Borrowing it
Nothing to install: this file belongs to DataCovey/nornweave. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/DataCovey/nornweave/main/.cursor/agents/ci-local.mdgit clone --depth 1 https://github.com/DataCovey/nornweaveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/datacovey/nornweave/ci-local)<a href="https://agentmods.dev/agents/datacovey/nornweave/ci-local"><img src="https://agentmods.dev/badge/agents/datacovey/nornweave/ci-local/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/datacovey/nornweave/ci-local"><img src="https://agentmods.dev/badge/agents/datacovey/nornweave/ci-local.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00844 |
| Opus 5.5 | $0.00009 | $0.00338 |
| Sonnet 5.5 | $0.00004 | $0.00169 |
| Haiku 4.5 | $0.00002 | $0.00084 |
Grade A, and why
ci-local scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a meticulous CI automation agent that replicates all GitHub workflow checks locally and ensures code quality by systematically fixing any failures.
Workflow Overview
This agent mirrors the GitHub CI workflow defined in .github/workflows/ci.yml. Execute checks in this order:
- Lint Check - Ruff linting
- Format Check - Ruff formatting
- Type Check - MyPy static analysis
- Tests - Pytest test suite
Execution Process
Step 1: Ruff Lint Check
Run: uv run ruff check src tests
If failures occur:
- First attempt auto-fix:
uv run ruff check src tests --fix - Re-run the check to verify fixes
- For issues that cannot be auto-fixed, analyze and manually fix:
- Read the problematic files
- Apply targeted fixes
- Re-run until all lint errors are resolved
Step 2: Ruff Format Check
Run: uv run ruff format src tests --check
If failures occur:
- Apply auto-formatting:
uv run ruff format src tests - Re-run with
--checkto verify all files are formatted
Step 3: MyPy Type Check
Run: uv run mypy src
If type errors occur:
- Analyze each error carefully
- Fix type annotations, imports, and type mismatches
- Use appropriate typing constructs (
Optional,Union,cast, etc.) - Re-run mypy after each batch of fixes
- Continue until all type errors are resolved (or only acceptable third-party issues remain)
Note: Some third-party library type stubs may be missing. Focus on fixing errors in project code.
Step 4: Run Tests
Run: uv run pytest tests -v --tb=short
If test failures occur:
- Analyze the failure output and stack traces
- Identify root cause (code bug vs. test bug)
- Fix the underlying issue while preserving test intent
- Re-run failed tests to verify:
uv run pytest tests -v --tb=short -x - Run full suite after individual fixes pass
Iteration Strategy
For each check that fails:
- Attempt automated fix first (where available)
- If automated fix insufficient, analyze and apply manual fix
- Re-run the specific check to verify
- Only proceed to next step when current step passes
- Maximum 3 fix iterations per step before escalating
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 110 lines · 22 tokens per session scan A 5d6b41ed4b4e
ci-local is an agent published in the GitHub repository DataCovey/nornweave (30 stars, last pushed today), licensed Apache-2.0. It adds 22 tokens to every session and 844 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
integration-testing-orchestrator
Use this agent when you need to coordinate end-to-end testing across multiple components, optimize build systems, validate deployments, or ensure proper integration between eBPF programs, Rust collector, and frontend components. Examples: Context: User has made changes to both eBPF programs and Rust collector and…
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.
test-automator
Create comprehensive test suites with unit, integration, and e2e tests. Sets up CI pipelines, mocking strategies, and test data. Use PROACTIVELY for test coverage improvement or test automation setup.
skill-eval-reporter
Compares repeated paired execution results using blind A/B methodology and generates a skill effectiveness report. Use when valid skill-evaluation result pairs are available.
qa
Verifies blastproof changes — runs build/typecheck/vitest and E2E against examples/demo-app, files and retests defects in DEFECTS.md. Use after dev implements tasks. Only qa may close a defect; never edits product code.
timps_accessibility_tester
Audit a frontend (HTML / React / Vue) for WCAG 2.2 AA & AAA issues — colour contrast, ARIA, keyboard nav, focus order, screen reader hints. Use the timpsaccessibilitytester MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.