The more determinism you have, the more consistent you can be and the more leverage you can build. (yes I understand that skill calls are non deterministic).
The more determinism you have, the more consistent you can be and the more leverage you can build. (yes I understand that skill calls are non deterministic).
One of the more interesting benefits to skills is that many harnesses now run the inline command(s) in backticks, shortcutting the model needing to make a tool call. This is helpful for deterministically building up context content for the skill before the agent ever sees it.
We take this further in some instances and have workflows that (1) does deterministic context gathering (2) invokes an agent (3) processes a file the agent is told to produce. This has made our PR review agent much better and removed it's access to all credential files. We have a step that gathers the diff + existing pull request comments into a .review dir, let the agent process that and create a comments.jsonl, then run a script in a new step to apply the comments against the API
Part of that is to create standardized "docs/{architecture, decisions, contracts, operations}" folders that my harness recognizes in my repos to have a better sense of where was it.
Glad others are converging towards similar ideas!
https://github.com/hofstadter-io/hof
(sharing because it has sloppy parts and that/I.S. shouldn't stop you from sharing!)