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Agent Startup And Context Ingestion

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mvschwarz
agent-startup-and-context-ingestion

Use when designing or auditing how an agent becomes useful after launch — AGENTS.md overlays, role files, skills, rig specs, workflow specs, startup checklists, refocus messages, "rig context" surface. Covers the 4 failure modes that make startup context fail (old rig spec misses current operating mode; current agents never told about new guidance; startup file as dumping ground; orchestrator transmits implementation without preserving product intent).

Overview

Publishermvschwarz
Repositoryopenrig
Skill nameagent-startup-and-context-ingestion
Stars
5K
Forks
366
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Agent Startup And Context Ingestion 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/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/agent-startup-and-context-ingestion
  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/mvschwarz/openrig.git /tmp/openrig
mkdir -p .claude/skills
cp -r /tmp/openrig/packages/daemon/specs/agents/shared/skills/core/agent-startup-and-context-ingestion .claude/skills/agent-startup-and-context-ingestion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Startup And Context Ingestion 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 Startup And Context Ingestion 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 Startup And Context Ingestion 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 Startup and Context Ingestion

How an agent becomes useful after launch: AGENTS.md overlays, role files, skills, rig specs, workflow specs, startup checklists, refocus messages, and the current rig context retrieval and profile surface.

It is the current concrete startup path inside the broader context-engineering-and-retrieval primitive. Startup gets a seat into the right initial shape; context engineering is the larger question of how a seat gets the right context for the work it is doing right now.

Most coordination failures are not tool failures; they are context failures. Agents need to know their role, operating mode, coordination convention, boundaries, and current product intent. If startup context is scattered or stale, agents execute the wrong thing very efficiently.

Use this when

  • Authoring a new agent's startup files (role / culture / startup-context)
  • Refreshing a seat that's been running on stale guidance
  • Auditing whether agents got the current operating mode (not just what's in old files)
  • Designing the orchestrator → next-agent context-transmission shape
  • Building a startup map for OpenRig-building rigs

Don't use this when

  • The agent is being created via Agent Starter — the starter's manifest carries startup context
  • The work is artifact-backed mental-model rebuild from a packet — that's session-compaction-and-restore
  • The intent is to ship reusable startup content as a skill

Failure modes (4)

  1. A new agent starts from an old rig spec and misses the current operating mode. Specs go stale; current state must be visible at startup, not just historical config.
  2. Guidance is written to a file that future agents read, but current agents are never told. File edits don't propagate to running sessions. Cultural rollout (broadcast + fleet-changes-feed) is needed alongside file edits.
  3. A startup file becomes a dumping ground and loses the map-to-canonical-sources role. Startup should point AT canonical sources; it shouldn't TRY to be one.
  4. The orchestrator transmits implementation instructions without preserving product intent. Instructions decay; intent travels.

Task-scoped startup

Run rig whoami --json, then resolve project.yaml -> mission.yaml -> active slice.yaml -> selected component or wave map -> addressed context. The complete lookup and precedence rule is docs/reference/product-journey-sdlc.md#resolve-the-selected-path (installed: $OPENRIG_HOME/reference/product-journey-sdlc.md#resolve-the-selected-path). Read the selected addresses and source needed for this task; skills available in your profile are capabilities, not a mandatory reading list. No composition means light Part A. Role names and idle seats add no gates. Explicit rigor and authored wave boundaries retain their named checks.

Startup files are address maps, not universal reading mandates. Do not preload unrelated planning/review doctrine or require per-file ACKs and quizzes. Skill availability is distinct from loading its full body. An old packet does not choose the current work. Missing selected context is a named gap to resolve.

Proof standard

In a disposable fresh session, observe the actual reads and resulting next action. Test no-selection, explicit rigor, and wave-boundary cases. An edit is not evidence that an already-running seat adopted it; adoption needs an authorized refresh or next-launch observation. Do not clear or re-prime a live seat merely to test prose.

Cross-runtime startup paths (5; do not collapse)

When a seat's startup is artifact-backed mental-model rebuild (active-work reentry case, distinct from reusable Agent Starters / priming packs):

  • See the cross-runtime restore/reentry packet standard v0
  • Source-trust ranking applies: rig whoami > target rigspec > bounded latest transcript > full transcript > touched-files > restore-summary.json

Memory surfaces consumed at startup

Inventory the selected startup inputs: AGENTS/role/CULTURE overlays, replay context, and any declared restore packet or starter. Record where each comes from, whether it is current, and why this task needs it. An available skill or old packet does not select the work.

Use the task-scoped startup path above to resolve current authority. Permission to read an input is not permission to rewrite its source; writes follow the active project/rig policy and assignment. Load skills/openrig-operating-model/SKILL.md with rig context get when deciding where durable context belongs.

Startup files vs skills (the distinction)

Per agent-startup-guide.md (product reference doc) and the team handbook:

Startup filesSkills
Rig-specific, role-specific identityReusable SOPs / methodology / knowledge
Tell agent WHO it is, WHAT it's working on, HOW this team operatesTell agent HOW to do something (transferable across rigs)
Examples: role.md, CULTURE.md, startup/context.mdExamples: openrig-user, test-driven-development, vault-user
Authored per-rigAuthored once, used everywhere

Don't put skill content in startup files. Don't put identity content in skills. The 7-layer additive startup model (agent / profile / rig / culture / pod / member / operator) handles the layering.

See also

  • mission-slice-sop — load when starting assigned mission/slice work; the light artifact and handoff procedure for SPEC.md, NOTES.md, PROGRESS.md and proof. It does not choose the SDLC for the task.
  • forming-an-openrig-mental-model skill — orientation for new agents
  • session-compaction-and-restore skill — restore-time startup ingestion
  • agent-starters skill — reusable starter manifests that compose startup context
  • openrig-operating-model skill — placement and authority of durable context
  • openrig/docs/reference/agent-startup-guide.md (product reference doc; not a skill) — the 7-layer additive startup model + delivery hints

Frequently asked questions

What does the Agent Startup And Context Ingestion AI skill do?

Use when designing or auditing how an agent becomes useful after launch — AGENTS.md overlays, role files, skills, rig specs, workflow specs, startup checklists, refocus messages, "rig context" surface. Covers the 4 failure modes that make startup context fail (old rig spec misses current operating mode; current agents never told about new guidance; startup file as dumping ground; orchestrator transmits implementation without preserving product intent).

Why use Agent Startup And Context Ingestion on TypingMind?

Because you install it once and use it with any model. Agent Startup And Context Ingestion 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 Startup And Context Ingestion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/agent-startup-and-context-ingestion. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Startup And Context Ingestion?

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 Startup And Context Ingestion?

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

Is the Agent Startup And Context Ingestion AI skill free?

Yes. It is published on GitHub by mvschwarz 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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