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Agent Native

Community
boshu2
agent-native

Dispatch independent tasks to parallel workers or subagents without write collisions. Use when: running or planning agents in parallel, even two; check scopes before any launch.

Overview

Publisherboshu2
Repositoryagentops
Skill nameagent-native
Stars
450
Forks
42
Bundled files
9
LicenseApache-2.0
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.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by boshu2 on GitHub. Read the source before you install it.

Installation

Install the Agent Native 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.
    https://github.com/boshu2/agentops/tree/main/plugin/skills/agent-native
  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/boshu2/agentops.git /tmp/agentops
mkdir -p .claude/skills
cp -r /tmp/agentops/plugin/skills/agent-native .claude/skills/agent-native
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Native 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 Agent Native 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 Agent Native 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.

Agent Native

Launch and observe caller-selected agent sessions as explicit roles, and return runtime facts per packet. Execution does not validate output, and the runtime never becomes AgentOps lifecycle authority: an adapter cannot select AgentOps semantics, issue a binding verdict, or turn factory completion into delivery or validation proof. Orchestrate decides what to dispatch and when; this skill launches and observes.

Before launch, and while running

  • Validate the whole batch first. Every packet needs its selected executor, packet identity, all transitive effects, the actions it must not take (such as push, merge, history rewrite, tracker or external writes) and a canonical workspace-relative write scope in separate isolation. If any packet is invalid, launch none: starting the valid ones and fixing the rest later is a partial launch.
  • Compare canonical scopes. Resolve symlinks, normalize paths and compare case-insensitively so an alias cannot hide a collision. A lexical check alone cannot prove symlink or runtime isolation. Disjoint files are not independence: workers, readers included, contend on a resource one of them writes, installs into or holds exclusively, such as a bound port, a database it writes, a package install it mutates or a locked browser profile; give each its own instance or serialize only those. A shared model-provider limit is backpressure, launched in waves, not serialized (shared runtime).
  • Delivery is not engagement. A delivered prompt or an acknowledgement proves transport only; reading it as a working worker is prompt-send optimism. Prove engagement from observable state: session output, tool activity, changed files.
  • Capture before restart. Before a nudge, replacement or restart, capture the worker's observable state. A restart destroys the evidence of why it stalled, and rescue is usually cheaper than rerun. Silence or impatience alone does not justify a restart; any replacement stays within authority and remaining bounds.
  • Dispatch once. Keep each packet's identity with its result. An executor error is a result, not a retry trigger; repair and follow-up are the caller's authority. A launch call the runtime refuses, so that its worker never starts, is not a launch: correct a malformed call once, and treat a concurrency or capacity limit as backpressure, waiting natively for a slot and launching the validated batch in waves under it; what each freed slot runs first stays the caller's decision (Orchestrate). Running a packet inline or elsewhere is the caller's disclosed choice (refused launches).
  • Account for every input. When a packet carries many items (files, records, issues), give each a stable ID before launch and join the returned results back to that list. An item with no result is missing, not done; an output with no matching input is unexplained. Report both by ID. Name each return field and its allowed values in the packet when results will be counted, sorted, deduplicated or compared; parsed prose is where counts drift.
  • Prove integration before removal. Before a caller-authorized removal of a finished worker's workspace, branch or stash, compare its content with the integration target by patch or file diff, not by merged status: squash and rebase hide ancestry. Preserve uncommitted, unique or unintegrated content in a recoverable form, such as an archive ref or bundle, first. Report what was kept, what was removed and the evidence for each.

Roles

  • Orchestrator: passes focused intent, scope and evidence references, names the integration/final-review owner, and reports runtime facts. Retrieve extra history only for a consequential uncertainty; a fresh context is not necessarily small. A new goal does not clear history or renew spent bounds.
  • Implementer: may modify only its packet's declared subject.
  • Validator: receives exact candidate content in a fresh, read-only context. It may supply judgment to Validate; only Validate writes verdict.v2.
  • Scribe: records runtime evidence without judging acceptance.

