Epismo
A platform to share and manage powerful human-AI workflows
297 followers
A platform to share and manage powerful human-AI workflows
297 followers
Epismo is an all-in-one platform to work with AI as true teammates. Design tasks and steps, route each step to the best agent, and enforce quality with explicit checks. Clone proven workflows from the community, customize them to your needs, then publish your own for others to reuse. Start free and turn AI work into a repeatable process.
This is the 4th launch from Epismo. View more
Epismo Context Pack
Launched this week
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.
Free
Launch Team
Epismo
@hirokiyn Congrats. How would you recommend structuring a shared pack for a team's 'personal branding playbook' to make it plug-and-play for Claude or ChatGPT agents?
Epismo
@swati_paliwal Thanks! Honestly, agents can handle a lot of this.
I’d keep a few core sections stable, then let Claude or ChatGPT agents interpret and apply the pack for each task
The part about reusing context from other people's workflows is interesting. If I load someone's published context pack, does it just give me their prompts or does it actually carry over the decisions and reasoning behind why they built it that way?
Epismo
@abhra_das1 Not just prompts.
A Context Pack can carry the surrounding context too, like decisions, rationale, project background, conventions, and other working knowledge behind the workflow.
The goal is to reuse understanding, not only reuse prompt text.
how do you handle context conflicts when multiple agents write to the same memory pack?
Epismo
@mykola_kondratiuk Good question!
We keep track of how often a pack is used and favorited, so higher-value context naturally stands out over time while lower-value entries get pruned. A good pattern on top of that is to have agents periodically run an organize workflow to clean up, merge, and reconcile overlapping context.
That makes sense. Usage + favorites is basically organic quality filtering - the useful stuff surfaces itself. Works well as long as the initial pool stays manageable.
Epismo
@mykola_kondratiuk there might be better filtering in the future, although usage + favorites can work as organic quality filtering for humans.
For agents, I think the better model is ranking context by things like reuse success, consistency, freshness, and task relevance, so the most useful context gets selected more often over time.
reuse success and consistency are the right signals for agents. freshness I'd weigh lower - stale context often still works if it's stable.
Re explaining context in every workflow gets tiring fast. This idea of carrying memory across sessions feels useful. Keeping that memory updated over time is the tricky part.