api-and-interface-desi…
Guides stable API and interface design. Use when designing APIs, module boundaries, or any public interface. Use when creating REST or GraphQL endpoints,…
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
$ npx -y skills add kevinnft/ai-agent-skills --skill kanban-orchestrator --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/kanban-orchestratorContext preview
The summary Claude sees to decide when to auto-load this skill.
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
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
metadata:
hermes:
tags: [kanban, multi-agent, orchestration, routing]
related_skills: [kanban-worker]
origin: aggregated
source_license: MIT
source_repo: NousResearch/hermes-agent
source_url: https://github.com/NousResearch/hermes-agent/tree/main/skills/devops/kanban-orchestrator
language: en> 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.
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.
Your job description says "route, don't execute." The rules that enforce that:
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` |
Ask clarifying questions if the goal is ambiguous. Cheap to ask; expensive to spawn the wrong fleet.
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.
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.
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:
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": "wri191 attribution-first agent skills for Hermes Agent, Claude Code, Cursor — one installer, 28 categories, searchable catalog. See NOTICE for upstream attribution.
Repo: kevinnft/ai-agent-skills
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