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feat(kanban): durable multi-profile collaboration board (#17805)

Browse source 
Salvage of PR #16100 onto current main (after emozilla's #17514 fix
that unblocks plugin Pydantic body validation). History preserved on
the standing `feat/kanban-standing` branch; this squashes the 22
iterative commits into one clean landing.

What this lands:
- SQLite kernel (hermes_cli/kanban_db.py) — durable task board with
  tasks, task_links, task_runs, task_comments, task_events,
  kanban_notify_subs tables. WAL mode, atomic claim via CAS,
  tenant-namespaced, skills JSON array per task, max-runtime timeouts,
  worker heartbeats, idempotency keys, circuit breaker on repeated
  spawn failures, crash detection via /proc/<pid>/status, run history
  preserved across attempts.
- Dispatcher — runs inside the gateway by default
  (`kanban.dispatch_in_gateway: true`). Ticks every 60s, reclaims
  stale claims, promotes ready tasks, spawns `hermes -p <assignee>
  chat -q "work kanban task <id>"` with HERMES_KANBAN_TASK +
  HERMES_KANBAN_WORKSPACE env. Auto-loads `--skills kanban-worker`
  plus any per-task skills. Health telemetry warns on stuck ready
  queue.
- Structured tool surface (tools/kanban_tools.py) — 7 tools
  (kanban_show, kanban_complete, kanban_block, kanban_heartbeat,
  kanban_comment, kanban_create, kanban_link). Gated on
  HERMES_KANBAN_TASK via check_fn so zero schema footprint in normal
  sessions.
- System-prompt guidance (agent/prompt_builder.py KANBAN_GUIDANCE)
  injected only when kanban tools are active.
- Dashboard plugin (plugins/kanban/dashboard/) — Linear-style board
  UI: triage/todo/ready/running/blocked/done columns, drag-drop,
  inline create, task drawer with markdown, comments, run history,
  dependency editor, bulk ops, lanes-by-profile grouping, WS-driven
  live refresh. Matches active dashboard theme via CSS variables.
- CLI — `hermes kanban init|create|list|show|assign|link|unlink|
  claim|comment|complete|block|unblock|archive|tail|dispatch|context|
  init|gc|watch|stats|notify|log|heartbeat|runs|assignees` +
  `/kanban` slash in-session.
- Worker + orchestrator skills (skills/devops/kanban-worker +
  kanban-orchestrator) — pattern library for good summary/metadata
  shapes, retry diagnostics, block-reason examples, fan-out patterns.
- Per-task force-loaded skills — `--skill <name>` (repeatable),
  stored as JSON, threaded through to dispatcher argv as one
  `--skills X` pair per skill alongside the built-in kanban-worker.
  Dashboard + CLI + tool parity.
- Deprecation of standalone `hermes kanban daemon` — stub exits 2
  with migration guidance; `--force` escape hatch for headless hosts.
- Docs (website/docs/user-guide/features/kanban.md + kanban-tutorial.md)
  with 11 dashboard screenshots walking through four user stories
  (Solo Dev, Fleet Farming, Role Pipeline, Circuit Breaker).
- Tests (251 passing): kernel schema + migration + CAS atomicity,
  dispatcher logic, circuit breaker, crash detection, max-runtime
  timeouts, claim lifecycle, tenant isolation, idempotency keys, per-
  task skills round-trip + validation + dispatcher argv, tool surface
  (7 tools × round-trip + error paths), dashboard REST (CRUD + bulk
  + links + warnings), gateway-embedded dispatcher (config gate, env
  override, graceful shutdown), CLI deprecation stub, migration from
  legacy schemas.

Gateway integration:
- GatewayRunner._kanban_dispatcher_watcher — new asyncio background
  task, symmetric with _kanban_notifier_watcher. Runs dispatch_once
  via asyncio.to_thread so SQLite WAL never blocks the loop. Sleeps
  in 1s slices for snappy shutdown. Respects HERMES_KANBAN_DISPATCH_IN_GATEWAY=0
  env override for debugging.
- Config: new `kanban` section in DEFAULT_CONFIG with
  `dispatch_in_gateway: true` (default) + `dispatch_interval_seconds: 60`.
  Additive — no \_config_version bump needed.

Forward-compat:
- workflow_template_id / current_step_key columns on tasks (v1 writes
  NULL; v2 will use them for routing).
- task_runs holds claim machinery (claim_lock, claim_expires,
  worker_pid, last_heartbeat_at) so multi-attempt history is first-
  class from day one.

Closes #16102.

Co-authored-by: emozilla <emozilla@nousresearch.com>
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---
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]
---
# 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.

