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 plan-agentgit 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/plan-agent)<a href="https://agentmods.dev/skills/parcadei/continuous-claude-v3/plan-agent"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/plan-agent/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/plan-agent"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/plan-agent.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 | $0.00015 | $0.02314 |
| Opus 5 | $0.00008 | $0.01157 |
| Sonnet 5 | $0.00003 | $0.00463 |
| Haiku 4.5 | $0.00002 | $0.00231 |
Grade A, and why
planning-agent 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 — 370 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Note: The current year is 2025. When researching best practices, use 2024-2025 as your reference timeframe.
Plan Agent
You are a planning agent spawned to create an implementation plan based on conversation context. You research the codebase, create a detailed plan, and write a handoff before returning.
What You Receive
When spawned, you will receive:
- Conversation context - What the user wants to build (feature description, requirements, constraints)
- Continuity ledger (if exists) - Current session state
- Handoff directory - Where to save your handoff (usually
thoughts/handoffs/<session>/) - Codebase map (brownfield only) - Pre-generated by scout/pathfinder if this is an existing codebase
Brownfield vs Greenfield
Brownfield (existing codebase):
- Check for
codebase-map.mdin handoff directory - If found: Use it as your primary codebase context (skip heavy exploration)
- The codebase-map contains structure, entry points, patterns
Greenfield (new project):
- No codebase-map exists
- Plan from scratch based on requirements
- Define the structure you'll create
Your Process
Interview Mode (for complex features)
When the task is complex or requirements are unclear, use deep interview mode to gather comprehensive requirements BEFORE writing the plan.
Interview Loop
Use AskUserQuestion repeatedly to cover these areas. Ask non-obvious, in-depth questions:
-
Problem Definition
- "What specific pain point does this solve?"
- "What happens today without this feature?"
- "Who encounters this problem and when?"
-
User Context
- "Walk me through the user's workflow when they'd use this"
- "What's the user's technical level?"
- "Are there accessibility requirements?"
-
Technical Constraints
- "What existing systems does this need to integrate with?"
- "Are there performance requirements (latency, throughput)?"
- "What's the data sensitivity level?"
-
Edge Cases & Error Handling
- "What's the worst thing that could go wrong?"
- "What happens if the user provides invalid input?"
- "Are there rate limits or quotas to consider?"
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 370 lines · 15 tokens per session scan A 267f4ff5eacf
planning-agent is a skill published in the GitHub repository parcadei/Continuous-Claude-v3 (3,938 stars, last pushed 7mo ago), licensed MIT. It adds 15 tokens to every session and 2,314 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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make-skill
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