I realized this data isn't just for review, audit, or to sit quietly in the background. It's a source of truth that can be used for building better workflows. I thought, "What if I could use my Entire session history to recreate that ‘package up a session’ magic, but in a way that works across any agent, and works retrospectively?"
The most popular way people are currently building reusable workflows is with Skills, so I built an orchestrator skill called Session-to-Skill. It creates Skills for me based on repeated behavior.
The Before and After
Before I used to say:
- "Look at past blog posts in this repo, check the folder structure, and the front matter."
- "I want to add a new blog post. Here’s the content: [insert content copied from google doc here] "
- "Create a new PR. Make sure we’ve pulled the latest from main and branch off main before you create this PR."
- "Why did you make the word Checkpoints lowercase when I purposely had them capitalized? Please restore that."
- "Does the OG image work? What’s the path for me to check that again?"
Now, I can say:
- "Create a blog post from this content [insert content copied from google doc here]."
This is possible because I prompted my agent to use the Session-to-Skill Skill: "Look at my past sessions where I set up blog posts. Find the repeated steps and conventions, then draft a Skill from that data, so I can create blog posts quickly in the future." My agent created a Skill called Create-blog, which included requirements to properly format the blog, open a PR, and return the path to confirm the OG image rendered.
Well, that’s kind of dumb..
Some may have pushback on this idea of me building an orchestrator Skill because at any moment in a session you can prompt any agent to turn it into a Skill.
The reality is I don’t have perfect foresight. Most reusable workflows are recognized later. After the third time I publish a blog post, I realize I have been doing the same thing over and over again. By then, the valuable evidence is spread across past sessions.
There is also the issue of quality. Asking an agent to summarize a transcript often leads to overfitting and noise. The resulting Skill might include accidental details, temporary file paths, or one-off preferences that happened to be present in that single session.
Instead my Skill is extracting the answers to the following questions:
- What was the reusable behavior?
- What should a future agent know before attempting this again?
I don't have to remember the session ID from six weeks ago. I just know the work happened. The Skill uses Entire to search my session metadata, checkpoints, and explanations of prior work to find the durable pattern.
Procedural Memory as Infrastructure
My approach creates procedural memory for agents. Procedural memory is the answer to the question, "How do I do this kind of work well, here, in this repo, with this team?"
Daily engineering work is not net-new. You may receive a new ticket, but somebody has solved this problem before.
By using Entire's data to generate Skills, I get a layer of determinism and portability. The agent starts with a template based on real work rather than a generic prompt. It encodes patterns that have already succeeded. And because Skills are portable files, I can take my blog-publishing Skill from Claude Code to Codex without re-explaining my workflow and share it with teammates.
With all this said, I want to urge readers to stop treating our agent sessions as disposable and start turning our history into our infrastructure.
Check out Entire at entire.io