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---
name: kanban-worker
description: Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.
version: 2.0.0
metadata:
hermes:
tags: [kanban, multi-agent, collaboration, workflow, pitfalls]
related_skills: [kanban-orchestrator]
---
# Kanban Worker — Pitfalls and Examples
> You're seeing this skill because the Hermes Kanban dispatcher spawned you as a worker with `--skills kanban-worker` — it's loaded automatically for every dispatched worker. The **lifecycle** (6 steps: orient → work → heartbeat → block/complete) also lives in the `KANBAN_GUIDANCE` block that's auto-injected into your system prompt. This skill is the deeper detail: good handoff shapes, retry diagnostics, edge cases.
## Workspace handling
Your workspace kind determines how you should behave inside `$HERMES_KANBAN_WORKSPACE`:
| Kind | What it is | How to work |
|---|---|---|
| `scratch` | Fresh tmp dir, yours alone | Read/write freely; it gets GC'd when the task is archived. |
| `dir:<path>` | Shared persistent directory | Other runs will read what you write. Treat it like long-lived state. Path is guaranteed absolute (the kernel rejects relative paths). |
| `worktree` | Git worktree at the resolved path | If `.git` doesn't exist, run `git worktree add <path> <branch>` from the main repo first, then cd and work normally. Commit work here. |
## Tenant isolation
If `$HERMES_TENANT` is set, the task belongs to a tenant namespace. When reading or writing persistent memory, prefix memory entries with the tenant so context doesn't leak across tenants:
- Good: `business-a: Acme is our biggest customer`
- Bad (leaks): `Acme is our biggest customer`
## Good summary + metadata shapes
The `kanban_complete(summary=..., metadata=...)` handoff is how downstream workers read what you did. Patterns that work:
**Coding task:**
```python
kanban_complete(
summary="shipped rate limiter — token bucket, keys on user_id with IP fallback, 14 tests pass",
metadata={
"changed_files": ["rate_limiter.py", "tests/test_rate_limiter.py"],
"tests_run": 14,
"tests_passed": 14,
"decisions": ["user_id primary, IP fallback for unauthenticated requests"],
},
)
```
**Research task:**
```python
kanban_complete(
summary="3 competing libraries reviewed; vLLM wins on throughput, SGLang on latency, Tensorrt-LLM on memory efficiency",
metadata={
"sources_read": 12,
"recommendation": "vLLM",
"benchmarks": {"vllm": 1.0, "sglang": 0.87, "trtllm": 0.72},
},
)
```
**Review task:**
```python
kanban_complete(
summary="reviewed PR #123; 2 blocking issues found (SQL injection in /search, missing CSRF on /settings)",
metadata={
"pr_number": 123,
"findings": [
{"severity": "critical", "file": "api/search.py", "line": 42, "issue": "raw SQL concat"},
{"severity": "high", "file": "api/settings.py", "issue": "missing CSRF middleware"},
],
"approved": False,
},
)
```
Shape `metadata` so downstream parsers (reviewers, aggregators, schedulers) can use it without re-reading your prose.
## Block reasons that get answered fast
Bad: `"stuck"` — the human has no context.
Good: one sentence naming the specific decision you need. Leave longer context as a comment instead.
```python
kanban_comment(
task_id=os.environ["HERMES_KANBAN_TASK"],
body="Full context: I have user IPs from Cloudflare headers but some users are behind NATs with thousands of peers. Keying on IP alone causes false positives.",
)
kanban_block(reason="Rate limit key choice: IP (simple, NAT-unsafe) or user_id (requires auth, skips anonymous endpoints)?")
```
The block message is what appears in the dashboard / gateway notifier. The comment is the deeper context a human reads when they open the task.
## Heartbeats worth sending
Good heartbeats name progress: `"epoch 12/50, loss 0.31"`, `"scanned 1.2M/2.4M rows"`, `"uploaded 47/120 videos"`.
Bad heartbeats: `"still working"`, empty notes, sub-second intervals. Every few minutes max; skip entirely for tasks under ~2 minutes.
## Retry scenarios
If you open the task and `kanban_show` returns `runs: [...]` with one or more closed runs, you're a retry. The prior runs' `outcome` / `summary` / `error` tell you what didn't work. Don't repeat that path. Typical retry diagnostics:
- `outcome: "timed_out"` — the previous attempt hit `max_runtime_seconds`. You may need to chunk the work or shorten it.
- `outcome: "crashed"` — OOM or segfault. Reduce memory footprint.
- `outcome: "spawn_failed"` + `error: "..."` — usually a profile config issue (missing credential, bad PATH). Ask the human via `kanban_block` instead of retrying blindly.
- `outcome: "reclaimed"` + `summary: "task archived..."` — operator archived the task out from under the previous run; you probably shouldn't be running at all, check status carefully.
- `outcome: "blocked"` — a previous attempt blocked; the unblock comment should be in the thread by now.
## Do NOT
- Call `delegate_task` as a substitute for `kanban_create`. `delegate_task` is for short reasoning subtasks inside YOUR run; `kanban_create` is for cross-agent handoffs that outlive one API loop.
- Modify files outside `$HERMES_KANBAN_WORKSPACE` unless the task body says to.
- Create follow-up tasks assigned to yourself — assign to the right specialist.
- Complete a task you didn't actually finish. Block it instead.
## Pitfalls
**Task state can change between dispatch and your startup.** Between when the dispatcher claimed and when your process actually booted, the task may have been blocked, reassigned, or archived. Always `kanban_show` first. If it reports `blocked` or `archived`, stop — you shouldn't be running.
**Workspace may have stale artifacts.** Especially `dir:` and `worktree` workspaces can have files from previous runs. Read the comment thread — it usually explains why you're running again and what state the workspace is in.
**Don't rely on the CLI when the guidance is available.** The `kanban_*` tools work across all terminal backends (Docker, Modal, SSH). `hermes kanban <verb>` from your terminal tool will fail in containerized backends because the CLI isn't installed there. When in doubt, use the tool.
## CLI fallback (for scripting)
Every tool has a CLI equivalent for human operators and scripts:
- `kanban_show` ↔ `hermes kanban show <id> --json`
- `kanban_complete` ↔ `hermes kanban complete <id> --summary "..." --metadata '{...}'`
- `kanban_block` ↔ `hermes kanban block <id> "reason"`
- `kanban_create` ↔ `hermes kanban create "title" --assignee <profile> [--parent <id>]`
- etc.
Use the tools from inside an agent; the CLI exists for the human at the terminal.