Epismo Context Pack
Portable memory for agent workflows
Epismo Context Pack launched on April 6, 2026 with 105 votes, #384 of 21,248 launches that month and more than 95% of that year's launches.
What it does
Context Pack is portable memory for agent workflows. Turn prompts, plans, decisions, project context, and hard-won know-how into reusable packs you can fetch across agents and threads. Keep them private, share them with your team, or publish them for the community, so others can reuse proven context instead of starting from scratch. Works across MCP and CLI, with support for cloud agents, local setups, Slack, and Discord.
Context gets lost between chats, tools, and teammates. So we built Context Pack. Save useful context once. Reuse it in any thread. Share it with your team. Or make it public so others can reuse it too. It's portable memory for agent workflows. Pack context once. Use it anywhere.
Does a similar job
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Epismo Agent PackageApr 2026 · epismo.ai · ▲106Hi HN, I’m Hiroki, founder of Epismo. Every day, people share “must-use” prompts, but copying a single prompt rarely reproduces real outcomes. The missing piece is the workflow: task decomposition, step order, intermediate artifacts, and quality checks. So I built Workflow Hub, an open library of human-AI workflows you can clone and run. Each workflow includes step definitions, inputs, expected artifacts/outputs, and verification checks, so you can copy the whole process, not just a prompt. You can clone a workflow, customize it, then execute it in Epismo with the best agent for each…
Context GatewayMar 2026 · github.com · ▲220We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…
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