Add vertical intelligence flywheel skills and artifacts#1198
Open
danielfoch wants to merge 1 commit intogarrytan:mainfrom
Open
Add vertical intelligence flywheel skills and artifacts#1198danielfoch wants to merge 1 commit intogarrytan:mainfrom
danielfoch wants to merge 1 commit intogarrytan:mainfrom
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary
Adds a small Vertical Intelligence Flywheel layer to GStack: skills and schemas for compressing repeated context, capturing human-approved runs as redacted traces, reviewing data-heavy work, and exporting training-ready examples for optional future open-weight SLM/adapters.
Why
A lot of OpenClaw/GStack usage happens inside vertical businesses: real estate, legal ops, local services, healthcare admin, insurance, finance ops, etc. The repeated work is valuable. If users can capture approved runs, compress context, create evals, and export clean traces, they can gradually build private vertical intelligence without adding telemetry or training anything inside GStack.
The goal is:
approved run → compressed context → eval case → training-ready trace → optional future SLM/adapter
What changed
/context-compress/trace-to-train/data-reviewNon-goals
Privacy model
Local-first, opt-in, redacted by default. The skills help users decide whether a run is retrieval-ready, eval-ready, or trainable. Anything client-facing, compliance-sensitive, or PII-bearing requires human review.
Model stance
Model-agnostic. Gemma or another open-weight model can be a downstream target later, but this PR only creates the context/eval/trace foundation.
Example
A realtor OpenClaw provider can capture approved lead-intake, listing-copy QA, CRM follow-up, and transaction-checklist runs. Over time those traces can become compact context cards, evals, and training examples for cheaper vertical inference.
Test plan
bun run gen:skill-docs --host allbun run skill:checkjq emptygit diff --checkFocused Bun tests were run, but the suite reports an existing large tracked fixture failure on
browse/test/fixtures/security-bench-haiku-responses.json, which is outside this PR.Full
bun testwas also attempted, but this local workspace has unrelated failures from the repo path containing spaces, missing git author config in temporary test repos, and existing browser test assertions outside the touched files.