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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
VexJoy AI Agent with Intelligent Routing - /do routes plain-English requests to the right specialist agent and gates the work with reviews, tests, and a learning loop.
| Date | Stars |
|---|---|
| 2026-07-31 | 413 |
| 2026-08-06 | 413 |
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# VexJoy Agent <img src="docs/repo-hero.png" alt="VexJoy Agent" width="100%"> Essays and writing behind this toolkit live at [vexjoy.com](https://vexjoy.com). AI agents skip steps. "Looks correct" replaces running tests. "Trivial change" replaces verification. The agent confidently ships broken code because nothing structurally prevented it from skipping the work. Harnesses have a second problem: given only a skill list, they do not route eagerly enough, or correctly enough. Good skills sit unused. So this toolkit connects the skills, agents, and workflows we want directly into the harness, automatically. You don't have to understand what is here. Say what you want in plain English and you get all the value we have put into it: the right specialist with the right methodology, behind gates that demand exit codes, not assertions. <!-- Counts here must match the Four Layers table (~line 143). Verify both: python3 scripts/validate-doc-counts.py --> 44 domain agents, 119 workflow skills, 86 hooks, 129 scripts. Agents carry knowledge, skills enforce methodology, hooks block incomplete work, scripts handle determinism. Works across Claude Code (`/do`), Codex (`$do`), Factory (`/do`), Reasonix (`/do`). ## What It Looks Like ``` $ claude > /do debug this Go test Routing: go-engineer + systematic-debugging Phase 1/4: Reproduce: running test, capturing failure... Phase 2/4: Hypothesize: 3 candidates from stack trace... Phase 3/4: Verify: isolated root cause in connection pool timeout Phase 4/4: Fix: patch applied, test passing, PR opened ✓ Delivered: PR #847, fix connection pool timeout in health check ``` The router reads intent, picks a Go agent paired with a debugging skill, and runs the full lifecycle. You typed one sentence. The system did the rest. ## The Pipeline ``` ROUTE PLAN EXECUTE VERIFY DELIVER LEARN ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ │ /do │───▶│ Task │───▶│Agent │───▶│Tests │───▶│ PR │───▶│Record│ │Router│ │ Plan │ │+Skill│ │Gates │ │Branch│ │Evolve│ └──────┘ └──────┘ └──────┘ └──────┘ └──────┘ └──────┘ ``` ## Anti-Rationalization This is the single thing that separates it from "agent with a system prompt." | Agent Says | What Happens | |---|---| | "Code looks correct, skip tests" | Exit gate requires test output. Blocked. | | "Trivial change, no verification" | Hook blocks completion without evidence. | | "Similar to before" | Skill demands case-specific proof. | | "User is in a hurry" | Protocol overrides time pressure. | | "I'm confident" | Gate demands exit code, not assertion. | Hooks fire automatically. Gates block completion. Skills encode counter-arguments at every skip-worthy step. The agent verifies or it doesn't finish. For what I do, the difference is enormous. If you're doing simple single-file edits, maybe less so. ## Knowledge Work Is First-Class The same routing serves knowledge work. The content engine researches, drafts in a calibrated voice, validates against 397 AI patterns, and repurposes finished pieces for each platform. `/html` turns any request into a single self-contained HTML file: report, slide deck, prototype, data viz, diagram. Non-engineers who try the toolkit consistently name the HTML artifacts as the thing they love. No code, no setup beyond the installer. ## It Proves Its Own Changes Changes to the toolkit itself ship with evidence. New skills get blind A/B tests against a no-skill baseline before merge. Routing and writing-standard decisions carry measured verdicts; [PHILOSOPHY.md](docs/PHILOSOPHY.md) cites the numbers. Experiments that lost go into the negative-results registry, [what-didnt-work.md](docs/what-didnt-work.md), so no future session re-runs a known-dead path. The automated nightly evolution loop (`/evolve`, writes to `evolution-reports/`) ran regularly through mid-May 2026. It is currently dormant; recent evidence has come from manual
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6189b0781d7e2f57, llm:Repository topics and description: 'ai, ai-agents, ai-pipelines, ai-skills, ai-workflows' and 'VexJoy AI Agent with Intelligent Routing - /do routes plain-English requests to the right specialist agent and gates the work with reviews, tests, and a learning loop.'
matched fp:6189b0781d7e2f57, llm:Repository topics and description: 'ai, ai-agents, ai-pipelines, ai-skills, ai-workflows' and 'VexJoy AI Agent with Intelligent Routing - /do routes plain-English requests to the right specialist agent and gates the work with reviews, tests, and a learning loop.'
matched fp:6189b0781d7e2f57, llm:Repository topics and description: 'ai, ai-agents, ai-pipelines, ai-skills, ai-workflows' and 'VexJoy AI Agent with Intelligent Routing - /do routes plain-English requests to the right specialist agent and gates the work with reviews, tests, and a learning loop.'