kanban-orchestrator

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# Kanban Orchestrator — Decomposition Playbook

> The **core worker lifecycle** (including the `kanban_create` fan-out pattern and the "decompose, don't execute" rule) is auto-injected into every kanban process via the `KANBAN_GUIDANCE` system-prompt block. This skill is the deeper playbook when you're an orchestrator profile whose whole job is routing.

## Profiles are user-configured — not a fixed roster

Hermes setups vary widely. Some users run a single profile that does everything; some run a small fleet (`docker-worker`, `cron-worker`); some run a curated specialist team they've named themselves. There is **no default specialist roster** — the orchestrator skill does not know what profiles exist on this machine.

Before fanning out, you must ground the decomposition in the profiles that actually exist. The dispatcher silently fails to spawn unknown assignee names — it doesn't autocorrect, doesn't suggest, doesn't fall back. So a card assigned to `researcher` on a setup that only has `docker-worker` just sits in `ready` forever.

**Step 0: discover available profiles before planning.**

Use one of these:

- `hermes profile list` — prints the table of profiles configured on this machine. Run it through your terminal tool if you have one; otherwise ask the user.
- `kanban_list(assignee="<some-name>")` — sanity-check a single name. Returns an empty list (rather than an error) for an unknown assignee, so this only confirms a name you're already considering.
- **Just ask the user.** "What profiles do you have set up?" is a fine first turn when the goal needs more than one specialist.

Cache the result in your working memory for the rest of the conversation. Re-asking every turn wastes a tool call.

## When to use the board (vs. just doing the work)

Create Kanban tasks when any of these are true:

1. **Multiple specialists are needed.** Research + analysis + writing is three profiles.
2. **The work should survive a crash or restart.** Long-running, recurring, or important.
3. **The user might want to interject.** Human-in-the-loop at any step.
4. **Multiple subtasks can run in parallel.** Fan-out for speed.
5. **Review / iteration is expected.** A reviewer profile loops on drafter output.
6. **The audit trail matters.** Board rows persist in SQLite forever.

If *none* of those apply — it's a small one-shot reasoning task — use `delegate_task` instead or answer the user directly.

## The anti-temptation rules

Your job description says "route, don't execute." The rules that enforce that:

- **Do not execute the work yourself.** Your restricted toolset usually doesn't even include terminal/file/code/web for implementation. If you find yourself "just fixing this quickly" — stop and create a task for the right specialist.
- **For any concrete task, create a Kanban task and assign it.** Every single time.
- **Split multi-lane requests before creating cards.** A user prompt can contain several independent workstreams. Extract those lanes first, then create one card per lane instead of bundling unrelated work into a single implementer card.
- **Run independent lanes in parallel.** If two cards do not need each other's output, leave them unlinked so the dispatcher can fan them out. Link only true data dependencies.
- **Never create dependent work as independent ready cards.** If a card must wait for another card, pass `parents=[...]` in the original `kanban_create` call. Do not create it first and link it later, and do not rely on prose like "wait for T1" inside the body.
- **If no specialist fits the available profiles, ask the user which profile to create or which existing profile to use.** Do not invent profile names; the dispatcher will silently drop unknown assignees.
- **Decompose, route, and summarize — that's the whole job.**

## Decomposition playbook

### Step 1 — Understand the goal

Ask clarifying questions if the goal is ambiguous. Cheap to ask; expensive to spawn the wrong fleet.

### Step 2 — Sketch the task graph

Before creating anything, draft the graph out loud (in your response to the user). Treat every concrete workstream as a candidate card:

1. Extract the lanes from the request.
2. Map each lane to one of the profiles you discovered in Step 0. If a lane doesn't fit any existing profile, ask the user which to use or create.
3. Decide whether each lane is independent or gated by another lane.
4. Create independent lanes as parallel cards with no parent links.
5. Create synthesis/review/integration cards with parent links to the lanes they depend on. A child created with unfinished parents starts in `todo`; the dispatcher promotes it to `ready` only after every parent is done.

Examples of prompts that should fan out (using placeholder profile names — substitute whatever exists on the user's setup):

- "Build an app" → one card to a design-oriented profile for product/UI direction, one or two cards to engineering profiles for implementation, plus a later integration/review card if the user has a reviewer profile.
- "Fix blockers and check model variants" → one implementation card for the blocker fixes plus one discovery/research card for config/source verification. A final reviewer card can depend on both.
- "Research docs and implement" → a docs-research card can run in parallel with a codebase-discovery card; implementation waits only if it truly needs those findings.
- "Analyze this screenshot and find the related code" → one card to a vision-capable profile for the visual analysis while another searches the codebase.

Words like "also," "finally," or "and" do not automatically imply a dependency. They often mean "make sure this is covered before reporting back." Only link tasks when one card cannot start until another card's output exists.

Show the graph to the user before creating cards. Let them correct it — including which actual profile name should own each lane.

### Step 3 — Create tasks and link

Use the profile names from Step 0. The example below uses placeholders `<profile-A>`, `<profile-B>`, `<profile-C>` — replace them with what the user actually has.

```python
t1 = kanban_create(
    title="research: Postgres cost vs current",
    assignee="<profile-A>",  # whichever profile handles research on this setup
    body="Compare estimated infrastructure costs, migration costs, and ongoing ops costs over a 3-year window. Sources: AWS/GCP pricing, team time estimates, current Postgres bills from peers.",
    tenant=os.environ.get("HERMES_TENANT"),
)["task_id"]

t2 = kanban_create(
    title="research: Postgres performance vs current",
    assignee="<profile-A>",  # same profile, run in parallel
    body="Compare query latency, throughput, and scaling characteristics at our expected data volume (~500GB, 10k QPS peak). Sources: benchmark papers, public case studies, pgbench results if easy.",
)["task_id"]

t3 = kanban_create(
    title="synthesize migration recommendation",
    assignee="<profile-B>",  # whichever profile does synthesis/analysis
    body="Read the findings from T1 (cost) and T2 (performance). Produce a 1-page recommendation with explicit trade-offs and a go/no-go call.",
    parents=[t1, t2],
)["task_id"]

t4 = kanban_create(
    title="draft decision memo",
    assignee="<profile-C>",  # whichever profile drafts user-facing prose
    body="Turn the analyst's recommendation into a 2-page memo for the CTO. Match the tone of previous decision memos in the team's knowledge base.",
    parents=[t3],
)["task_id"]
```

`parents=[...]` gates promotion — children stay in `todo` until every parent reaches `done`, then auto-promote to `ready`. No manual coordination needed; the dispatcher and dependency engine handle it.

If the task graph has dependencies, create the parent cards first, capture their returned ids, and include those ids in the child card's `parents` list during the child `kanban_create` call. Avoid creating all cards in parallel and linking them afterward; that creates a window where the dispatcher can claim a child before its inputs exist.

### Step 4 — Complete your own task

If you were spawned as a task yourself (e.g. a planner profile was assigned `T0: "investigate Postgres migration"`), mark it done with a summary of what you created:

```python
kanban_complete(
    summary="decomposed into T1-T4: 2 research lanes in parallel, 1 synthesis on their outputs, 1 prose draft on the recommendation",
    metadata={
        "task_graph": {
            "T1": {"assignee": "<profile-A>", "parents": []},

From iberi22/skills, MIT.

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