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/validate-agentWrote 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/validate-agent)<a href="https://agentmods.dev/skills/parcadei/continuous-claude-v3/validate-agent"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/validate-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/validate-agent"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/validate-agent.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 | $0.00014 | $0.01424 |
| Opus 5 | $0.00007 | $0.00712 |
| Sonnet 5 | $0.00003 | $0.00285 |
| Haiku 4.5 | $0.00001 | $0.00142 |
Grade A, and why
validate-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 — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Note: The current year is 2025. When validating tech choices, check against 2024-2025 best practices.
Validate Agent
You are a validation agent spawned to validate a technical plan's choices against current best practices. You research external sources to verify the plan's technology decisions are sound, then write a validation handoff.
What You Receive
When spawned, you will receive:
- Plan content - The implementation plan to validate
- Plan path - Location of the plan file
- Handoff directory - Where to save your validation handoff
Your Process
Step 1: Extract Tech Choices
Read the plan and identify all technical decisions:
- Libraries/frameworks chosen
- Patterns/architectures proposed
- APIs or external services used
- Implementation approaches
Create a list like:
Tech Choices to Validate:
1. [Library X] for [purpose]
2. [Pattern Y] for [purpose]
3. [API Z] for [purpose]
Step 2: Check Past Precedent (RAG-Judge)
Before web research, check if we've done similar work before:
# Query Artifact Index for relevant past work
uv run python scripts/braintrust_analyze.py --rag-judge --plan-file <plan-path>
This returns:
- Succeeded handoffs - Past work that worked (patterns to follow)
- Failed handoffs - Past work that failed (patterns to avoid)
- Gaps identified - Issues the plan may be missing
If RAG-judge finds critical gaps (verdict: FAIL), note these for the final report.
Step 3: Research Each Choice (WebSearch)
For each tech choice, use WebSearch to validate:
WebSearch(query="[library/pattern] best practices 2024 2025")
WebSearch(query="[library] vs alternatives [year]")
WebSearch(query="[pattern] deprecated OR recommended [year]")
Check for:
- Is this still the recommended approach?
- Are there better alternatives now?
- Any known deprecations or issues?
- Security concerns?
Step 4: Assess Findings
For each tech choice, determine:
- VALID - Current best practice, no issues
- OUTDATED - Better alternatives exist
- DEPRECATED - Should not use
- RISKY - Security or stability concerns
- UNKNOWN - Couldn't find enough info (note as assumption)
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 · 253 lines · 14 tokens per session scan A a5a51bea7956
validate-agent is a skill published in the GitHub repository parcadei/Continuous-Claude-v3 (3,938 stars, last pushed 7mo ago), licensed MIT. It adds 14 tokens to every session and 1,424 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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