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Claim Agent Ownership

OrganizationPopular
Prismer-AI
claim-agent-ownership

Orchestrator skill for resolving multi-daemon binding contention. Use when you (the orchestrator) detect an `agent.binding.contested` sync event indicating two daemons are racing for the same agent — explicitly rebind ownership to a chosen target daemon so subsequent dispatches route deterministically. Implements Gap G-2-⑤ on top of the Wave 2-B2 `/api/im/agent-bindings/:agentImUserId/rebind` endpoint.

Overview

PublisherPrismer-AI
RepositoryPrismerCloud
Skill nameclaim-agent-ownership
Stars
1.6K
Forks
14
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Prismer-AI on GitHub. Read the source before you install it.

Installation

Install the Claim Agent Ownership AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git /tmp/PrismerCloud
mkdir -p .claude/skills
cp -r /tmp/PrismerCloud/sdk/prismer-cloud/built-in-skills/claim-agent-ownership .claude/skills/claim-agent-ownership
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Claim Agent Ownership in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Claim Agent Ownership on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Claim Agent Ownership is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Claim Agent Ownership

When two daemons (typically Mac Studio at home + a k8s pod) both run agent.host.declare for the same agent, the first one wins ownership and the second registers a contested binding. The user UI surfaces a "binding contested" badge and the cloud's resolveAgentDaemonRoute keeps routing dispatches to the original owner — but a human-driven or orchestrator-driven decision is needed to converge.

This skill is the orchestrator's tool for that decision. It calls the server's authoritative rebind endpoint, which atomically:

  1. flips im_agent_bindings.boundDaemonId to the chosen target daemon
  2. writes an audit row with boundBy='user-explicit' (or 'orchestrator')
  3. returns the in-flight task count on the previous owner so the orchestrator can wait / drain before redirecting traffic
  4. emits a sync event (agent.binding.rebound) so other daemons drop their hosting state for the agent

When to use

  • A sync event with type agent.binding.contested arrives in your inbox.
  • A user explicitly asks "the agent is bouncing between machines — pin it to my laptop".
  • You (orchestrator) decide to migrate an agent off a misbehaving daemon (high error rate, stale heartbeats, etc.) and a target is available.
  • Devices panel shows >1 daemon claiming the same agent and the user requests arbitration.

Do not use it for:

  • Healthy single-daemon bindings (no contention — nothing to claim).
  • Agents the orchestrator does not own / has no permission for (server rejects 403).
  • Routing decisions that should be reversible at the chat-message level (use metadata.daemonId overrides in single dispatches instead).

API

There is no generic cloud im get / cloud im post subcommand in the runtime CLI today (release 201 audit, sdk/prismer-cloud/runtime/src/cli/). A dedicated cloud agent rebind verb is not landed either. The skill calls the cloud HTTP endpoint directly via curl (or the equivalent fetch from the orchestrator's MCP runtime), authenticating with the daemon API key already present in the environment as $PRISMER_API_KEY:

bash
# Inspect the current bindings first.
curl -fsS \
  -H "Authorization: Bearer $PRISMER_API_KEY" \
  "$PRISMER_CLOUD_BASE/api/im/workspaces/<workspaceId>/agent-bindings"

# Issue the rebind. Requires workspace owner / active orchestrator / admin.
curl -fsS \
  -X POST \
  -H "Authorization: Bearer $PRISMER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"targetDaemonId":"<daemonId>","targetDaemonKind":"local","reason":"<short reason>"}' \
  "$PRISMER_CLOUD_BASE/api/im/agent-bindings/<agentImUserId>/rebind"

PRISMER_CLOUD_BASE defaults to the cloud the daemon paired with (https://prismer.cloud in prod, http://127.0.0.1:3000 in local dev).

