hermes-agent/skills/devops/kanban-orchestrator/SKILL.md
Teknium 98db898c0b feat(skills): declare platforms frontmatter for all 79 undeclared built-in skills
Completes the Windows-gating coverage for the built-in skills/ tree. Every
bundled SKILL.md now carries an explicit platforms: declaration so the
loader (agent.skill_utils.skill_matches_platform) can skip-load skills
that don't fit the current OS.

74 skills declared cross-platform (platforms: [linux, macos, windows]):
  Creative (16): ascii-art, ascii-video, architecture-diagram, baoyu-comic,
    baoyu-infographic, claude-design, creative-ideation, design-md,
    excalidraw, humanizer, manim-video, p5js, pixel-art,
    popular-web-designs, pretext, sketch, songwriting-and-ai-music,
    touchdesigner-mcp
  Autonomous agents: claude-code, codex, hermes-agent, opencode
  Data/devops: jupyter-live-kernel, kanban-orchestrator, kanban-worker,
    webhook-subscriptions, dogfood, codebase-inspection
  GitHub: github-auth, github-code-review, github-issues,
    github-pr-workflow, github-repo-management
  Media: gif-search, heartmula, songsee, spotify, youtube-content
  MCP / email / gaming / notes / smart-home: native-mcp, himalaya,
    pokemon-player, obsidian, openhue
  mlops (non-broken): weights-and-biases, huggingface-hub, llama-cpp,
    outlines, segment-anything-model, dspy, trl-fine-tuning
  Productivity: airtable, google-workspace, linear, maps, nano-pdf,
    notion, ocr-and-documents, powerpoint
  Red-teaming / research: godmode, arxiv, blogwatcher, llm-wiki,
    polymarket
  Software-dev: debugging-hermes-tui-commands, hermes-agent-skill-authoring,
    node-inspect-debugger, plan, requesting-code-review, spike,
    subagent-driven-development, systematic-debugging,
    test-driven-development, writing-plans
  Misc: yuanbao

5 skills gated from Windows (platforms: [linux, macos]):
  mlops/inference/vllm (serving-llms-vllm)
    vLLM is officially Linux-only; Windows requires WSL.
  mlops/training/axolotl
    Axolotl's flash-attn + deepspeed + bitsandbytes stack is Linux-first.
  mlops/training/unsloth
    Requires Triton + xformers + flash-attn — Linux only in practice.
  mlops/models/audiocraft (audiocraft-audio-generation)
    torchaudio ffmpeg backend + encodec dependencies are Linux-first.
  mlops/inference/obliteratus
    Research abliteration workflow; relies on Linux-focused pytorch
    kernels and MLX — no first-class Windows path.

Same strict-over-lenient policy as the optional-skills sweep: when the
underlying tool's Windows support is rough, missing, or WSL-only, gate the
skill. Easier to un-gate after verified Windows support lands than to leak
partial support that manifests as mid-task failures.

Combined with prior commits in this branch, every bundled SKILL.md
(skills/ + optional-skills/) now has a platforms: declaration.
2026-05-08 14:27:40 -07:00

