Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/yiouli/pixie-qa/copilot-instructionsgit clone --depth 1 https://github.com/yiouli/pixie-qaWhat 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 | $0.05630 | $0.05630 |
| Opus 5 | $0.02815 | $0.02815 |
| Sonnet 5 | $0.01126 | $0.01126 |
| Haiku 4.5 | $0.00563 | $0.00563 |
Grade A, and why
pixie-qa copilot-instructions.md 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 2d 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 — 674 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Copilot Instructions for pixie-qa
Project Overview
pixie-qa is a Python package and coding-agent skill for automated quality assurance of AI applications. The package is published as pixie and provides instrumentation, evaluation, and observability primitives that plug into LLM-powered apps.
Technology Stack
- Python 3.11+ with type hints
- uv for package management, virtual environments, and builds
- pytest for testing
- mypy for static type checking
- ruff for linting and formatting
- OpenTelemetry SDK and OpenInference for LLM call instrumentation
Package Structure
pixie/
__init__.py
assets/
index.html # compiled React scorecard (build artifact, gitignored)
cli/
test_command.py # pixie test entry point
evals/
scorecard.py # scorecard data models + template-based HTML generation
instrumentation/
__init__.py # public API: init(), flush()
spans.py # ObserveSpan, LLMSpan, message/content types
handler.py # InstrumentationHandler ABC
wrap.py # wrap() API for data-object-based tracing
processor.py # LLMSpanProcessor (OTel SpanProcessor)
queue.py # _DeliveryQueue (background worker thread)
instrumentors.py # auto-discovers and activates OpenInference instrumentors
py.typed
frontend/ # React scorecard SPA source
src/ # React components, types, styles
package.json # React 19, Vite 6, vite-plugin-singlefile
vite.config.ts # builds to ../pixie/assets/
README.md # frontend dev & build instructions
tests/
README.md # testing instructions and manual verification guide
pixie/ # automated tests (pytest)
cli/
test_test_command.py # CLI unit tests
e2e_fixtures/ # mock evaluators and datasets for e2e
evals/
test_scorecard.py
instrumentation/
test_spans.py
test_context.py
test_queue.py
test_processor.py
test_integration.py
manual/ # manual testing fixtures (not run by pytest)
mock_evaluators.py
datasets/
sample-qa.json # run with: pixie test tests/manual/datasets/sample-qa.json
specs/ # design specs and architecture docs
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.
- 2d ago First seen · 674 lines · 5,630 tokens per session scan A f5ad57f82d4c
pixie-qa copilot-instructions.md is an instructions file published in the GitHub repository yiouli/pixie-qa (7 stars, last pushed 4mo ago), licensed MIT. It adds 5,630 tokens to every session, about $0.0281 per session on Opus 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 2026-08-31.
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