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 shyftlabs/continuum --skill continuum-testinggit clone --depth 1 https://github.com/shyftlabs/continuumWrote 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/shyftlabs/continuum/continuum-testing)<a href="https://agentmods.dev/skills/shyftlabs/continuum/continuum-testing"><img src="https://agentmods.dev/badge/skills/shyftlabs/continuum/continuum-testing/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/shyftlabs/continuum/continuum-testing"><img src="https://agentmods.dev/badge/skills/shyftlabs/continuum/continuum-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00080 | $0.01712 |
| Opus 5.5 | $0.00032 | $0.00685 |
| Sonnet 5.5 | $0.00016 | $0.00342 |
| Haiku 4.5 | $0.00008 | $0.00171 |
Grade A, and why
continuum-testing 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Continuum Testing Skill
Continuum's test-friendly seams:
Container(ContainerConfig(auto_initialize=False))— opt out of auto-init and inject mocks.Container.set_*_client(...)accepts the real client, a custom subclass, or any object satisfying theILLMClient/IMemoryClient/ISessionClientprotocols.fakeredis(a dev dependency) drops in for the Redis session provider.reset_container()andreset_global_memory()/reset_global_session()cleanly nuke globals between tests.
Imports
import pytest
from continuum.agent import AgentRunner, BaseAgent
from continuum.core.container import Container, ContainerConfig, reset_container
from continuum.llm.types import LLMResponse, StreamChunk, Usage
from continuum.protocols import ILLMClient
1. Mock LLM that satisfies ILLMClient
class MockLLM:
is_enabled = True
def __init__(self, replies: list[str]):
self._replies = list(replies)
async def chat(self, messages, **kw):
text = self._replies.pop(0) if self._replies else "mock reply"
return LLMResponse(model="mock", content=text, role="assistant",
usage=Usage(prompt_tokens=10, completion_tokens=5,
total_tokens=15))
async def chat_stream(self, messages, **kw):
text = self._replies.pop(0) if self._replies else "mock reply"
for ch in text:
yield StreamChunk(model="mock", content=ch)
yield StreamChunk(model="mock", is_finished=True)
def count_tokens(self, messages, model=None) -> int:
return sum(len(m.get("content", "")) // 4 for m in messages)
Wire it into the container:
container = Container(ContainerConfig(auto_initialize=False))
container.set_llm_client(MockLLM(["Hello, world!"]))
container.set_memory_client(None)
container.set_session_client(None)
runner = AgentRunner(container=container)
agent = BaseAgent(name="t", instructions="...")
resp = await runner.run(agent, "anything")
assert resp.content == "Hello, world!"
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 · 237 lines · 80 tokens per session scan A 3c55ae851c7a
continuum-testing is a skill published in the GitHub repository shyftlabs/continuum (84 stars, last pushed 11d ago), licensed Apache-2.0. It adds 80 tokens to every session and 1,712 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 2026-09-03.
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