have enough to assess and ask the scope question. Here's what I found.
Quality Maturity Assessment
Current Level: 3 -- Workflow-Centric
Evidence:
- Three test tiers in place: tests/unit/, tests/integration/, tests/functional/
- pytest + pytest-django + pytest-xdist; coverage.py uploaded to Codecov
- GitHub Actions matrix: 5 Python versions x 3 Django versions, PostgreSQL service
- Key e-commerce workflows covered: payment (bankcard, models), checkout (calculators, session, surcharges, views), order (creator, event_handler, processing, reports), basket, offer, customer, dashboard, partner, shipping
- Python linting (flake8, isort, pylint, black) and JS linting (eslint) in CI
- Functional tests use django-webtest (HTTP-level, not browser)
Strengths:
- Unusually thorough integration test coverage for a library -- most e-commerce modules have meaningful tests at the right layer
- CI version matrix is a genuine strength; regressions across Django/Python versions are caught before release
- Codecov tracks coverage at patch level
Gaps:
- CodeQL runs on a weekly schedule only -- not gated on PRs; a vulnerability introduced in payment or checkout code can merge before the next scan
- No dependency vulnerability scanning (no pip-audit, Safety, or Bandit)
- No browser-based E2E (functional tests use WebTest -- no JS execution, no real browser rendering)
- No accessibility testing despite a customer-facing storefront surface in the sandbox
- No coverage threshold enforced; CI never blocks on coverage regression Archetype: Django Oscar is a Library/SDK with a customer-facing storefront + admin dashboard delivered via a sandbox. Risk is concentrated in the e-commerce domain -- payment, checkout, offer calculation, order processing, and the customer auth backend.
Likely relevant plans:
Plan Rationale Quality risk strategy Always Unit Existing suite; strengthen offer/voucher calculation and checkout pipeline edge cases Integration Existing suite; strengthen payment seams and order event handling Security High priority -- CodeQL weekly gap, no SAST, payment + auth + admin E2E Sandbox has real customer + admin UI; WebTest covers logic but not JS or real browser rendering Accessibility Lightweight -- customer-facing storefront; no current tooling Not recommended: Performance (no runtime load concern for a library), Contract (no HTTP API surface; Oscar's extension points are covered by integration tests and semantic versioning).
Would you like:
All relevant plans -- quality risk strategy + unit + integration + security + E2E + accessibility (recommended) Only the umbrella quality risk strategy A custom subset
All plans written and dashboard generated. Here's the summary:
Do first (in this order):
CodeQL on PRs -- 3-line YAML edit to .github/workflows/codeql-analysis.yml. Closes the biggest gap immediately; a vulnerability introduced in checkout or auth code currently survives until the next weekly scan. pip-audit in CI -- run it locally first to get a clean baseline, then add a security job to test.yml. ~15 lines. Offer/voucher boundary unit tests -- benefit cap when discount exceeds line total, per-customer voucher reuse, exclusive voucher blocking. Order event handler failure test -- verify test_event_handler.py covers the dispatch-failure path explicitly. Defer for now:
Playwright E2E -- valuable, but start it after security CI is stable Accessibility -- add axe-playwright alongside Playwright once that suite is running Performance and contract -- not applicable at the library layer Deliverables written to django-oscar/quality/:
File 1-quality-risk-strategy.md Risk table, Mermaid diagrams, coverage summary, top 10 test cases 1-quality-risk-strategy-dashboard.html Stakeholder-ready visual 2-unit-test-plan.md Offer/voucher/checkout priority cases 3-integration-test-plan.md Checkout seam, payment, order event handler 4-security-test-plan.md CodeQL gate + pip-audit -- exact YAML included 5-e2e-test-plan.md 5 Playwright journeys against the sandbox 6-accessibility-test-plan.md axe-playwright on 4 key pages, keyboard baseline README.md Entry point with plan table and do-first summary

shift-left
Professional, repo-specific quality strategies and risk-based test plans for modern dev teams.
Secure checkout via Stripe
Included in download
- Create a repo-specific quality roadmap based on current technical debt.
- Generate targeted test cases for a specific feature or archetype.
- terminal, file_read, file_write automation included
- Includes example output and usage patterns
- Instant install
Sample Output
A real example of what this skill produces.
I have enough to assess and ask the scope question. Here's what I found.
