Borrowing it
Nothing to install: this file belongs to vlad-ryzhkov/ai-context-engineering-for-qa. 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/vlad-ryzhkov/ai-context-engineering-for-qa/main/.github/copilot-instructions.mdgit clone --depth 1 https://github.com/vlad-ryzhkov/ai-context-engineering-for-qaWrote 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/instructions/vlad-ryzhkov/ai-context-engineering-for-qa/copilot-instructions)<a href="https://agentmods.dev/instructions/vlad-ryzhkov/ai-context-engineering-for-qa/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/vlad-ryzhkov/ai-context-engineering-for-qa/copilot-instructions.svg" alt="Measured on agentmods" 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 | $0.00226 | $0.00226 |
| Opus 5 | $0.00113 | $0.00113 |
| Sonnet 5 | $0.00045 | $0.00045 |
| Haiku 4.5 | $0.00023 | $0.00023 |
Grade A, and why
ai-context-engineering-for-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 4d 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.
What it actually says
ACE Pipeline — Continuous Improvement
After completing any task:
- Analyze what rules were missing (Gardener Protocol)
- Append observations to
.ai-lessons/pending.md - On failure: formulate 1 root-cause rule (Reflection Protocol)
When .ai-lessons/pending.md has >=3 entries, run /curate-lessons to promote rules.
Delta Update Protocol: Use Edit (surgical replace), never Write (full overwrite) on context files.
Gardener Output Format
GARDENER ANALYSIS
| # | Observation | Proposed rule | Section | Target file |
If no proposals: "GARDENER: no proposals for this run"
Target File Selection
- Skill-specific rule ->
skills/{name}/SKILL.md - Global QA pattern ->
qa-antipatterns/{category}.md - Cross-cutting rule ->
.ai-lessons/pending.md
Reflection (on failure only)
- Identify root cause (not symptom)
- Formulate exactly 1 rule
- Dedup check before appending to pending.md
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.
- 4d ago First seen · 33 lines · 226 tokens per session scan A d68b896dce8d
ai-context-engineering-for-qa copilot-instructions.md is an instructions file published in the GitHub repository vlad-ryzhkov/ai-context-engineering-for-qa (6 stars, last pushed 1mo ago), licensed Unlicense. It adds 226 tokens to every session, about $0.0011 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 2026-09-03.
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