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 skills add signnow/sn-mcp-server --skill sn-implementationgit clone --depth 1 https://github.com/signnow/sn-mcp-serverWrote 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/signnow/sn-mcp-server/sn-implementation)<a href="https://agentmods.dev/skills/signnow/sn-mcp-server/sn-implementation"><img src="https://agentmods.dev/badge/skills/signnow/sn-mcp-server/sn-implementation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/signnow/sn-mcp-server/sn-implementation"><img src="https://agentmods.dev/badge/skills/signnow/sn-mcp-server/sn-implementation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00077 | $0.02021 |
| Opus 5.5 | $0.00031 | $0.00808 |
| Sonnet 5.5 | $0.00015 | $0.00404 |
| Haiku 4.5 | $0.00008 | $0.00202 |
Grade A, and why
sn-implementation 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 1mo 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Standards
Layer-Specific Rules
Before writing any code, identify the layer you're editing and apply its rules.
API Models (signnow_client/models/)
- ✅ Pydantic
BaseModel,Field, validators, type aliases,__all__exports. - ❌ Imports from
sn_mcp_server.*orsignnow_client/client*.py. Any business logic. - Pure data definitions only — mirror the SignNow REST API schema.
API Client (signnow_client/client_*.py)
- ✅ Use
self._get(),self._post(),self._put(),self._post_multipart()fromclient_base.py. Import models fromsignnow_client/models/. Use exceptions fromsignnow_client/exceptions.py. - ❌ Imports from
sn_mcp_server.*(upward import). Directhttpxcalls. Starlette imports. MCP logic. - Each method takes
token: strand passesAuthorization: Bearer {token}.
Tool Response Models (sn_mcp_server/tools/models.py)
- ✅ Pydantic
BaseModel,Field. Type references fromsignnow_client/models/. - ❌ Raw API passthrough. Fields the agent won't act on. Empty lists, null metadata, internal IDs.
- Every field must justify: "Does the agent need this for its next decision?"
Tool Business Logic (sn_mcp_server/tools/<feature>.py)
- ✅ Import
SignNowAPIClient,tools/models.py. Pure logic, data transformation. - ❌ Import Starlette. Import from other
tools/<feature>.py. Resolve tokens. Module-level mutable state. Generic errors. - Functions receive
(client: SignNowAPIClient, token: str, ...)→ return curated DTOs.
Tool Orchestrator (sn_mcp_server/tools/signnow.py)
- ✅ Call
_get_token_and_client(), delegate totools/<feature>.py, useAnnotated[..., Field(...)]for params. - ❌ Business logic. Complex conditionals. Data transformation.
- Each tool function: resolve token → call business logic → return result. Docstrings = MCP tool descriptions.
Auth (sn_mcp_server/auth.py, token_provider.py)
- ✅ Import
signnow_client, config. - ❌ Import from tools layer. Expose tokens/secrets in logs or errors.
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.
- 1mo ago First seen · 223 lines · 77 tokens per session scan A acbb7ca3562b
sn-implementation is a skill published in the GitHub repository signnow/sn-mcp-server (8 stars, last pushed 2mo ago), licensed MIT. It adds 77 tokens to every session and 2,021 once invoked, about $0.0003 per session on Opus 5.5. 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
datamodel-code-generator
Use this skill when the user wants Python data models, Pydantic models, dataclasses, TypedDicts, msgspec structs, or type-safe Python classes generated from OpenAPI, AsyncAPI, JSON Schema, GraphQL, JSON/YAML/CSV sample data, MCP tool schemas, Protocol Buffers, XML Schema, Apache Avro, or existing Python model objects.…
kui-framework
Guide for building web applications with the Kui framework. Use when writing Kui ASGI/WSGI handlers, defining routes, binding parameters, configuring OpenAPI docs, or working with middleware/dependency injection.
mastering-python-skill
Modern Python coaching covering language foundations through advanced production patterns. Use when asked to "write Python code", "explain Python concepts", "set up a Python project", "configure Poetry or PDM", "write pytest tests", "create a FastAPI endpoint", "run uvicorn server", "configure alembic migrations"…
fastapi-expert
Expert-level FastAPI development for high-performance Python APIs with async support. Use when the user mentions Python, API, async, REST, OpenAPI, or Pydantic, or when the task involves FastAPI Features.
docker_packaging
A set of rules and templates for packaging a Python MCP service in Docker. Docker packages an application and its dependencies into a repeatable container that can run on another machine.
validate-python-stack
Validate the Python Template repository or a project generated from it. Use before committing, publishing, deploying, or reviewing a stack to check catalog compilation, rendering, dependencies, formatting, tests, security workflows, skills, and representative runtime behavior.