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/hybridlabor-api/bdb-dev-optimized-agent-skills/copilot-instructionsgit clone --depth 1 https://github.com/hybridlabor-api/bdb-dev-optimized-agent-skillsWrote 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/hybridlabor-api/bdb-dev-optimized-agent-skills/copilot-instructions)<a href="https://agentmods.dev/instructions/hybridlabor-api/bdb-dev-optimized-agent-skills/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/hybridlabor-api/bdb-dev-optimized-agent-skills/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 | $0.00638 | $0.00638 |
| Opus 5 | $0.00319 | $0.00319 |
| Sonnet 5 | $0.00128 | $0.00128 |
| Haiku 4.5 | $0.00064 | $0.00064 |
Grade A, and why
bdb-dev-optimized-agent-skills 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 5d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
memB Auto-Injected Context
The following knowledge was automatically retrieved from the memB vector engine.
Global Developer Preferences (Godmode)
Project Context: bdb-dev-optimized-agent-skills
- None
- None
- None
- None
Global Agent Instructions
1. Core Behavior & Communication
- Direct Output: Eliminate conversational filler and pleasantries. Deliver immediate, actionable answers.
- Content Language: All generated code, file content, documentation, and technical outputs MUST be in English.
- Formatting: Use structured Markdown with bullet points and bold text. Avoid dense text blocks.
- Images: Open generated images/mockups directly via Chrome terminal command in new tabs, or provide a tab listing links to the images.
2. Safety, Control & Rollback
- Mandatory Git Snapshots: Before modifying, refactoring, or deleting any file in the workspace, take a Git snapshot or create a commit of the current state.
- Rollback Readiness: Ensure all changes can be safely reverted. Ask for confirmation before performing destructive actions (e.g., massive deletions).
- Explicit GO Confirmation: If the user specifies "warte auf mein GO" (or similar), halt all plan execution, tools, or background tasks. Wait until the user explicitly responds with the literal word "GO" (case-insensitive) in the chat. Do NOT rely on automatic system approvals.
3. Token Efficiency & Code Quality
- Clarification first: If a prompt is ambiguous or lacks context, ask brief, targeted questions before generating long solutions.
- Minimalist Comments: Write clean, modular, self-documenting code. Keep comments to an absolute minimum, only explaining the "why" behind complex logic or hardware workarounds. Do not restate obvious operations.
4. Development & Platform Context
- Domain Adaptation: Adapt dynamically to the specific architecture, language, and project type (React, Node, Python, SQLite, Embedded C, Lua, etc.). Strictly follow design patterns, constraints, and platform-specific requirements of the current workspace.
- Efficiency & Safety: Prioritize memory efficiency and safety for embedded systems, and scalability and responsiveness for higher-level applications.
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.
- 5d ago First seen · 40 lines · 638 tokens per session scan A 20194b25d17f
bdb-dev-optimized-agent-skills copilot-instructions.md is an instructions file published in the GitHub repository hybridlabor-api/bdb-dev-optimized-agent-skills (6 stars, last pushed 7d ago), licensed Apache-2.0. It adds 638 tokens to every session, about $0.0032 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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