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 leann-searchgit 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/leann-search)<a href="https://agentmods.dev/skills/parcadei/continuous-claude-v3/leann-search"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/leann-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/leann-search"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/leann-search.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 | $0.00014 | $0.00466 |
| Opus 5 | $0.00007 | $0.00233 |
| Sonnet 5 | $0.00003 | $0.00093 |
| Haiku 4.5 | $0.00001 | $0.00047 |
Grade A, and why
leann-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 11d 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.
What it actually says
LEANN Semantic Search
Use LEANN for meaning-based code search instead of grep.
When to Use
- Conceptual queries: "how does authentication work", "where are errors handled"
- Understanding patterns: "streaming implementation", "provider architecture"
- Finding related code: code that's semantically similar but uses different terms
When NOT to Use
- Exact matches: Use Grep for
class Foo,def bar, specific identifiers - Regex patterns: Use Grep for
error.*handling,import.*from - File paths: Use Glob for
*.test.ts,src/**/*.py
Commands
# Search the current project's index
leann search <index-name> "<query>" --top-k 5
# List available indexes
leann list
# Example
leann search rigg "how do providers handle streaming" --top-k 5
MCP Tool (in Claude Code)
leann_search(index_name="rigg", query="your semantic query", top_k=5)
Rebuilding the Index
When codebase changes significantly:
cd /path/to/project
leann build <project-name> --docs src tests scripts \
--file-types '.ts,.py,.md,.json' \
--no-recompute --no-compact \
--embedding-mode sentence-transformers \
--embedding-model all-MiniLM-L6-v2
How It Works
- LEANN uses sentence embeddings to understand meaning
- Searches find conceptually similar code, not just text matches
- Results ranked by semantic similarity score (0-1)
Grep vs LEANN Decision
| Query Type | Tool | Example |
|---|---|---|
| Natural language | LEANN | "how does caching work" |
| Class/function name | Grep | "class CacheManager" |
| Pattern matching | Grep | error|warning |
| Find implementations | LEANN | "rate limiting logic" |
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.
- 11d ago First seen · 69 lines · 14 tokens per session scan A 22c8cc193837
leann-search is a skill published in the GitHub repository parcadei/Continuous-Claude-v3 (3,937 stars, last pushed 7mo ago), licensed MIT. It adds 14 tokens to every session and 466 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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Use when attacking an AI/ML system or model — prompt injection & jailbreaks (Crescendo, Skeleton Key, Best-of-N), RAG/vector poisoning, agentic/MCP exploitation (CVE-2025-54136), ML supply-chain RCE (pickle CVE-2025-32434), model extraction / membership inference / adversarial suffixes (GCG).