Claude Code skill combines DeepMind Aletheia and Anthropic harness approaches

Technical implementation details
A developer has built a Claude Code skill that combines two recent research approaches: DeepMind's Aletheia math research agent and Anthropic's multi-agent coding architecture. The skill creates a synthesis that neither original implementation included.
The pipeline follows a Planner → Generator → Evaluator → Reviser structure. A key innovation is what the developer calls "blind pre-analysis" - the evaluator reasons about the correct approach before it ever sees the candidate code. This extends Aletheia's decoupling concept by having the evaluator form its own expectations first, then grade solutions against them.
After the blind analysis, the evaluator runs the code and grades it against concrete criteria: correctness, completeness, security, resilience, and quality. It returns a structured verdict (CORRECT / FIXABLE / WRONG) that drives targeted revision.
Installation and usage
Installation requires creating a directory and cloning the repository:
mkdir -p ~/.claude/skills/aletheia
# clone repo, copy SKILL.md + evaluator.md + planner.mdUsage examples from the source:
/aletheia Build a rate limiter middleware for Fastify using Redis/aletheia review src/routes/auth.ts/aletheia quick Fix the N+1 query in the dashboard
The skill is available at https://github.com/zhadyz/aletheia-harness.
Research context
DeepMind's Aletheia represents a shift from convergent thinking (reproducing known solutions) to divergent thinking (generating original mathematical results). Anthropic's harness research focused on multi-agent coding architecture but lacked chain-of-thought decoupling in the evaluator. This implementation combines both approaches into a single pipeline.
📖 Read the full source: r/ClaudeAI
👀 See Also

Exploring LiveDocs: An AI-native Data Analysis Notebook
LiveDocs offers a reactive notebook environment allowing data teams to perform multi-step analyses and maintain analysis end-to-end with the help of an AI agent.

Oodle.ai Launches Agent Observability at $10/Million Traces
Oodle.ai offers $10 per million agent traces with sub-second P99 query latency, storing 100% of traces without sampling in S3-based columnar storage.

OmniRecall Beta: FAISS-Powered Memory Injection for Cloud LLM Chats
OmniRecall is a local mitmproxy bypass that intercepts traffic to cloud chat interfaces like DeepSeek, adding a permanent memory layer using FAISS indexing and sentence-transformers MiniLM-L6. It's currently in beta, requires CPU-only operation, and uses an aggressively restrictive source-available license.

CostClaw: Free Local Cost Tracking Dashboard for OpenClaw Agents
CostClaw is a free, local plugin that captures every LLM call via OpenClaw's native hooks and provides a dashboard showing model breakdowns, per-session costs, and hourly spend charts. The developer discovered their heartbeat agent was running Claude Sonnet every 3 minutes 24/7, costing $60/month, and switching to Haiku cut their bill by ~65%.