make-trace
Turn any source that describes how a kind of task gets done (a SKILL.md, a chat log, a runbook, plain prose) into a runnable Morph trace. Lift it into a DAG, write a contract per step, place inputs, and drive the full run lifecycle to verify it. Use whenever someone wants to make a trace from a source.
npx skills add AIScientists-Dev/Flowtrace --skill make-trace --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Make Trace You turn a **source** (anything that describes how a kind of task gets done) into a **trace**: a folder holding a DAG that a human and an AI both read while the work runs. This skill covers the whole path, from a blank folder to a finished run. ## Before you start Two reads give you the full surface. Do them once: 1. `references/CLI.md` (bundled next to this file) is the system contract: every command, the `trace.json` schema, the reply payload schema, the path rules, the state machine. 2. The source itself. Read it closely; the steps you need are usually hiding in its prose. For a large or multi-file source, read the spine in full (the main document and any workflow section) and only sample the rest to confirm a step exists, rather than reading every file to the same depth. The `flowtrace` binary drives everything. Get it in this order: honor `$TRACE_BIN` if it is set; else use it if it is on your `PATH`; else, inside a flowtrace checkout, use the build under `target/` (`target/release/flowtrace`, else `target/debug/flowtrace`) or build one with `./scripts/install.sh` from the repo root; else clone the repo first (`git clone https://github.com/AIScientists-Dev/Flowtra
- Before you start
- The cycle
- 1. Scaffold
- 2. Lift the source into a DAG (the hard part)
- 3. Verify faithfulness (do not skip)
- 4. Write a contract per step
- 5. Provide inputs
- 6. Run the lifecycle
- 7. Watch it
- Reuse a trace on new input
- Steer a run: change a step, re-run what depends on it
- Precipitating a completed run
- Rules that trip people
- The one thing this cannot hand you
cd <wherever you keep traces> # conventionally ~/traces/ flowtrace init <slug> # creates <slug>/ with .git and an empty trace.json flowtrace validate flowtrace show --fmt mermaid flowtrace step <id> running --message "..." flowtrace step <id> done --asset <file> flowtrace reply < reply.json flowtrace deliverable done --asset <step_id>/<final-output> flowtrace run show # confirm every step is done and the deliverable is done flowtrace serve # opens the DAG at http://localhost:3000
What does the make-trace skill do?
Turn any source that describes how a kind of task gets done (a SKILL.md, a chat log, a runbook, plain prose) into a runnable Morph trace. Lift it into a DAG, write a contract per step, place inputs, and drive the full run lifecycle to verify it. Use whenever someone wants to make a trace from a source.
How do I install it?
Run `npx skills add AIScientists-Dev/Flowtrace --skill make-trace --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From AIScientists-Dev/Flowtrace, a repository with 484 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.