Just realized my software factory skills repo is ~1k github stars now 😬

To celebrate, I just added a karpathy style autoresearch skill, for spiking specific technical bottlenecks outside of the typical software factory loop.

What else should I add?

https://t.co/WcLfM9NhP6

dzhng/skills

Source: https://github.com/dzhng/skills

Skills

AI skills for building software factories. My personal library of domain-agnostic agent skills, reused across every project. Small, composable, and hackable — works with any harness that supports skills: Claude Code, Codex, opencode, Cursor, duet, and 70+ others.

npx skills add dzhng/skills

Add --list to pick individual skills, or copy any skills/<category>/<name>/ folder into your harness’s skills directory (e.g. .claude/skills/).

From a clone, npm run install-skills does the same without the registry:

npm run install-skills              # into ~/.agents/skills, linked from ~/.claude/skills
npm run install-skills -- ../my-app # into a repo instead of the home directory
npm run install-skills -- --only write-spec,review ../my-app
npm run list-skills                 # names and categories

.agents/skills/<name>/ holds the real files (flat, category-free, with cross-category links rewritten to match); .claude/skills/<name> is a relative symlink into it, so both harnesses read one copy. Re-running overwrites the installed copies — a .claude/skills/<name> you keep as a real directory is left alone, and a .claude/skills that is already a symlink is left as is. Add --dry-run to see the plan first.

Why

Software is moving from tasks to factories: agents that pursue a goal autonomously until the output can be trusted. The hard part isn’t breaking the goal into tasks — it’s breaking it into independently verifiable pieces, and knowing where the pieces even are.

These skills run that loop. Treat the unknown as fog of war: map the terrain, carve it into territories that build and verify in isolation, and recursively re-slice whatever hides more map. And re-planning doesn’t stop when planning ends — the spec is a living document, updated and re-sliced mid-implementation whenever the work teaches the agent that the plan is stale. Every piece must prove itself — architecture review, code review, and visual review against a baseline — before the loop moves on. Each iteration gets less wrong, until the goal is done.

Proof: one unattended Codex run pursuing a single goal for 1d 16h on top of these skills, slicing and iterating until done.

How to use

Use a chained pipeline to build a feature, a research loop to discover what works, or individual skills as needed. Every skill stands alone.

The full loop — a big feature, start to finish

1. Map the fog. /explore-unknowns on the idea. It interviews you quadrant by quadrant and hands you rendered options, mocks, and decision tables to react to instead of asking you to imagine. By the end you know what the feature does.

2. Codify. /write-spec on that map. Most decisions were already made upstream, so this pass is transcription — I don’t read the spec. Anything genuinely new it hits, it asks about instead of deciding.

3. Build. Kick off the loop:

   /goal /implement-spec specs/<feature>
   

   /goal is what puts the harness in loop mode — same move in Claude Code or Codex — and the spec drives it from there. A couple of hours for a small feature, two or three days for a large one. Add whatever framing fits: on the xyz branch, or using /codex as the implementer while you stay the parent orchestrator and reviewer.

4. Review the choices, not the diff. The run ends by consolidating specs/<feature>/choices.md — every decision the agent made where the spec was silent, ranked least-confident first. That’s the review surface. Send changes back and the next pass re-audits: every time the AI writes code, you audit what it chose.

   The rest fires on its own: a /review pass at the end of every slice, /screenshot-critique and /compare-screenshots on anything visual, /close-spec when the last slice lands, and a re-slice of the plan whenever implementation proves it stale.

Budget: 30 minutes to a few hours on steps 1–2, 30 minutes to a few hours on step 4. A run that goes two days is more like 2–3 hours on each end. Your time is in the bookends; the middle is unattended.

Research — learn through fast experiments

Use Auto Research when the next decision needs experimental evidence. It starts with one fast, revealing task, tests a short batch of hypotheses, checks combinations, and expands coverage as the approach improves. New failures become the focus; earlier tasks become regression checks.

/auto-research Reduce cost per task by at least 15% relative to the saved
baseline, without reducing task success. Start with one fast development task.

If the evaluator, metric, baseline, or required improvement is unclear, the skill asks before experimenting. Passing an evaluation and meeting an improvement target are separate requirements. The output includes the best verified artifact and a parameter-effect map: what was tested, where it helps or hurts, and how changes interact. Use that evidence to inform a spec when the research is ready for implementation.

À la carte — the spontaneous path

• A brainstorm turns out to be a feature. /explore-unknowns works at the end of a discussion as well as at the start — run it to sweep for the angles neither of you thought of, then pick the loop up at step 2.

• Any code change that didn’t come from a spec. An ad hoc fix that touched more than expected: /review first (refactor-clean → code-review → write-docs), then /audit-choices. When the diff is too big to read, the choices ledger is how you still understand what is now in your codebase.

Skills

Engineering — slice, build, verify, repeat

Visual review — never accept visuals on vibes

Authoring — keep the skills themselves sharp

Graphics

License

MIT