For pure-API use (orchestrator runtime, MCP tool, etc.), the underlying call is:

http
POST /api/im/agent-bindings/:agentImUserId/rebind
Authorization: Bearer <daemon api key>
Content-Type: application/json

{
  "targetDaemonId":   "daemon-mac-studio-01",   // required, must exist in workspace
  "targetDaemonKind": "local",                  // optional: 'k8s' | 'local' | 'edge'
  "targetDaemonLabel": "Mac Studio (home)",     // optional display label
  "reason":           "user prefers desktop daemon while on home network"
}

Successful response:

json
{
  "ok": true,
  "data": {
    "binding": {
      "agentImUserId":     "u_agent_ceo",
      "boundDaemonId":     "daemon-mac-studio-01",
      "boundDaemonKind":   "local",
      "boundDaemonLabel":  "Mac Studio (home)",
      "boundBy":           "user-explicit",
      "boundAt":           "2026-05-22T16:30:00.000Z"
    },
    "inFlightTaskCount":   3,
    "previousDaemonId":    "daemon-k8s-pod-7f4"
  }
}

Failure modes worth handling explicitly:

StatusCode (in body)Meaning
400target daemon ... not registeredTarget daemonId not in im_containers for this workspace.
400 / missing fieldtargetDaemonId is requiredBody parse / missing required field — surface as developer error.
403ForbiddenCaller is not workspace owner / orchestrator / admin.
404Agent not found ...Agent has no im_agent_cards row (deleted? wrong id?).
404No binding row exists ...Agent never ran host.declare. Tell the daemon to declare first.
409target daemon ... is stoppedTarget container has stoppedAt set — pick a different target.

Workflow

  1. Subscribe to agent.binding.contested sync events. The orchestrator's sync inbox delivers these out of band; the skill consumes them.
  2. Resolve the target daemon. Read the binding list and pick the daemon you want to keep — usually the one with:
    • smaller lastDispatchAt skew (fresher),
    • matching device kind to user preference (local vs k8s),
    • lower contestCount (less flapping).
  3. Decide whether to drain. If inFlightTaskCount > 0 on the previous owner, surface this to the user ("3 tasks finishing on the old daemon before switching"). The orchestrator should usually let those finish — the rebind takes effect for subsequent dispatches, in-flight tasks complete via the existing route.
  4. Call rebind. Pass targetDaemonId + reason. Optional kind/label improve UI readability.
  5. Verify. Read /workspaces/:wsId/agent-bindings again and confirm boundDaemonId matches your target. If still divergent, the daemon may have re-declared between read and write — repeat once.

Operating Rules

  • Never rebind to a daemon you cannot see in the workspace listing. The server validates via im_containers — calling with a fictional id returns 400. Don't guess.
  • Always supply a reason. It's persisted in the audit trail and surfaces in the Devices panel ("rebound by orchestrator at HH:MM: "). Empty reason becomes the default 'unspecified' which is opaque to the user.
  • One rebind per binding per minute (orchestrator self-limit). The endpoint is not throttled, but flapping rebinds spam sync events and confuse the user. If the contest re-fires within 60s of a rebind, escalate to the human — don't auto-rebind again.
  • Wait for the dispatch in-flight count to drain before declaring "done" if the user is observing. The cloud routes new dispatches immediately, but the prior daemon's outstanding tasks still finish on it.

Output reporting

After successfully calling rebind:

[claim-agent-ownership] rebound agent=<agentImUserId> from=<previousDaemonId>
                       to=<targetDaemonId> in-flight-on-previous=<n>
                       reason="<reason>"

Then surface to the user as a chat message: who you rebound, where to, why, and how many tasks are still finishing on the old daemon (if any).

After a 4xx/5xx failure: report the HTTP status + the server's error code + message verbatim. Do not silently retry on 400/403/404 — those need human attention.

Backing capabilities

  • Server endpoint: POST /api/im/agent-bindings/:agentImUserId/rebind (Wave 2-B2, src/im/api/agent-bindings.ts)
  • Data model: IMAgentBinding (migration 410, fields: boundDaemonId, boundDaemonKind, boundDaemonLabel, boundBy, contestCount, contestedSince)
  • Sync event consumed: agent.binding.contested — emitted by AgentBindingService when a second daemon declares an agent already bound to a different daemon.
  • Sync event emitted (server-side): agent.binding.rebound — broadcast after successful rebind so all daemons + UI consumers drop stale routing state.
  • Audit trail: Each rebind writes an IMTaskLog-style entry with boundBy='user-explicit' / 'orchestrator' and the supplied reason; visible in the Devices panel.

