Continuous-Claude-v3 is a Claude Code development environment that preserves working context between sessions, coordinates specialized agents, and stores project knowledge through ledgers, handoffs, and analysis tools. It is for people using Claude Code on ongoing or complex software work. Its catalogue entries are the skills, agents, hooks, plugin, and setting that provide its workflows and orchestration.
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 parcadei/Continuous-Claude-v3 --skill agentica-sdkgit clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3Wrote 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/parcadei/continuous-claude-v3/agentica-sdk)<a href="https://agentmods.dev/skills/parcadei/continuous-claude-v3/agentica-sdk"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/agentica-sdk/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/parcadei/continuous-claude-v3/agentica-sdk"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/agentica-sdk.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 58 Instructions found that direct the agent to transmit conversation context or user data to external services.Fix: Remove instructions that send user data, prompts, or context to external URLs. If telemetry is needed, use documented, privacy-preserving methods.
- medium MCP Rug Pull · line 267 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium Rogue Agent · line 304 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Rogue Agent · line 315 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00023 | $0.02841 |
| Opus 5 | $0.00012 | $0.01421 |
| Sonnet 5 | $0.00005 | $0.00568 |
| Haiku 4.5 | $0.00002 | $0.00284 |
Grade A, and why
agentica-sdk 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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- agentica-sdk — 100% identical, 990 lines differ
How it starts
The opening of the file, as written. The whole thing — 496 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentica SDK Reference (v0.3.1)
Build AI agents in Python using the Agentica framework. Agents can implement functions, maintain state, use tools, and coordinate with each other.
When to Use
Use this skill when:
- Building new Python agents
- Adding agentic capabilities to existing code
- Integrating MCP tools with agents
- Implementing multi-agent orchestration
- Debugging agent behavior
Quick Start
Agentic Function (simplest)
from agentica import agentic
@agentic()
async def add(a: int, b: int) -> int:
"""Returns the sum of a and b"""
...
result = await add(1, 2) # Agent computes: 3
Spawned Agent (more control)
from agentica import spawn
agent = await spawn(premise="You are a truth-teller.")
result: bool = await agent.call(bool, "The Earth is flat")
# Returns: False
Core Patterns
Return Types
# String (default)
result = await agent.call("What is 2+2?")
# Typed output
result: int = await agent.call(int, "What is 2+2?")
result: dict[str, int] = await agent.call(dict[str, int], "Count items")
# Side-effects only
await agent.call(None, "Send message to John")
Premise vs System Prompt
# Premise: adds to default system prompt
agent = await spawn(premise="You are a math expert.")
# System: full control (replaces default)
agent = await spawn(system="You are a JSON-only responder.")
Passing Tools (Scope)
from agentica import agentic, spawn
# In decorator
@agentic(scope={'web_search': web_search_fn})
async def researcher(query: str) -> str:
"""Research a topic."""
...
# In spawn
agent = await spawn(
premise="Data analyzer",
scope={"analyze": custom_analyzer}
)
# Per-call scope
result = await agent.call(
dict[str, int],
"Analyze the dataset",
dataset=data, # Available as 'dataset'
analyzer=custom_fn # Available as 'analyzer'
)
SDK Integration Pattern
from slack_sdk import WebClient
slack = WebClient(token=SLACK_TOKEN)
# Extract specific methods
@agentic(scope={
'list_users': slack.users_list,
'send_message': slack.chat_postMessage
})
async def team_notifier(message: str) -> None:
"""Send team notifications."""
...
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.
- 10d ago First seen · 496 lines · 23 tokens per session scan A f49036346a8b
agentica-sdk is a skill published in the GitHub repository parcadei/Continuous-Claude-v3 (3,937 stars, last pushed 7mo ago), licensed MIT. It adds 23 tokens to every session and 2,841 once invoked, about $0.0001 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.
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