| name | workflow-creator |
| description | Turn a repeatable multi-step task into a runnable, reusable saved workflow through guided conversation, in either of two shapes — chaining existing skills/recordings (wiring each one's output into the next's input), or composing the workflow script's own primitives (agent/parallel/pipeline) directly when the task needs fresh sub-agent orchestration and no existing skill covers it. Figure out which shape fits, generate the Ruby workflow, dry-run it, and save it with workflow_save. Use when the user wants to combine / chain / orchestrate work into one repeatable flow — whether that's several EXISTING skills, or a from-scratch multi-agent script (parallel review, fan-out research, a pipeline over a list) — e.g. "chain these skills", "把这几个技能连起来", "串个流程", "做个 workflow", "编排一下", "build a workflow", "写个并行跑多个 agent 的脚本". Do NOT use to author a single new skill (that is skill-creator), to run one skill/agent a single time (just call it), or to design a recurring/self-triggering loop with its own trigger and state file (that is loop-engineering — a saved workflow is just the one-shot script such a loop calls, not the loop itself). |
Build a saved workflow
A saved workflow is a small Ruby (mruby) script that runs on demand or on a
schedule. Your job is to guide the user from a one-off description of what they
want repeated to a saved, validated workflow. It takes one of two shapes,
sometimes mixed in the same script:
- Skill chain — the script calls existing skills/recordings in order via
recording(...) / skill(...), passing each one's output to the next's
input. You are composing things that already exist.
- Primitive-composed — the script calls
agent(...) directly (alone, or
fanned out with parallel/pipeline) to do fresh sub-agent work that has no
matching existing skill — e.g. "review this diff across 3 dimensions in
parallel" or "run this check over every file in the list." There is nothing to
inventory here; you're writing the orchestration from scratch, just not the
agent-level logic (that's still an LLM call inside agent(...), not new Go/tool
code).
Figure out which shape (or mix) fits before drafting anything — see Step 0.
The pieces you use
agent(prompt, opts = {}) — run one sub-agent to completion, returns a String.
opts[:schema] (a JSON-schema string) makes the reply come back as a JSON
string matching it — parse it yourself with JSON.parse, unlike skill()'s
schema results, which arrive already parsed.skill(name, params = {}, opts = {}) — runs one existing SKILL.md skill as
a sub-agent and returns its result as native Ruby. opts[:schema] makes the
reply come back structured (already parsed).recording(name, params = {}) — replays one existing browser recording
deterministically and returns its declared outputs as a Ruby Hash.args — the workflow's input, a Ruby Hash, so the saved workflow is
parameterizable and reusable.parallel(items) { |it| ... } / pipeline(items, *stages) — for fan-out or
staged flows, over agent() / skill() / recording() calls.log(msg) / phase(title) — progress output, no effect on scheduling.- The
workflow tool — runs a script (in the background: returns a run id).
workflow_save — persists the script as a named workflow.
Steps
0. Decide the shape
First check this isn't actually a loop in disguise: if the user describes
something that keeps running on its own (daily/hourly cadence, "keep checking
until X", needs to remember state between runs, needs a done-condition or a
human gate) — that's loop-engineering's job, not this skill's. A saved
workflow is a single deterministic script; it has no opinion on when or how
often it's invoked. Redirect there instead of scoping cadence/state yourself.
Otherwise, ask what the workflow should do — its steps and their order, not the
broader scenario or how often it runs — then place it:
- Every step maps to a skill/recording the user already has → skill chain,
go to Step 1.
- The work is fresh sub-agent orchestration with no matching skill (a review
fanned out across dimensions, a search run in parallel over several sources, a
per-item pipeline) → primitive-composed, skip straight to Step 3 and write
agent/parallel/pipeline calls directly — there's nothing to inventory or
infer an output schema for, since you're writing the prompts yourself.
- Some steps are existing skills and others are ad hoc agent work → do both:
inventory only the skill steps (Step 1), and for the primitive steps just
decide the prompt and, if a later step needs structured output from it, a
schema.
