Chalk Labs
Need help deploying subagents?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
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/ran4om-subagents
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.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions: not checked yet.
What's inside
- Skill name
subagents- Description
- invoke this skill when the user asks you to use subagents
- License
- MIT
- Instructions
- 51 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Subagents
Each subagent is headless, has its own context window, cannot see the parent conversation, cannot ask the user, and cannot spawn subagents or workflows. Give every child a self-contained prompt with paths, constraints, and the expected report.
## Pi Harness
**Harness:** `pi`
**Prompt nicknames:** “pi”, “pi agent”, “pi subagent”
**Best default:** Use when the user does not request another harness. It inherits the parent model and thinking level when `model` or `reasoning_effort` is omitted.
Do not use models from the Anthropic provider even if one appears in the model list.
Pi can use any model shown by `pi --list-models`. Prefer `provider/model-id`; a bare model id only works when unambiguous. Common picks in this environment:
| Model | Recommended effort |
| -------------------------------- | ------------------ |
| inherited parent model (default) | inherited |
| `openai-codex/gpt-5.6-sol` | `high` |
| `openai-codex/gpt-5.6-terra` | `high` |
**Thinking budgets:** `off`, `minimal`, `low`, `medium`, `high`, `xhigh`, `max`. These map directly to pi thinking levels.
## Codex Harness
**Harness:** `codex`
**Prompt nicknames:** “codex”, “Codex CLI”, “codex agent”, “codex subagent”
**Best default:** `gpt-5.6-sol` with `high` effort for coding work. Do not use anything other than sol unless the user specifically asks for it.
| Model | Recommended effort |
| --------------- | ------------------ |
| `gpt-5.6-sol` | `high` |
| `gpt-5.6-terra` | `high` |
| `gpt-5.6-luna` | `high` |
**Thinking budgets accepted by the extension:** `off`, `minimal`, `low`, `medium`, `high`, `xhigh`, `max`. Codex maps these to the nearest effort supported by the selected model; `off`/`minimal` become `minimal`, while `max` becomes the highest extension-supported Codex effort.
Requires the Codex CLI to be installed and authenticated.
## Spawn and Manage
Call `subagent_spawn` with a complete `prompt`, short `name`, chosen `harness`, and optional `working_dir`, `model`, and `reasoning_effort`. At most four subagents run concurrently.
- `subagent_check({ id })`: peek without blocking.
- `subagent_list()`: list all runs.
- `subagent_wait({ ids })`: block only when results are required to proceed.
- `subagent_cancel({ ids })`: stop runs while preserving partial transcripts.
- `/subagents`: inspect or take over a run interactively.
Results return automatically. After spawning, continue useful parent work instead of immediately waiting.From ran4om/my-pi-setup, MIT.
More from ran4om/my-pi-setup
- 0.499score
domain-modeling
ran4om
Build and sharpen a project's domain model.
SkillBFreshness: BClaude Code +9 - 0.499score
grilling
ran4om
Grill the user relentlessly about a plan, decision, or idea.
SkillBFreshness: BClaude Code +9 - 0.488score
implement
ran4om
Implement a piece of work based on a spec or set of tickets.
SkillBFreshness: BClaude Code +9
Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Aug 4, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/ran4om/my-pi-setup
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
Same skill elsewhere
Copies of this SKILL.md published by others; search shows only one of them.
More from ran4om
- 0.499score
domain-modeling
ran4om
Build and sharpen a project's domain model.
SkillBFreshness: BClaude Code +9 - 0.499score
grilling
ran4om
Grill the user relentlessly about a plan, decision, or idea.
SkillBFreshness: BClaude Code +9 - 0.488score
implement
ran4om
Implement a piece of work based on a spec or set of tickets.
SkillBFreshness: BClaude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get subagents running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
subagents skill. Agent Mart. https://agentmart.dev/skills/ran4om-subagents (last updated 2026-10-24)