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/ocbunknown/fastapi-claude-template/endpointnpx skills add ocbunknown/fastapi-claude-template --skill endpointgit clone --depth 1 https://github.com/ocbunknown/fastapi-claude-templateWhat 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 | $0.00073 | $0.03263 |
| Opus 5 | $0.00036 | $0.01631 |
| Sonnet 5 | $0.00015 | $0.00653 |
| Haiku 4.5 | $0.00007 | $0.00326 |
Grade A, and why
endpoint 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 3d 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 — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing HTTP endpoints (src/presentation/http/v1/endpoints/<audience>/)
Endpoints are thin adapters. They have no business logic, no DB access, no validation beyond schema parsing. Their job is contract → Request → request_bus.send() → OkResponse(contract.from(result)).
Pick the audience folder first
Endpoints live under presentation/http/v1/endpoints/<audience>/ where <audience> decides auth:
| Folder | Router guard | Use for |
|---|---|---|
public/ |
none | healthcheck, register, login, refresh, public forms |
user/ |
Authorization() (any authenticated user) |
/users/me, logout, anything user-owned |
admin/ |
Authorization("Admin") |
admin user management, moderation, cross-user operations |
internal/ |
service-to-service stub, include_in_schema=False |
webhooks, internal APIs |
You never attach Authorization(...) at the endpoint function level when the router already enforces it. FastAPI caches the dependency result per request, so declaring user: Annotated[UserResult, Require(Authorization())] as a parameter inside a user/-audience endpoint is free — it returns the same cached UserResult that the router-level guard already validated.
Endpoint file skeleton
# src/presentation/http/v1/endpoints/admin/widget.py
from typing import Annotated
import uuid_utils.compat as uuid
from dishka.integrations.fastapi import DishkaRoute
from fastapi import APIRouter, Query, status
from fastapi import Depends as Require
from src.application.common.interfaces.request_bus import RequestBus
from src.application.common.pagination import OffsetPagination
from src.application.v1.results import OffsetResult, WidgetResult
from src.application.v1.usecases.widget import (
CreateWidgetRequest,
SelectManyWidgetRequest,
SelectWidgetRequest,
UpdateWidgetRequest,
)
from src.common.di import Depends
from src.database.psql.types.widget import WidgetLoads
from src.presentation.http.common.responses import OkResponse
from src.presentation.http.v1 import contracts
admin_widget_router = APIRouter(
prefix="/widgets", tags=["Admin | Widget"], route_class=DishkaRoute
)
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.
- 3d ago First seen · 284 lines · 73 tokens per session scan A c71896090ca5
endpoint is a skill published in the GitHub repository ocbunknown/fastapi-claude-template (32 stars, last pushed 4mo ago), licensed MIT. It adds 73 tokens to every session and 3,263 once invoked, about $0.0004 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 2026-08-30.
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…