* docs: deep audit — fix stale config keys, missing commands, and registry drift Cross-checked ~80 high-impact docs pages (getting-started, reference, top-level user-guide, user-guide/features) against the live registries: hermes_cli/commands.py COMMAND_REGISTRY (slash commands) hermes_cli/auth.py PROVIDER_REGISTRY (providers) hermes_cli/config.py DEFAULT_CONFIG (config keys) toolsets.py TOOLSETS (toolsets) tools/registry.py get_all_tool_names() (tools) python -m hermes_cli.main <subcmd> --help (CLI args) reference/ - cli-commands.md: drop duplicate hermes fallback row + duplicate section, add stepfun/lmstudio to --provider enum, expand auth/mcp/curator subcommand lists to match --help output (status/logout/spotify, login, archive/prune/ list-archived). - slash-commands.md: add missing /sessions and /reload-skills entries + correct the cross-platform Notes line. - tools-reference.md: drop bogus '68 tools' headline, drop fictional 'browser-cdp toolset' (these tools live in 'browser' and are runtime-gated), add missing 'kanban' and 'video' toolset sections, fix MCP example to use the real mcp_<server>_<tool> prefix. - toolsets-reference.md: list browser_cdp/browser_dialog inside the 'browser' row, add missing 'kanban' and 'video' toolset rows, drop the stale '38 tools' count for hermes-cli. - profile-commands.md: add missing install/update/info subcommands, document fish completion. - environment-variables.md: dedupe GMI_API_KEY/GMI_BASE_URL rows (kept the one with the correct gmi-serving.com default). - faq.md: Anthropic/Google/OpenAI examples — direct providers exist (not just via OpenRouter), refresh the OpenAI model list. getting-started/ - installation.md: PortableGit (not MinGit) is what the Windows installer fetches; document the 32-bit MinGit fallback. - installation.md / termux.md: installer prefers .[termux-all] then falls back to .[termux]. - nix-setup.md: Python 3.12 (not 3.11), Node.js 22 (not 20); fix invalid 'nix flake update --flake' invocation. - updating.md: 'hermes backup restore --state pre-update' doesn't exist — point at the snapshot/quick-snapshot flow; correct config key 'updates.pre_update_backup' (was 'update.backup'). user-guide/ - configuration.md: api_max_retries default 3 (not 2); display.runtime_footer is the real key (not display.runtime_metadata_footer); checkpoints defaults enabled=false / max_snapshots=20 (not true / 50). - configuring-models.md: 'hermes model list' / 'hermes model set ...' don't exist — hermes model is interactive only. - tui.md: busy_indicator -> tui_status_indicator with values kaomoji|emoji|unicode|ascii (not kawaii|minimal|dots|wings|none). - security.md: SSH backend keys (TERMINAL_SSH_HOST/USER/KEY) live in .env, not config.yaml. - windows-wsl-quickstart.md: there is no 'hermes api' subcommand — the OpenAI-compatible API server runs inside hermes gateway. user-guide/features/ - computer-use.md: approvals.mode (not security.approval_level); fix broken ./browser-use.md link to ./browser.md. - fallback-providers.md: top-level fallback_providers (not model.fallback_providers); the picker is subcommand-based, not modal. - api-server.md: API_SERVER_* are env vars — write to per-profile .env, not 'hermes config set' which targets YAML. - web-search.md: drop web_crawl as a registered tool (it isn't); deep-crawl modes are exposed through web_extract. - kanban.md: failure_limit default is 2, not '~5'. - plugins.md: drop hard-coded '33 providers' count. - honcho.md: fix unclosed quote in echo HONCHO_API_KEY snippet; document that 'hermes honcho' subcommand is gated on memory.provider=honcho; reconcile subcommand list with actual --help output. - memory-providers.md: legacy 'hermes honcho setup' redirect documented. Verified via 'npm run build' — site builds cleanly; broken-link count went from 149 to 146 (no regressions, fixed a few in passing). * docs: round 2 audit fixes + regenerate skill catalogs Follow-up to the previous commit on this branch: Round 2 manual fixes: - quickstart.md: KIMI_CODING_API_KEY mentioned alongside KIMI_API_KEY; voice-mode and ACP install commands rewritten — bare 'pip install ...' doesn't work for curl-installed setups (no pip on PATH, not in repo dir); replaced with 'cd ~/.hermes/hermes-agent && uv pip install -e ".[voice]"'. ACP already ships in [all] so the curl install includes it. - cli.md / configuration.md: 'auxiliary.compression.model' shown as 'google/gemini-3-flash-preview' (the doc's own claimed default); actual default is empty (= use main model). Reworded as 'leave empty (default) or pin a cheap model'. - built-in-plugins.md: added the bundled 'kanban/dashboard' plugin row that was missing from the table. Regenerated skill catalogs: - ran website/scripts/generate-skill-docs.py to refresh all 163 per-skill pages and both reference catalogs (skills-catalog.md, optional-skills-catalog.md). This adds the entries that were genuinely missing — productivity/teams-meeting-pipeline (bundled), optional/finance/* (entire category — 7 skills: 3-statement-model, comps-analysis, dcf-model, excel-author, lbo-model, merger-model, pptx-author), creative/hyperframes, creative/kanban-video-orchestrator, devops/watchers, productivity/shop-app, research/searxng-search, apple/macos-computer-use — and rewrites every other per-skill page from the current SKILL.md. Most diffs are tiny (one line of refreshed metadata). Validation: - 'npm run build' succeeded. - Broken-link count moved 146 -> 155 — the +9 are zh-Hans translation shells that lag every newly-added skill page (pre-existing pattern). No regressions on any en/ page.
9.8 KiB
title, sidebar_label, description
| title | sidebar_label | description |
|---|---|---|
| Kanban Orchestrator | Kanban Orchestrator | Decomposition playbook + specialist-roster conventions + anti-temptation rules for an orchestrator profile routing work through Kanban |
{/* This page is auto-generated from the skill's SKILL.md by website/scripts/generate-skill-docs.py. Edit the source SKILL.md, not this page. */}
Kanban Orchestrator
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.
Skill metadata
| Source | Bundled (installed by default) |
| Path | skills/devops/kanban-orchestrator |
| Version | 2.0.0 |
| Platforms | linux, macos, windows |
| Tags | kanban, multi-agent, orchestration, routing |
| Related skills | kanban-worker |
Reference: full SKILL.md
:::info The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active. :::
Kanban Orchestrator — Decomposition Playbook
The core worker lifecycle (including the
kanban_createfan-out pattern and the "decompose, don't execute" rule) is auto-injected into every kanban process via theKANBAN_GUIDANCEsystem-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:
- Multiple specialists are needed. Research + analysis + writing is three profiles.
- The work should survive a crash or restart. Long-running, recurring, or important.
- The user might want to interject. Human-in-the-loop at any step.
- Multiple subtasks can run in parallel. Fan-out for speed.
- Review / iteration is expected. A reviewer profile loops on drafter output.
- 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
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:
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:
- Reclaim (or
hermes kanban reclaim <task_id>) — abort the running worker immediately and reset the task toready. The existing claim TTL is ~15 min; this is the fast path out. - 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. - Change profile model — the dashboard prints a copy-paste hint for
hermes -p <profile> modelsince 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.