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 skills/microsoft/aks-lab-githubcopilot/copilot-sdk-gpt55npx skills add microsoft/AKS-Lab-GitHubCopilot --skill copilot-sdk-gpt55git clone --depth 1 https://github.com/microsoft/AKS-Lab-GitHubCopilotWhat 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 | $0.00000 | $0.01499 |
| Opus 5 | $0.00000 | $0.00749 |
| Sonnet 5 | $0.00000 | $0.00300 |
| Haiku 4.5 | $0.00000 | $0.00150 |
Grade A, and why
copilot-sdk-gpt55 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: GitHub Copilot SDK on gpt-5.5
Reflects the actual
agent-framework[github-copilot]1.3.x API installed in this repo. EarlierGitHubCopilotChatClient/ChatAgent(client=...)syntax is not valid here.
The two imports
from agent_framework.github import GitHubCopilotAgent, GitHubCopilotOptions
from copilot.generated.rpc import PermissionDecisionApproveOnce
from copilot.session import PermissionRequestResult
from src.shared.copilot import build_copilot_client
The one builder shape
def _approve_all(_request: object, _context: dict[str, str]) -> PermissionRequestResult:
return PermissionDecisionApproveOnce()
async def build_agent(settings: Settings) -> _RunnableAgent:
agent = GitHubCopilotAgent(
instructions=SYSTEM_PROMPT,
client=build_copilot_client(),
name="<name>",
description="...",
tools=list(TOOLS), # local @tool functions, NEVER MCP
default_options=GitHubCopilotOptions(
model=settings.copilot_model, # default "gpt-5.5"
timeout=settings.copilot_timeout_seconds,
on_permission_request=_approve_all, # MANDATORY — see §Permissions
mcp_servers={ # specialists only; orchestrator omits this
"<key>": {
"type": "http",
"url": settings.<which>_mcp_url,
"tools": ["*"],
"timeout": int(settings.copilot_timeout_seconds * 1000),
},
},
),
)
return _RunnableAgent(agent=agent)
_RunnableAgent is a @dataclass(frozen=True) adapter exposing async def run(self, message: str) -> AgentResponse[None]. The shared make_app calls .run(req.goal) on it.
Permissions (do NOT skip)
The Copilot SDK runs every tool call through a permission gate. The handler must return PermissionDecisionApproveOnce(). Without a handler, every call returns:
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 · 135 lines · 0 tokens per session scan A 2002c14763df
copilot-sdk-gpt55 is a skill published in the GitHub repository microsoft/AKS-Lab-GitHubCopilot (7 stars, last pushed 28d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,499 tokens. 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.
Other skills, from other repositories
Prompting
Meta-prompting standard library for generating, optimizing, and composing prompts programmatically via Standards, Handlebars Templates, and Tools; output is always a prompt to use elsewhere, not final content. USE WHEN meta-prompting, template generation, prompt optimization, prompt engineering, write a prompt, create…
add-prompt-enhancement-guide
Author a prompt-enhancement system prompt for a new ecosystem and register/update it on the orchestrator's prompt-analysis service. Use when onboarding a new ecosystem (e.g. happyhorse, a new Flux variant, a new Wan video version) and the user provides the ecosystem key plus a reference link, model card, or…
ai
AI/LLM 能力索引。Agent 开发、LLM 安全、RAG 系统。当用户提到 AI、LLM、Agent、RAG、Prompt 时路由到此。.
hve-builder
Author, review, or validate Copilot prompt-engineering artifacts through independent review, behavior testing, and host checks.
prompt-design
Design MCP prompts to expose reusable prompt templates. Use when creating parameterized prompts in xmcp.
best-practices
Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.