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/dsfaccini/siigo-mcp/technicalnpx skills add dsfaccini/siigo-mcp --skill technicalgit clone --depth 1 https://github.com/dsfaccini/siigo-mcpWrote 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/skills/dsfaccini/siigo-mcp/technical)<a href="https://agentmods.dev/skills/dsfaccini/siigo-mcp/technical"><img src="https://agentmods.dev/badge/skills/dsfaccini/siigo-mcp/technical.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 | $0.00035 | $0.00587 |
| Opus 5 | $0.00017 | $0.00293 |
| Sonnet 5 | $0.00007 | $0.00117 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
technical 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.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical MCP Expert
Research-First Implementation skill for MCP/FastMCP best practices.
When to Use
Invoke this skill when working on:
- MCP server architecture decisions
- Tool design and progressive disclosure
- Context management optimization
- FastMCP middleware and composition
- Logfire observability patterns
Key Resources
FastMCP
- FastMCP Docs - Official documentation
- FastMCP GitHub - Source and examples
- Tool Transformation - Making tools LLM-friendly
- Middleware - Cross-cutting concerns
MCP Protocol
- MCP Best Practices - Architectural guidelines
- MCP Spec - Latest protocol specification
Progressive Disclosure
- Lazy MCP Proxy - 85-93% context reduction
- Pattern: Discover → Load → Execute (don't expose all tools upfront)
- Use gateway pattern for 5+ related capabilities
FastMCP Patterns
Server Composition
# Dynamic composition (live links)
mcp.mount("customers", customer_server)
# Static composition (snapshot)
mcp.import_server(shared_utils)
Dependency Injection
@asynccontextmanager
async def get_client() -> AsyncIterator[Client]:
client = Client()
try:
yield client
finally:
await client.close()
@mcp.tool
async def my_tool(client: Client = Depends(get_client)) -> str:
return await client.fetch()
Context Usage
@mcp.tool
async def long_task(query: str, ctx: Context) -> str:
ctx.log.info(f"Processing: {query}")
ctx.report_progress(0.5)
return "result"
Logfire Integration
import logfire
logfire.configure()
# Automatic instrumentation for:
# - HTTP requests (httpx)
# - Async operations
# - Custom spans with @logfire.instrument
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 · 87 lines · 35 tokens per session scan A 19a1bcc6c29c
technical is a skill published in the GitHub repository dsfaccini/siigo-mcp (1 stars, last pushed 8mo ago), licensed MIT. It adds 35 tokens to every session and 587 once invoked, about $0.0002 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…