renezander030

One query across GitHub, Notion, and your task app — cross-source agent memory for Claude Code (RRF, no vector database)

Connectors give your agent access to one tool each. This fuses them: a single search across every backend, ranked by meaning, each result tagged with where it came from.

Last tested: 2026-06-19 — @reneza/ats-mcp + @reneza/ats-adapter-composite. See Changelog at the bottom.

If this saves you setup time, follow @renezander030 — practical AI / coding-agent infrastructure notes.

Full repo with the MCP server + adapter source: github.com/renezander030/agentic-task-system

renezander030

GitHub issues as agent memory for Claude Code — hybrid retrieval over issues + discussions, no vector database

You already have a GitHub connector. This adds the semantic layer: the issues you use as scratchpads, ADRs, and RFC threads become retrievable by meaning.

Last tested: 2026-06-19 — @reneza/ats-mcp + @reneza/ats-adapter-github, GitHub REST 2022-11-28. See Changelog at the bottom.

If this saves you setup time, follow @renezander030 — practical AI / coding-agent infrastructure notes.

Full repo with the MCP server + adapter source: github.com/renezander030/agentic-task-system

renezander030

Notion as agent memory for Claude Code — hybrid retrieval over your pages, no vector database

You already have a Notion connector. This adds the semantic layer: retrieval ranked by meaning, so the agent pulls the right page on the first try.

Last tested: 2026-06-19 — @reneza/ats-mcp + @reneza/ats-adapter-notion, Notion API 2022-06-28. See Changelog at the bottom.

If this saves you setup time, follow @renezander030 — practical AI / coding-agent infrastructure notes.

Full repo with the MCP server + adapter source: github.com/renezander030/agentic-task-system

renezander030

AI agent action audit trail in SQLite: who approved what, when (GDPR Art. 22)

An append-only approval-audit table for human-in-the-loop AI pipelines: prove which operator approved which proposed action, what they actually saw, and when — and find actions that executed with no approval at all.

Last tested: June 2026. See Changelog at the bottom.

If this saves you setup time, follow @renezander030 — production AI agent governance in Go.

Reference implementation (MIT, Go): github.com/renezander030/draftcat.

renezander030

Self-improving voice AI agent: human-approved prompt diffs (Dograh + Go)

A governed learning loop for production voice agents: harvest call Learning-Items, group them, propose a workflow/prompt diff, gate it behind two human approvals, then publish an auto-versioned workflow.

Last tested: June 2026. See Changelog at the bottom.

If this saves you setup time, follow @renezander030 — production AI agent governance in Go.

Reference implementation (MIT, Go): github.com/renezander030/draftcat — build with -tags voice.

renezander030

Authenticating inbound agent webhooks in Go: constant-time bearer token, no timing leak, async 202 dispatch

How to accept an inbound HTTP trigger from another agent or harness in Go without a timing side-channel, an unauthenticated open door, or a request that blocks while the pipeline runs.

Last tested: June 2026. See Changelog at the bottom.

If this saves you from a == token check, follow @renezander030 — production notes on wiring agents together safely.

Working implementation: github.com/renezander030/draftcat (Go, MIT) — the webhook handler, constant-time auth, and the tests below all ship in the repo.

renezander030

LLM skills as YAML: prompt + output_schema validation + a linter (Go)

Keep agent prompts in versioned YAML files with a typed output schema, so you edit a prompt without recompiling and a linter catches a broken skill before it ships.

Last tested: June 2026. See Changelog at the bottom.

If this saves you from hardcoded prompt strings, follow @renezander030 — production notes on running LLM agents outside a demo.

Working implementation: github.com/renezander030/draftcat (Go, MIT) — the skill loader, output_schema validator, and draftcat validate linter below all ship in the repo.

renezander030

Test an LLM agent pipeline with fixtures: zero API calls, zero tokens, deterministic CI (Go)

How to dry-run a multi-step AI agent pipeline against recorded fixtures so go test and CI never hit the model, the CRM, or a real inbox.

Last tested: June 2026. See Changelog at the bottom.

If this saves you from burning tokens in CI, follow @renezander030 — production notes on testing agents that take real actions.

Working implementation: github.com/renezander030/draftcat (Go, MIT) — the draftcat test command below ships in the repo.

renezander030

Agent memory without a vector database — reciprocal rank fusion (RRF) over data you already curate

Hybrid retrieval (dense + sparse + keyword) with a 15-line RRF implementation, and bench numbers showing why fusion beats any single retriever.

Last tested: 2026-06-12 — Node 20, qdrant 1.x optional, nomic-embed-text via Ollama optional. See Changelog at the bottom.

If this saves you setup time, follow @renezander030 — practical AI / coding-agent infrastructure notes.

Working implementation: github.com/renezander030/agentic-task-system (packages/core/retrieval.js)

renezander030

Obsidian vault as agent memory for Claude Code — hybrid retrieval over plain markdown, no vector database

Point an MCP server at the folder of markdown you already keep. No Obsidian plugins, no REST API, no OAuth — files on disk.

Last tested: 2026-06-12 — @reneza/ats-adapter-obsidian v0.4, Claude Code via ats-mcp. See Changelog at the bottom.

If this saves you setup time, follow @renezander030 — practical AI / coding-agent infrastructure notes.

Full repo: github.com/renezander030/agentic-task-system