Agent skill

Executor Usage

by jeremyosih in jeremyosih/pi-executor

Load this skill before using the execute tool. An agent skill from jeremyosih/pi-executor.

MITAuto-check passedBackend & APIs

Install Executor Usage

skills CLI
$ npx skills add jeremyosih/pi-executor --skill executor-usage -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremyosih/pi-executor executor-usage --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremyosih/pi-executor.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/executor-usage .claude/skills/executor-usage && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
executor-usage
GitHub stars
104
Token cost
~1.4k tokens
SKILL.md length
553 words
Files
1
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Load this skill before using the execute tool. An agent skill from jeremyosih/pi-executor.

  • Works in 5 steps: Search for the tool. → Pick the best path from the search… → Describe the tool to get compact… → …
  • Working with third-party services
  • SKILL.md covers Source of truth, Mental model, Non-negotiable workflow and Canonical pattern, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Executor Usage is an agent skill from jeremyosih/pi-executor. Load this skill before using the execute tool. It teaches the Executor sandbox calling model: discover tools with tools.search({ query, limit }), inspect them with tools.describe.tool({ path }), inspect configured sources with tools.executor.sources.list({}), then call the real tool as tools.<namespace.<tool(args). Use when working with third-party services, SaaS APIs, MCP/OpenAPI/GraphQL tools, auth-managed actions, or remote data that should go through Executor instead of direct shell scripts or ad hoc HTTP code.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Backend & APIs, covering Shell scripting, OpenAPI specifications and GraphQL. It works with Model Context Protocol, OpenAPI and GraphQL. The repository describes itself as: Pi extension for executor. The licence is MIT.

When your agent uses it

  • Working with third-party services
  • MCP/OpenAPI/GraphQL tools
  • Auth-managed actions
  • Remote data that should go through Executor instead of direct shell scripts

Example prompts

  • “/executor-usage”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Search for the tool.
  2. Pick the best path from the search results.
  3. Describe the tool to get compact TypeScript shapes.
  4. Call the tool using the full namespace path.
  5. Normalize the result before returning it when the tool returns MCP-style content blocks.

What it can do on your machine

Read from SKILL.md and the folder at commit b744f0c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Executor Usage loads about 1.4k tokens when it runs. Until then it costs about 137 tokens; SKILL.md has 553 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~137
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremyosih/pi-executor at commit b744f0c, republished under its MIT licence (© jeremyosih). 553 words, ~1,443 tokens.

Download SKILL.mdSave it as .claude/skills/executor-usage/SKILL.md (or your agent's skills folder).
name
executor-usage
description
Load this skill before using the `execute` tool. It teaches the Executor sandbox calling model: discover tools with `tools.search({ query, limit })`, inspect them with `tools.describe.tool({ path })`, inspect configured sources with `tools.executor.sources.list({})`, then call the real tool as `tools.<namespace>.<tool>(args)`. Use when working with third-party services, SaaS APIs, MCP/OpenAPI/GraphQL tools, auth-managed actions, or remote data that should go through Executor instead of direct shell scripts or ad hoc HTTP code.
metadata.short-description
Required calling pattern for Executor `execute`

Executor Usage

Use this skill before any execute call.

This skill exists to prevent trial-and-error inside the Executor sandbox. The runtime is a lazy proxy over Executor tools, not a normal JS object, so naive probing (Object.keys, globalThis, random namespace guesses, tools()) is misleading.

Source of truth

Before calling execute, read the execute tool definition in the current prompt/context. That definition is the session-specific source of truth.

This skill summarizes the current Executor calling model exposed by the execute tool definition in your session. Avoid baking in repository-specific file paths or local implementation details.

Mental model

Inside execute:

  • Pi exposes Executor's sandbox, not arbitrary top-level JS globals.
  • The tools object is a lazy proxy.
  • You discover tools with helper functions first.
  • You then call the real tool by its full path as nested properties:
    • tools.mcp_linear_app.list_issues({...})
    • not tools.linear(...)
    • not tools()
  • Built-in helper paths are:
    • tools.search({ query, namespace?, limit? })
    • tools.describe.tool({ path })
    • tools.executor.sources.list({ query?, limit? })

Non-negotiable workflow

Follow this order inside every execute snippet unless you already know the exact tool path with high confidence.

  1. Search for the tool.
  2. Pick the best path from the search results.
  3. Describe the tool to get compact TypeScript shapes.
  4. Call the tool using the full namespace path.
  5. Normalize the result before returning it when the tool returns MCP-style content blocks.

