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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3npx agentmods add skills/parcadei/continuous-claude-v3/perplexity-searchWrote 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/perplexity-search)<a href="https://agentmods.dev/skills/parcadei/continuous-claude-v3/perplexity-search"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/perplexity-search/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/perplexity-search"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/perplexity-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00018 | $0.00809 |
| Opus 5 | $0.00009 | $0.00404 |
| Sonnet 5 | $0.00004 | $0.00162 |
| Haiku 4.5 | $0.00002 | $0.00081 |
Grade A, and why
perplexity-search 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 9d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perplexity AI Search
Web search with AI-powered answers, deep research, and chain-of-thought reasoning.
When to Use
- Direct web search for ranked results (no AI synthesis)
- AI-synthesized research with citations
- Chain-of-thought reasoning for complex decisions
- Deep comprehensive research on topics
Models (2025)
| Model | Purpose |
|---|---|
sonar |
Lightweight search with grounding |
sonar-pro |
Advanced search for complex queries |
sonar-reasoning-pro |
Chain of thought reasoning |
sonar-deep-research |
Expert-level exhaustive research |
Usage
Quick question (AI answer)
uv run python scripts/mcp/perplexity_search.py \
--ask "What is the latest version of Python?"
Direct web search (ranked results, no AI)
uv run python scripts/mcp/perplexity_search.py \
--search "SQLite graph database patterns" \
--max-results 5 \
--recency week
AI-synthesized research
uv run python scripts/mcp/perplexity_search.py \
--research "compare FastAPI vs Django for microservices"
Chain-of-thought reasoning
uv run python scripts/mcp/perplexity_search.py \
--reason "should I use Neo4j or SQLite for small graph under 10k nodes?"
Deep comprehensive research
uv run python scripts/mcp/perplexity_search.py \
--deep "state of AI agent observability 2025"
Parameters
| Parameter | Description |
|---|---|
--ask |
Quick question with AI answer (sonar) |
--search |
Direct web search - ranked results without AI synthesis |
--research |
AI-synthesized research (sonar-pro) |
--reason |
Chain-of-thought reasoning (sonar-reasoning-pro) |
--deep |
Deep comprehensive research (sonar-deep-research) |
Search-specific options
| Parameter | Description |
|---|---|
--max-results N |
Number of results (1-20, default: 10) |
--recency |
Filter: day, week, month, year |
--domains |
Limit to specific domains |
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.
- 9d ago First seen · 112 lines · 18 tokens per session scan A 609fe418fded
perplexity-search is a skill published in the GitHub repository parcadei/Continuous-Claude-v3 (3,938 stars, last pushed 7mo ago), licensed MIT. It adds 18 tokens to every session and 809 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 2026-09-03.
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