Reader and Writer are bounded cheap delegations, not roles with authority. A Reader returns line-referenced bullets over files the caller never loads, in slices of at most 350 lines. A Writer lands one patterned file from a spec plus a required reference file and returns a receipt the caller never reads back. Both are caller-selected per call and yield runtime facts only; a receipt is not validation. For Codex, use the source-owned bulk-reader and code-writer native roles; their model and sandbox are pinned in agents/bulk-reader.toml and agents/code-writer.toml. Context-budget delegation covers installation, native invocation, opt-in refusal hooks and the limits of role instructions.

Launch and observe

  1. Before starting a worker, require caller intent, role, workspace, starting commit, authorized source/output scope and evidence destination. Record the dispatch association in the caller-owned native channel before execution can fail. A requested ID is not an observed ID; worker identity stays unknown until the runtime reports it.
  2. At startup, before substantive work, capture the observed runtime, session and context identity and return it through that channel. Before its first edit, the worker confirms its HEAD is that starting commit; a mismatch is a launch failure, never repaired by reset. Report launch failures, recording gaps and unknowns; never fill them in. An authentication-shaped error from a sandboxed launch does not prove a logged-out account (launch and isolation). Session associations owns parent and resume links, multi-work spans and frozen source bounds.
  3. Keep concurrent writers disjoint and isolated, using the selected runtime's own isolation where it has one and never nesting a worktree inside the session's own (workspace isolation). Runtime coordination is not a claim, lease, queue or completion state in AgentOps.
  4. Observe through native waits or status notifications; observation costs time and context. Unchanged state is no reason for another analysis, review or retrospective, while a known blocking failure deserves action even as other jobs run. Stop at terminal status or the end of the caller's window.
  5. A headless worker cannot ask: on a choice its packet leaves open it takes no irreversible or out-of-scope step on a guess and returns the choice under gaps (open choices). For new authorized work after a worker completes, including an answer that settled intent supplies, use the runtime's documented follow-up or resume operation that starts a turn. A message operation only queues text for a running worker; a queued repair request is not a resumed attempt.
  6. Record provider state, transcript references, artifacts and terminal status. Provider retries, reconnects, idle states and failures stay runtime facts, never Plan, Candidate or verdict state. A result whose observed tool activity shows an excluded action is out of authority, not a candidate, whatever its checks show.

The batch contract's reference implementation, scripts/swarm/dispatch_once.py, needs an AgentOps source checkout and is not bundled with installed skills; installed use dispatches through the selected native runtime. It rejects a nonempty write_scope.exclude because its proof cannot honor exclusions. Batch mode selects no backlog work, creates no queue and integrates no changes.

Adapters and judgment

NTM, native processes, Agent Mail, Gas City and the one-shot headless runners (codex-exec, claude-exec, agy-native) are replaceable adapters. Use one only when the caller selected that execution shape; a single local agent pays no factory coordination cost.

For judgment, default to a fresh context in the author's model family. Cross-model Validate, mixed Council and dueling model perspectives are explicit caller selections. Model dispatch owns host authorization, isolation, finite input/output, timeout and cleanup for each leg; the working session is the controller, no factory is required, and Agent Mail is never the judgment path. Role requests declare authority, but only native runtime/OS filesystem and egress controls enforce it. Native judgment receipts define receipt references and the checks for caller-required model diversity; missing native identity never satisfies a leg. For exact native source spans, follow bounded raw source reads and its access and output limits.

Per-packet result

text
packet_id:        the caller's packet id
executor:         selected runtime; requested model; observed model if reported
observed_session: session/context id the runtime reported, else unknown
engagement:       observable evidence the worker started, else unobserved
last_activity:    time and kind of the last observed tool call, output or file change, else unobserved
terminal_status:  exit or terminal state | running | launch failed | not launched
result:           candidate <ref> | evidence <ref> | out of authority <action> | error <verbatim>
gaps:             recording failures and unknowns

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Native AI skill do?

Dispatch independent tasks to parallel workers or subagents without write collisions. Use when: running or planning agents in parallel, even two; check scopes before any launch.

Why use Agent Native on TypingMind?

Because you install it once and use it with any model. Agent Native 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 Agent Native in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/boshu2/agentops/tree/main/plugin/skills/agent-native. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Native?

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 Agent Native?

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

Is the Agent Native AI skill free?

Yes. It is published on GitHub by boshu2 under the Apache-2.0 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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