1. Inventory what's available (skill-chain steps only)
Read the system prompt's # Available skills (SKILL.md skills) and
# Browser recordings (recordings) sections. List the candidates relevant to the
user's goal — with their params, and, for recordings, their outputs — and ask
which ones, in what order.
Check for a name collision: one name can exist as both a recording and a
SKILL.md skill. That's no longer an error — recording("x") always picks the
recording, skill("x") always picks the skill — but it IS a readability trap
for whoever reads the script later. Flag it, and prefer renaming one of them.
2. Wire outputs → inputs (skill-chain steps only)
For each adjacent pair, propose how the earlier step's output feeds the next
step's params, then have the user confirm before you generate anything. Be
honest that the two skill types give you different amounts to work with:
- Upstream is a browser recording — its
outputs are in the manifest
([outputs: files (file[])]). Wire directly, e.g. dl["files"] → the next
step's inputs. This is manifest-driven and reliable.
- Upstream is a SKILL.md skill — the manifest does not list its outputs.
Infer the likely output shape from the skill's description/body and propose a
call-site
schema for that step so its reply comes back structured. Show the
user the shape you assumed and ask them to confirm or fix it. You are
proposing, not reading a declared contract — don't present it as certain.
For a primitive-composed step feeding another primitive-composed step, the
same idea applies but there's no manifest at all: you decide both the upstream
agent()'s schema (if you need structured output) and how the result is used
downstream, and confirm your choice with the user the same way.
3. Generate the Ruby workflow
The script is Ruby (mruby), not JavaScript. Read inputs via args. Show it
to the user before running. Shape for a skill chain:
dl = recording("download-excels", { "month" => args["month"] })
tbl = skill("merge-excels", { "inputs" => dl["files"] },
schema: '{"type":"object","properties":{"path":{"type":"string"}},"required":["path"]}')
ppt = skill("excels-to-ppt", { "table" => tbl["path"] })
ppt
Shape for a primitive-composed workflow — no skill() call anywhere:
findings = parallel(["correctness", "security", "perf"]) do |dimension|
agent("Review the current diff for #{dimension} issues", read_only: true)
end
findings.each { |f| log(f) }
findings
A failed agent()/skill()/recording() raises and halts the run, so you
don't have to check each result for errors.
4. Dry-run to validate the wiring — it is asynchronous
The workflow tool runs in the background: it returns a run id, and you must
poll workflow_status(id) until the run reports done before judging it. Don't
declare success before results land; cap how many times you poll.
A dry-run actually executes the steps, and a browser recording drives a real
Chrome right now. So if the chain contains a recording:
- confirm Chrome is attached (port 9222) before dry-running, or
- validate only the SKILL.md tail by hand-feeding a sample value for the
recording's output (e.g. pass a couple of file paths as
inputs), and validate
the recording itself separately with the browser tool's replay.
Fix any wiring error and re-run before saving.
5. Save it and show how to run it
Call workflow_save(name, script, description, scope). scope defaults to
project (.octo/workflows) and errors when you're not inside a git repo —
if there's no repo, use scope: "user" (~/.octo/workflows, available across
projects). Ask the user if it's unclear. Confirm the name with them first.
Then tell them the three ways to run it:
- In chat — ask to run the workflow by name (passing
args).
- CLI / headless — run it by name.
- On a schedule — use the
cron-task-creator skill.
6. State the constraints
- A workflow that includes a browser recording needs a live Chrome every time
it runs — it errors clearly in a headless/cron run without one. Say so when
the flow has a recording in it. A pure primitive-composed workflow has no such
constraint.
- A SKILL.md or
agent() step's structured handoff depends on the call-site
schema you proposed in step 2; without one, that step returns free text.
Don't
- Don't author a new SKILL.md — that is
skill-creator.
- Don't design cadence/state/trigger for a recurring loop — that is
loop-engineering; don't ask "定时跑还是手动触发" or "这个 workflow 解决什么场景"
as scoping questions, since scheduling is only ever a consumer of a saved
workflow (mentioned once, in Step 5, as one of three ways to run it by name).
- Don't save before the user has confirmed the name and scope.
- Don't report the dry-run as passing before
workflow_status says done.