Canonical pattern

ts
const matches = await tools.search({ query: "linear issues", limit: 5 });
const path = matches[0]?.path;
if (!path) return "No matching tools found.";

const details = await tools.describe.tool({ path });
console.log(details.inputTypeScript);
console.log(details.outputTypeScript);

const result = await tools.mcp_linear_app.list_issues({
  project: "<project-id>",
  limit: 5,
});

return result;

Result normalization pattern

Many MCP-backed tools return an MCP payload like:

ts
{
  content: [{ type: "text", text: "{...json...}" }],
  structuredContent?: {...}
}

Prefer structuredContent when present. Otherwise, parse the first text block if it looks like JSON.

ts
const unwrap = (value: any) => {
  if (value?.structuredContent) return value.structuredContent;

  const text = value?.content?.find?.((item: any) => item?.type === "text")?.text;
  if (typeof text !== "string") return value;

  try {
    return JSON.parse(text);
  } catch {
    return value;
  }
};

Use it like:

ts
const raw = await tools.mcp_linear_app.list_projects({ query: "gitinspect", limit: 10 });
return unwrap(raw);

Hard rules

Required
  • Always start with tools.search({ ... }) for unknown integrations.
  • Always pass an object to helper tools.
  • Always use the full namespace prefix when invoking the real tool.
  • Use tools.describe.tool({ path }) before invoking unfamiliar tools.
  • Use tools.executor.sources.list({}) when you need source inventory or namespace confirmation.
  • Let execute handle inline elicitation in Pi UI sessions.
Show full SKILL.md (250 more words)Show less
Forbidden
  • Do not call tools().
  • Do not probe with Object.keys(tools) or rely on globalThis for discovery.
  • Do not guess namespaces from property names like tools.linear or tools.mcp.
  • Do not pass string args to tools.search; pass an object.
  • Do not use includeSchemas with tools.describe.tool(); that parameter is no longer accepted.
  • Do not assume tool results are already plain JSON objects.
  • Do not use fetch when Executor already has the integration you need.

Good search patterns

Use short intent phrases with key nouns:

  • linear issues
  • github pull requests
  • calendar event
  • slack channel messages

When you know the namespace, narrow it:

ts
await tools.search({ namespace: "mcp_linear_app", query: "issues", limit: 10 });

Decision rule

Use Executor when the task is mostly outside the repo:

  • SaaS APIs
  • remote systems
  • configured MCP / OpenAPI / GraphQL integrations
  • auth- or approval-managed actions

Use Pi's native file tools when the task is mostly inside the repo:

  • reading files
  • editing code
  • refactors
  • local tests and builds

Interaction rule

In Pi UI sessions, let execute handle Executor interaction inline. Do not call resume unless execute explicitly cannot finish inline and gives you an execution ID to resume.

Quick recovery checklist

If an execute snippet behaves strangely, check these first:

  1. Did you forget to load this skill before using execute?
  2. Did you call tools.search({ ... }) instead of guessing?
  3. Did you pass an object to helper tools?
  4. Did you use tools.describe.tool({ path }) before calling an unfamiliar tool?
  5. Did you invoke the real tool with its full namespace path?
  6. Did you unwrap structuredContent / JSON text if the result looked nested?

© jeremyosih, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/executor-usage of jeremyosih/pi-executor.

Open the folder on GitHubat commit b744f0c

Compare with similar skills

Executor Usage next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Executor Usage compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Executor Usage this skilljeremyosih/pi-executor104—~1.4kAutomated safety check: PassMIT
SpikardGoldziher/spikard123—~799Automated safety check: PassMIT
Uxcholon-run/uxc115—~1.8kAutomated safety check: PassMIT
API DesignerJeffallan/claude-skills12k2 repos~2kAutomated safety check: PassMIT
AurlShawnPana/aurl167—~536Automated safety check: PassMIT
Distilled SDKalchemy-run/distilled431—~6kAutomated safety check: PassApache-2.0

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Categories

Questions about Executor Usage

What does Executor Usage do?

Load this skill before using the execute tool. An agent skill from jeremyosih/pi-executor. Executor Usage is an agent skill from jeremyosih/pi-executor. Load this skill before using the execute tool.

When should I use Executor Usage?

Executor Usage fits situations like: working with third-party services; MCP/OpenAPI/GraphQL tools; auth-managed actions; remote data that should go through Executor instead of direct shell scripts.

How do I install Executor Usage in Claude Code?

Run `npx skills add jeremyosih/pi-executor --skill executor-usage -a claude-code`. Or copy the skill folder (skills/executor-usage in jeremyosih/pi-executor) into .claude/skills/executor-usage in your project. Claude Code loads it when a task matches its description.

How do I install Executor Usage in Codex?

Run `npx skills add jeremyosih/pi-executor --skill executor-usage -a codex`. Or copy the skill folder (skills/executor-usage in jeremyosih/pi-executor) into .agents/skills/executor-usage in your project. Codex loads it when a task matches its description.

Can I use Executor Usage in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremyosih/pi-executor --skill executor-usage -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/executor-usage, .gemini/skills/executor-usage, .github/skills/executor-usage and .opencode/skills/executor-usage in your project.

What does Executor Usage need to run?

SKILL.md names no scripts, command-line tools or credentials: Executor Usage is instructions for the agent only.

Does Executor Usage access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Executor Usage safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Executor Usage use?

Executor Usage is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Executor Usage use?

About 1.4k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Executor Usage?

Skills that share tags, products or a category with Executor Usage: Spikard (Goldziher/spikard, 123 stars), Uxc (holon-run/uxc, 115 stars), API Designer (Jeffallan/claude-skills, 12k stars) and Aurl (ShawnPana/aurl, 167 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Executor Usage?

jeremyosih (a GitHub user) maintains it in jeremyosih/pi-executor, which has 104 GitHub stars. The repository was last updated on June 21, 2026.

Source: jeremyosih/pi-executor on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.