163 lines
9.1 KiB
Markdown

---
name: kanban-orchestrator
description: Decomposition playbook + specialist-roster conventions + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.
version: 2.0.0
platforms: [linux, macos, windows]
metadata:
hermes:
tags: [kanban, multi-agent, orchestration, routing]
related_skills: [kanban-worker]
---
# 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.
## 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.
- **If no specialist fits, ask the user which profile to create.** Do not default to doing it yourself under "close enough."
- **Decompose, route, and summarize — that's the whole job.**
## The standard specialist roster (convention)
Unless the user's setup has customized profiles, assume these exist. Adjust to whatever the user actually has — ask if you're unsure.
| Profile | Does | Typical workspace |
|---|---|---|
| `researcher` | Reads sources, gathers facts, writes findings | `scratch` |
| `analyst` | Synthesizes, ranks, de-dupes. Consumes multiple `researcher` outputs | `scratch` |
| `writer` | Drafts prose in the user's voice | `scratch` or `dir:` into their Obsidian vault |
| `reviewer` | Reads output, leaves findings, gates approval | `scratch` |
| `backend-eng` | Writes server-side code | `worktree` |
| `frontend-eng` | Writes client-side code | `worktree` |
| `ops` | Runs scripts, manages services, handles deployments | `dir:` into ops scripts repo |
| `pm` | Writes specs, acceptance criteria | `scratch` |
## 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). Example for "Analyze whether we should migrate to Postgres":
```
T1 researcher research: Postgres cost vs current
T2 researcher research: Postgres performance vs current
T3 analyst synthesize migration recommendation parents: T1, T2
T4 writer draft decision memo parents: T3
```
Show this to the user. Let them correct it before you create anything.
### Step 3 — Create tasks and link
```python
t1 = kanban_create(
title="research: Postgres cost vs current",
assignee="researcher",
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="researcher",
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="analyst",
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="writer",
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.
### Step 4 — Complete your own task
If you were spawned as a task yourself (e.g. `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 researchers parallel, 1 analyst on their outputs, 1 writer on the recommendation",
metadata={
"task_graph": {
"T1": {"assignee": "researcher", "parents": []},
"T2": {"assignee": "researcher", "parents": []},
"T3": {"assignee": "analyst", "parents": ["T1", "T2"]},
"T4": {"assignee": "writer", "parents": ["T3"]},
},
},
)
```
### Step 5 — Report back to the user
Tell them what you created in plain prose:
> I've queued 4 tasks:
> - **T1** (researcher): cost comparison
> - **T2** (researcher): performance comparison, in parallel with T1
> - **T3** (analyst): synthesizes T1 + T2 into a recommendation
> - **T4** (writer): turns T3 into a CTO memo
>
> The dispatcher will pick up T1 and T2 now. T3 starts when both finish. You'll get a gateway ping when T4 completes. Use the dashboard or `hermes kanban tail <id>` to follow along.
## Common patterns
**Fan-out + fan-in (research → synthesize):** N `researcher` tasks with no parents, one `analyst` task with all of them as parents.
**Pipeline with gates:** `pm → backend-eng → reviewer`. Each stage's `parents=[previous_task]`. Reviewer blocks or completes; if reviewer blocks, the operator unblocks with feedback and respawns.
**Same-profile queue:** 50 tasks, all assigned to `translator`, no dependencies between them. Dispatcher serializes — translator processes them in priority order, accumulating experience in their own memory.
**Human-in-the-loop:** Any task can `kanban_block()` to wait for input. Dispatcher respawns after `/unblock`. The comment thread carries the full context.
## Pitfalls
**Reassignment vs. new task.** If a reviewer blocks with "needs changes," create a NEW task linked from the reviewer's task — don't re-run the same task with a stern look. The new task is assigned to the original implementer profile.
**Argument order for links.** `kanban_link(parent_id=..., child_id=...)` — parent first. Mixing them up demotes the wrong task to `todo`.
**Don't pre-create the whole graph if the shape depends on intermediate findings.** If T3's structure depends on what T1 and T2 find, let T3 exist as a "synthesize findings" task whose own first step is to read parent handoffs and plan the rest. Orchestrators can spawn orchestrators.
**Tenant inheritance.** If `HERMES_TENANT` is set in your env, pass `tenant=os.environ.get("HERMES_TENANT")` on every `kanban_create` call so child tasks stay in the same namespace.
## Recovering stuck workers
When a worker profile keeps crashing, hallucinating, or getting blocked by its own mistakes (usually: wrong model, missing skill, broken credential), the kanban dashboard flags the task with a ⚠ badge and opens a **Recovery** section in the drawer. Three primary actions:
1. **Reclaim** (or `hermes kanban reclaim <task_id>`) — abort the running worker immediately and reset the task to `ready`. The existing claim TTL is ~15 min; this is the fast path out.
2. **Reassign** (or `hermes kanban reassign <task_id> <new-profile> --reclaim`) — switch the task to a different profile and let the dispatcher pick it up with a fresh worker.
3. **Change profile model** — the dashboard prints a copy-paste hint for `hermes -p <profile> model` since profile config lives on disk; edit it in a terminal, then Reclaim to retry with the new model.
Hallucination warnings appear on tasks where a worker's `kanban_complete(created_cards=[...])` claim included card ids that don't exist or weren't created by the worker's profile (the gate blocks the completion), or where the free-form summary references `t_<hex>` ids that don't resolve (advisory prose scan, non-blocking). Both produce audit events that persist even after recovery actions — the trail stays for debugging.