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shrimpy-agents

Create, inspect, configure, rename, remove, or debug Shrimpy agents.

Skill by zachmeador

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/zachmeador-shrimpy-agents

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Create, inspect, configure, rename, remove, or debug Shrimpy agents.

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/shrimpy-agents

What's inside

Skill name
shrimpy-agents
Description
Create, inspect, configure, rename, remove, or debug Shrimpy agents.
License
MIT
Instructions
162 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Shrimpy Agents

Agents are persistent workspace actors with identity, memory, skills, watches, sessions, vault, and projects. Use this skill when creating or managing agents. Use the CLI first; do not invent a parallel registry.

## Inspect First

Use the workspace as source of truth before changing anything:

```bash
shrimpy agent list
shrimpy agent show <id>
shrimpy agent inspect <id> --json
shrimpy models resolve --agent <id> --session local/main --json
shrimpy skills list --agent <id>
shrimpy context --agent <id> --sections
```

If creating a new agent, ask for one stable id and one sentence of purpose when missing. Agent ids should be short, lowercase, and durable.

## Choose The Smallest Actor

Do not create a persistent agent merely because a task can be named as a role. Prefer the smallest primitive that preserves the behavior the user actually needs:

- skill: reusable instructions or a capability the current agent can perform;
- tool, script, or context producer: deterministic action or bounded live information;
- worker session: delegated work with a concrete goal and completion point;
- persistent agent: a durable actor the user recognizes or that collaborates across the household.

A proposed persistent agent should satisfy all three conditions:

- It has an enduring identity or viewpoint.
- It needs genuinely separate memory.
- It acts independently over time.

If any condition is missing, start with a skill, tool, context producer, or worker session. Researching, editing, planning, reviewing, and similar job-shaped capabilities are not separate agents by default.

## Manage Agents

Create the agent through the CLI, then edit its scaffolded files:

```bash
shrimpy agent add <id> --channel-policy addressed --json
shrimpy agent add <id> \
  --model-policy <policy> \
  --tools reply,ask,notify,report,send_message,read_channel \
  --thinking medium \
  --channel-policy addressed \
  --json
```

Use `shrimpy agent set <id>` for model policy, tools, disabled tools, thinking, knowledge scope, root, or base channel policy changes. Ordinary agents default to `--knowledge-scope agent`, which searches shared workspace knowledge plus their own context, vault, and skills. Grant `--knowledge-scope global` only when the agent should search every agent corpus. Mechanic always resolves to global knowledge scope. Use `shrimpy agent rename <old> <new>` for durable id changes. Use `shrimpy agent remove <id>` only when the user asked to remove an agent; add `--delete-files` only when they explicitly want files deleted.

## Model Policy Changes

When the user asks an agent to use a specific model going forward, treat it as a persistent model-policy change, not just a current-session model switch.

Inspect first:

```bash
shrimpy models --json
shrimpy models policies --json
shrimpy models resolve --agent <id> --session local/main --json
```

Find the exact Pi-visible `<provider>/<model>` id. If the user's model name is ambiguous or missing from `shrimpy models`, ask before changing anything.

Prefer a named policy for durable defaults:

```bash
shrimpy models policies set <policy> --candidate <provider>/<model> --json
shrimpy agent set <id> --model-policy <policy> --json
shrimpy models resolve --agent <id> --session local/main --json
```

Use an existing policy only when changing every agent that uses it is intended. Otherwise create or update an agent-specific policy name such as `<agent-id>-default`.

Active sessions restore the model saved in their transcript. Changing an agent's model policy or restarting the gateway does not replace that saved selection.

When the user explicitly wants to change a current gateway-owned session, inspect its canonical id and set the session itself:

```bash
shrimpy sessions list --agent <id> --json
shrimpy sessions set <session-id> --agent <id> --model <provider>/<model>
shrimpy sessions set <session-id> --agent <id> --model-policy <policy>
shrimpy sessions set <session-id> --agent <id> --thinking <level>
```

Use the foreground host's model or thinking controls for a TUI-owned session. Do not reset a session merely to apply a default unless the user asked to archive its current transcript. See `reference/sessions.md` for session ownership and model persistence.

## Shape Choices

- `SOUL.md`: concise role, responsibilities, boundaries, and voice.
- `context/**/*.md`: durable memory and active references that should load into every normal prompt for this agent. Treat this as scarce prompt budget; keep files tiny, stable, and character-count efficient.
- `vault/`: saved files and reports.
- `projects/`: code, apps, experiments, and focused work folders.
- `skills/`: agent-specific skills that override workspace skills.
- `watches.json`: recurring attention owned by this agent.
- `channelPolicy`: prefer `addressed` or `mentions` on shared human channels; use `all` only for private channels, maintenance channels, or deliberate always-on listeners.

Create context files only when there is something real to preserve and always-loading it is worth the token cost. Keep bulky evidence, journals, reports, saved files, and working material in `vault/` or `projects/`, with only a compact pointer in `context/` when needed.

## App-Agent Context

For app-like agents, consider whether live app state should arrive through turn context instead of memory or a watch. Automatic producers under `context.turn.producers` in `config/shrimpy.json` can run a small workspace command on relevant turns and emit compact summaries with optional inspect commands. Reserve them for bounded facts the model must receive before it can decide what to inspect.

Inspect before changing context wiring:

```bash
shrimpy context sources list --agent <id> --channel <channel> --json
shrimpy context producers list --agent <id> --channel <channel> --json
shrimpy context producers run <producer-id> --agent <id> --channel <channel> --json
shrimpy context turn --agent <id> --channel <channel>
```

Prefer existing CLI/config helpers. If an automatic producer needs hand-edited config, read `docs/reference/configuration.md` and `docs/reference/context-assembly.md` first, keep the command output bounded, and test with `shrimpy context producers run` before declaring the app-agent wired.

## Wire Channels

Channel membership gives visibility; channel policy decides whether a visible message becomes a turn. Join only the channels the agent should see:

From zachmeador/shrimpy, MIT.

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Last commit (this folder)
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From GitHub (fetched Oct 24, 2026).

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License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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shrimpy-agents skill. Agent Mart. https://agentmart.dev/skills/zachmeador-shrimpy-agents (last updated 2026-10-24)