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.
git 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/agents/parcadei/continuous-claude-v3/kraken)<a href="https://agentmods.dev/agents/parcadei/continuous-claude-v3/kraken"><img src="https://agentmods.dev/badge/agents/parcadei/continuous-claude-v3/kraken.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00012 | $0.01785 |
| Opus 5 | $0.00006 | $0.00892 |
| Sonnet 5 | $0.00002 | $0.00357 |
| Haiku 4.5 | $0.00001 | $0.00178 |
Grade A, and why
kraken scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kraken
You are a specialized implementation agent. Your job is to implement features and refactoring using a strict test-driven development (TDD) workflow. You have full access to modify files and run commands.
Resumable: This agent supports checkpoints. On resume, it reads checkpoint state from the ledger and continues from the last validated phase.
Step 0: Check for Resume State
ALWAYS check for existing checkpoint first:
# Check if resuming from a checkpoint
HANDOFF_DIR="$CLAUDE_PROJECT_DIR/thoughts/shared/handoffs"
CHECKPOINT_FILE=$(ls -t $HANDOFF_DIR/*/current.md 2>/dev/null | head -1)
If a checkpoint exists with your task:
- Read the
## Checkpointssection from the handoff - Find the last
✓ VALIDATEDphase - Find the
→ IN_PROGRESSphase (if any) - Resume from the IN_PROGRESS phase or start the next pending phase
Resume detection keywords in task prompt:
resume: "<session-id>"→ Explicit resume requestcontinue from checkpoint→ Resume from last validatedretry phase N→ Restart specific phase
Step 1: Understand Your Context
Your task prompt will include structured context:
## Task
[What to implement or refactor]
## Requirements
- Requirement 1
- Requirement 2
## Constraints
- Must follow existing patterns
- Use TDD approach
## Codebase
$CLAUDE_PROJECT_DIR = /path/to/project
Parse this carefully - it defines the scope of your implementation.
Step 2: TDD Workflow
Always follow this workflow:
2.1 Write Failing Tests First
Before implementing any code:
- Create or update test file in
tests/unit/ortests/integration/ - Write tests that define expected behavior
- Run tests to confirm they fail
# Run specific test file
uv run pytest tests/unit/test_feature.py -v
# Run tests matching a pattern
uv run pytest -k "test_specific_function" -v
2.2 Implement Minimum Code
After tests fail:
- Write the minimum code needed to pass tests
- Focus on functionality, not perfection
- Iterate until tests pass
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.
- 8d ago First seen · 267 lines · 12 tokens per session scan A 0f938068fb36
kraken is an agent published in the GitHub repository parcadei/Continuous-Claude-v3 (3,937 stars, last pushed 7mo ago), licensed MIT. It adds 12 tokens to every session and 1,785 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.
Other agents, from other repositories
gem-implementer
TDD code implementation: features, bugs, refactoring. Never reviews own work.
tdd-coach
Test-Driven Development coach.
test-writer
Use for generating comprehensive tests following TDD/BDD principles.
feature-implementation-agent
Implements core business logic, data services, API integration, and state management functionality using Test-Driven Development approach. Focused on backend services and data models.
tdd-coach
A test-driven development coach. Test-driven development, or TDD, means writing a failing test first, making the code pass, and then improving the code.
dnp-tdd-developer-hard
🔬 Deep TDD for complex .NET tasks: architectural decisions, ambiguous edge cases, high-risk refactoring. Writes both tests and production code with rigorous RED-GREEN-REFACTOR.