Quality Maturity Assessment
Current Level: 3 -- Workflow-Centric
Evidence:
- Three test tiers in place: tests/unit/, tests/integration/, tests/functional/
- pytest + pytest-django + pytest-xdist; coverage.py uploaded to Codecov
- GitHub Actions matrix: 5 Python versions x 3 Django versions, PostgreSQL service
- Key e-commerce workflows covered: payment (bankcard, models), checkout (calculators, session, surcharges, views), order (creator, event_handler, processing, reports), basket, offer, customer, dashboard, partner, shipping
- Python linting (flake8, isort, pylint, black) and JS linting (eslint) in CI
- Functional tests use django-webtest (HTTP-level, not browser)
Strengths:
- Unusually thorough integration test coverage for a library -- most e-commerce modules have meaningful tests at the right layer
- CI version matrix is a genuine strength; regressions across Django/Python versions are caught before release
- Codecov tracks coverage at patch level
Gaps:
- CodeQL runs on a weekly schedule only -- not gated on PRs; a vulnerability introduced in payment or checkout code can merge before the next scan
- No dependency vulnerability scanning (no pip-audit, Safety, or Bandit)
- No browser-based E2E (functional tests use WebTest -- no JS execution, no real browser rendering)
- No accessibility testing despite a customer-facing storefront surface in the sandbox
- No coverage threshold enforced; CI never blocks on coverage regression Archetype: Django Oscar is a Library/SDK with a customer-facing storefront + admin dashboard delivered via a sandbox. Risk is concentrated in the e-commerce domain -- payment, checkout, offer calculation, order processing, and the customer auth backend.
Likely relevant plans:
Plan Rationale Quality risk strategy Always Unit Existing suite; strengthen offer/voucher calculation and checkout pipeline edge cases Integration Existing suite; strengthen payment seams and order event handling Security High priority -- CodeQL weekly gap, no SAST, payment + auth + admin E2E Sandbox has real customer + admin UI; WebTest covers logic but not JS or real browser rendering Accessibility Lightweight -- customer-facing storefront; no current tooling Not recommended: Performance (no runtime load concern for a library), Contract (no HTTP API surface; Oscar's extension points are covered by integration tests and semantic versioning).
Would you like:
All relevant plans -- quality risk strategy + unit + integration + security + E2E + accessibility (recommended) Only the umbrella quality risk strategy A custom subset
All plans written and dashboard generated. Here's the summary:
Do first (in this order):
CodeQL on PRs -- 3-line YAML edit to .github/workflows/codeql-analysis.yml. Closes the biggest gap immediately; a vulnerability introduced in checkout or auth code currently survives until the next weekly scan. pip-audit in CI -- run it locally first to get a clean baseline, then add a security job to test.yml. ~15 lines. Offer/voucher boundary unit tests -- benefit cap when discount exceeds line total, per-customer voucher reuse, exclusive voucher blocking. Order event handler failure test -- verify test_event_handler.py covers the dispatch-failure path explicitly. Defer for now:
Playwright E2E -- valuable, but start it after security CI is stable Accessibility -- add axe-playwright alongside Playwright once that suite is running Performance and contract -- not applicable at the library layer Deliverables written to django-oscar/quality/:
File 1-quality-risk-strategy.md Risk table, Mermaid diagrams, coverage summary, top 10 test cases 1-quality-risk-strategy-dashboard.html Stakeholder-ready visual 2-unit-test-plan.md Offer/voucher/checkout priority cases 3-integration-test-plan.md Checkout seam, payment, order event handler 4-security-test-plan.md CodeQL gate + pip-audit -- exact YAML included 5-e2e-test-plan.md 5 Playwright journeys against the sandbox 6-accessibility-test-plan.md axe-playwright on 4 key pages, keyboard baseline README.md Entry point with plan table and do-first summary
Screenshots
About This Skill
What it does
Shift Left transforms your AI agent into a Senior QA and Test Automation Engineer. Instead of generating generic testing advice, it performs deep, repo-specific inspections to create risk-based quality strategies, test plans, and actionable test cases that actually match your codebase and tech stack.
Why use this skill
Most AI agents provide "textbook" QA answers that don't scale. Shift Left is designed for developer-first testing: it respects token limits by lazily loading resources, identifies your project archetype (SaaS, CLI, Mobile, etc.), and refuses to "hallucinate" tools you haven't installed. It bridges the gap between raw code and a professional quality roadmap, ensuring your testing effort is prioritized based on actual technical risk.
Supported Deliverables
- Quality Risk Strategies: High-level roadmaps focused on where your app is most likely to break.
- Deep-Dive Test Plans: Specific guides for Unit, Integration, API, E2E, and Performance layers.
- Targeted Bug Hunting: Context-aware exploration for regressions in specific commits or branches.
- Automation Reviews: Audits of existing test suites for maintainability and coverage gaps.
- Technical Templates: Professional bug reports and quality dashboards generated as clean HTML or Markdown.
Use Cases
- Create a repo-specific quality roadmap based on current technical debt.
- Generate targeted test cases for a specific feature or archetype.
- Review existing automated tests for brittle logic or coverage gaps.
- Perform a scoped bug hunt to find regressions in a specific branch.
- Assess quality maturity to select the right testing tools and layers.
Known Limitations
- Best results come from repos with meaningful code and a recognizable project shape
- Not intended for completely empty repos or idea-stage projects with no real implementation yet
- Recommendations are only as good as the repository access the agent has
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/shift-left | tar xz -C ~/.claude/skills/Free skills install directly. Paid skills require purchase - use the download button above after buying.
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Security Scanned
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Permissions
Allowed Hosts
File Scopes
Requires a SKILL.md-compatible agent with local file access. Best when the runtime supports reading bundled markdown resources and running repo inspection workflows.