Examples

Example 1 — Orchestrator handling a contest event

Inbox event: { type: 'agent.binding.contested', agentImUserId: 'u_agent_ceo',
               existingDaemonId: 'daemon-k8s-pod-7f4',
               contendingDaemonId: 'daemon-mac-studio-01' }

Orchestrator inspects bindings:
  curl -H "Authorization: Bearer $PRISMER_API_KEY" \
    "$PRISMER_CLOUD_BASE/api/im/workspaces/ws_abc/agent-bindings"

Decision: user is on home network, prefer Mac Studio (local).

Action:
  curl -X POST -H "Authorization: Bearer $PRISMER_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{"targetDaemonId":"daemon-mac-studio-01","targetDaemonKind":"local",
         "reason":"user on home network, preferring local daemon"}' \
    "$PRISMER_CLOUD_BASE/api/im/agent-bindings/u_agent_ceo/rebind"

Response: boundDaemonId='daemon-mac-studio-01', inFlightTaskCount=2
Orchestrator messages user: "Pinned CEO agent to your Mac Studio. 2 tasks finishing on
                            the cloud pod first."

Example 2 — User explicit "pin agent X to laptop"

User: "Make sure DesignAgent always runs on my laptop, not the k8s pod."

Orchestrator (after reading bindings):
  curl -X POST -H "Authorization: Bearer $PRISMER_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{"targetDaemonId":"daemon-laptop-9a","targetDaemonKind":"local",
         "reason":"user-explicit: pin to laptop"}' \
    "$PRISMER_CLOUD_BASE/api/im/agent-bindings/u_design_agent/rebind"

Example 3 — Failure to handle gracefully

Orchestrator tries to rebind to a daemon that just went offline:
  POST .../rebind { targetDaemonId: "daemon-k8s-pod-7f4", ... }
  ← 409 { error: "target daemon daemon-k8s-pod-7f4 is stopped" }

Orchestrator should:
  1. Re-read /workspaces/<wsId>/agent-bindings to find a live alternative.
  2. If only one daemon remains and it's already the current owner, do nothing.
  3. Otherwise retry with the live candidate.

Anti-patterns

  • ❌ Calling rebind on every contested event without thinking — flap creates user confusion. Wait until the user signals preference or a clear health signal arrives.
  • ❌ Supplying targetDaemonKind / targetDaemonLabel that contradict the server's inference (e.g. claiming kind=k8s for a deviceType=local container). The server trusts the body so misuse leads to a wrong-shaped UI badge.
  • ❌ Treating inFlightTaskCount > 0 as a hard error — it's informational. The cloud has already redirected new dispatches; existing in-flight work simply finishes on the old route.
  • ❌ Re-binding while another orchestrator is also working on the same workspace without coordination. Use the workspace's orchestrator lease (orchestratorAgentId) to make sure you're the active arbiter before mutating bindings.

Frequently asked questions

What does the Claim Agent Ownership AI skill do?

Orchestrator skill for resolving multi-daemon binding contention. Use when you (the orchestrator) detect an `agent.binding.contested` sync event indicating two daemons are racing for the same agent — explicitly rebind ownership to a chosen target daemon so subsequent dispatches route deterministically. Implements Gap G-2-⑤ on top of the Wave 2-B2 `/api/im/agent-bindings/:agentImUserId/rebind` endpoint.

Why use Claim Agent Ownership on TypingMind?

Because you install it once and use it with any model. Claim Agent Ownership is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Claim Agent Ownership in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/prismer-cloud/built-in-skills/claim-agent-ownership. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Claim Agent Ownership?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Claim Agent Ownership?

As many as you like. As long as a model supports skills, you can use Claim Agent Ownership with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Claim Agent Ownership AI skill free?

Yes. It is published on GitHub by Prismer-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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