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$ npm run verify ✓ obsidian-claude-weekly-review 5/5 ✓ tldraw-make-real-sketch-to-code 5/5 ✓ obsidian-agent-harness 7/7 ✓ dedupe-and-rank-a-keyword-list 2/2 38/38 fixtures green · machine-verified
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View all →Orch8: validate a bounded durable ReAct loop before model or tool calls
Pin and structurally validate Orch8's durable ReAct sequence before deployment, so an unbounded loop, broken route, embedded endpoint, credential field, or unresolved block reference fails in CI before any model or tool can run.
agent-qa: validate Agent Skills before LLM-backed QA runs
Run agent-qa's deterministic skill-pack validator before its authoring, result-triage, or debug-fix skills can drive a model-backed web or mobile QA run, so missing metadata, references, or MCP guidance fail in CI first.
Buzz + Hermes over ACP: prove channel scope bounds what an agent reads, because signing is provenance not authorization
Run Hermes as an ACP channel member inside Block's Buzz workspace without giving it your whole workspace to read, by proving that Buzz's signed event log is complete provenance yet not authorization, so only scoping the agent to one narrow channel bounds what it can read.
n8n as an MCP server: prove each exposed workflow is one narrow tool behind its own token
Expose n8n workflows to an agent through MCP Server Triggers without handing it your whole toolbox, by proving each agent-callable trigger is one narrow workflow behind its own bearer token with no wildcard-HTTP or delete-capable node, so a leaked token or a prompt injection is bounded.
QM: prove the destructive-action denials hold in every security posture, even the loosest
Run YC's company-wide QM agent with one org-wide security posture, and prove the predeclared hard denials (recursive delete, destructive SQL, mass delete) are refused in every posture including the loosest, so loosening the posture only relaxes approvals, never the destructive-action guardrails.
pgvector on Postgres you already run: prove you picked the right distance operator
Use pgvector in the Postgres you already run instead of standing up a dedicated vector database, and prove the distance operator you query with (<-> L2, <=> cosine, or <#> inner product) matches your embeddings, so your semantic search doesn't silently rank the wrong rows first.
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