agentleFS
gigio1023gigio1023/gigio-skills/skills/additional/zcode-cli/SKILL.md

zcode-cli

Skill2 starsChanged 8 days agoMITView on GitHub

Configure API-key access and run authorized tasks through Zcode's official CLI with an available model, supported reasoning options, and resumable sessions; use for "use Zcode CLI", "set up GLM with an API key in Zcode", "delegate this to Zcode", "Zcode GLM API key 설정", or "Zcode로 위임해줘". NOT for automatic model calls from a Zcode mention, reading stored conversations from another agent (read-agent-sessions), or delegation through Codex or Cursor.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Check the result against the task: inspect cited sources for research, the actual diff and relevant checks for code, and the exact target …

    line 19
  2. Distinctive:

    When maintaining this package, validate metadata, resource links, source-backed commands, and discovery.

    line 41

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Zcode CLI

Set up the requested Zcode connection or run a task with an explicit model selection and permissions that match the request. Return the working connection, answer, or changed artifact with the observed session settings and evidence needed to check it.

Working path

  1. Establish the task, workspace, permitted effects, requested model and reasoning level, and expected result from the user's request. Inspection or discussion of this skill does not authorize a model call. Reuse existing authority for the requested work.
  2. Locate the official CLI and inspect its version, --help, and doctor --json. Read the CLI contract for the verified commands, macOS bundled-runtime fallback, and version-specific limitations. Do not replace an installed official runtime with a community CLI merely because zcode is absent from PATH.
  3. Inspect the runtime's available model catalog and authentication status through its supported interface. For API-key setup, read the supplied-key recipe, establish whether the key belongs to Coding Plan or the standard API, and use the matching template and access type. Ask for the intended service if the request does not establish it; the key's shape does not decide it. Preserve existing accounts and provider configuration, using a separate private profile when requested. A Desktop login, a stored default, and a successful doctor check do not establish usable model access.
  4. Select the requested provider, model, and supported reasoning option for the intended session. Read back an existing session's selection before running the task. For a new headless session seeded from a private profile, follow first-call verification and read back the returned session afterward. For another model, inspect its supported options instead of reusing GLM settings. If the user omits a choice, honor an established preference or reuse a valid existing selection; if neither exists, use the default below and ask for the model only when the catalog does not offer it. Do not silently substitute an unavailable requested model or reasoning level.
  5. Choose the permission mode explicitly and apply supported tool exclusions where needed. Read-only work uses plan mode with the restrictions described in the reference. Editing uses the mode and approval interaction permitted by the task. Headless prompts default to yolo in the inspected version, so never omit --mode or broaden permissions to make an unattended run proceed.
  6. Give Zcode a self-contained task with the relevant sources, allowed actions, preservation requirements, and expected answer or artifact. Run in the intended workspace and keep the exact session ID, process outcome, and relevant events. Use the host's process tracking for long work; do not confuse a running process with a completed task.
  7. Check the result against the task: inspect cited sources for research, the actual diff and relevant checks for code, and the exact target after an authorized external write. A selected model, a success event, and a correct result are separate verification steps.

Model and session choices

The scope is any model available through the user's Zcode runtime. The default is GLM-5.3 at max, the level recommended for most tasks; use high when the user asks for it or the task is short and mechanical, and low for connection checks and smoke tests or on request. These defaults hold only while the active catalog offers them, and another model uses its own catalog options. Use the catalog's exact provider and model IDs, and the session's native reasoning field. Do not invent --model or --reasoning-effort flags from another CLI. Usage is metered in credits, each call carries a large fixed prompt, and bursts of requests hit a rate limit; read usage limits before planning many calls, and keep one Zcode run per account at a time.

Prefer the TUI for interactive selection and normal approvals. Use app-server when automation needs structured model discovery, session selection, and approval responses. A plain headless prompt can continue an already configured session, but its prompt text is not the TUI command dispatcher: passing '/model' as a prompt is not model selection.

Resume related work by its exact returned session ID after checking the workspace and selection again. Start a new session for independent work. Avoid --continue when concurrent work could make the latest session ambiguous, and never run two continuations of one session at once.

Authority and records

Keep concurrent writers in separate authorized workspaces or assign disjoint files. A worktree isolates Git changes; it does not contain shell commands, MCP tools, network access, or shared Zcode state. Preserve the host's sandbox and approvals. A tool exclusion removes a whole named tool in this CLI; it is not a command-pattern allowlist.

Use the normal masked login flow or a supplied key in an authorized private provider profile. Do not extract Desktop credentials, copy private authentication headers, change entitlement flags, log secrets, or replace a provider to get past a failure. If authentication, model access, or a required permission cannot be established, stop the affected launch and report the observed error without guessing its cause. Retry only after an identified recoverable condition changes.

Prompts, attachments, and tool results may be sent to the selected provider and retained in Zcode's session storage. Keep credential values out of arguments and prompts. Keep task prompts and diagnostic logs outside this replaceable skill package, restrict access, and inspect them before sharing. Configuration changes, installs, login/logout, publication, and destructive actions need the corresponding task authority; a request to inspect models does not grant them.

Completion

Return the requested answer or artifact first. Include the CLI version, requested and observed model selection, reasoning level when reported, permission mode, session ID, process or turn outcome, relevant validation, and any remaining blocker. Label a local selection readback as configuration evidence; claim a successful model call only after receiving its actual response. Do not infer subscription billing or entitlement from a model label or successful configuration write.

When maintaining this package, validate metadata, resource links, source-backed commands, and discovery. Documentation changes do not require paid model trials. Report package checks, exercised CLI operations, and live model execution separately.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.