Intent-classified router, the front door to OrchestKit and the DEFAULT entry point for any goal-shaped request.

MITAuto-check passedWriting & Content

Install Auto

skills CLI
$ npx skills add yonatangross/orchestkit --skill auto -a claude-code

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

GitHub CLI
$ gh skill install yonatangross/orchestkit auto --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/yonatangross/orchestkit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src/skills/auto .claude/skills/auto && 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
auto
GitHub stars
289
Token cost
~3.6k tokens
SKILL.md length
1,816 words
Files
4 (incl. references)
Skills in repo
108
Repo updated
First seen
Licence
MIT

At a glance

Intent-classified router, the front door to OrchestKit and the DEFAULT entry point for any goal-shaped request.

  • Works in 3 steps: Classify → Confirm (low ceremony) → Hand off
  • Any goal description
  • SKILL.md covers When to use, Intent categories → OrchestKit…, Model weight (orthogonal… and The flow, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Auto is an agent skill from yonatangross/orchestkit. Intent-classified router, the front door to OrchestKit and the DEFAULT entry point for any goal-shaped request. Classifies a plain-English goal and routes it to the right specialist skill. Routing is never overhead, so use it even when the target skill seems obvious; skip only when already executing inside another skill (no recursion). Triggers on: auto, do this, figure out, just make, I want, help me, fix, build, improve, any goal description.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/routing-rules.md`, `routing-benchmark.json` and `test-cases.json`). Compatibility notes: Claude Code 2.1.277+.

It sits in Writing & Content, covering Plain language and style rules. The repository describes itself as: The Complete AI Development Toolkit for Claude Code. 106 skills, 36 agents, 171 hooks. Install ork for stable (v9.x), or ork-alpha for the v10 line, which ships daily. The licence is MIT.

When your agent uses it

  • Any goal description
  • Tasks that involve Plain language and style rules

Example prompts

  • “/auto”

Requirements

  • Compatibility (from SKILL.md): Claude Code 2.1.277+.
  • Pre-approved tools (allowed-tools): AskUserQuestion, Read, Grep, Glob, Skill, Agent

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Classify
  2. Confirm (low ceremony)
  3. Hand off

What it can do on your machine

Read from SKILL.md and the folder at commit 0ef71d2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • AskUserQuestion
    • Read
    • Grep
    • Glob
    • Skill
    • Agent

    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 bash).

    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.

  • Compatibility

    Claude Code 2.1.277+.

    From compatibility in the SKILL.md frontmatter.

Context cost

Auto loads about 3.6k tokens when it runs, and up to ~6.7k if it reads all its reference files. Until then it costs about 113 tokens; SKILL.md has 1,816 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~113
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.7k

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 yonatangross/orchestkit at commit 0ef71d2, republished under its MIT licence (© yonatangross). 1,816 words, ~3,635 tokens.

Download SKILL.mdSave it as .claude/skills/auto/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
auto
description
Intent-classified router, the front door to OrchestKit and the DEFAULT entry point for any goal-shaped request. Classifies a plain-English goal and routes it to the right specialist skill. Routing is never overhead, so use it even when the target skill seems obvious; skip only when already executing inside another skill (no recursion). Triggers on: auto, do this, figure out, just make, I want, help me, fix, build, improve, any goal description.
allowed-tools
AskUserQuestion, Read, Grep, Glob, Skill, Agent
compatibility
Claude Code 2.1.277+.
license
MIT
argument-hint
[plain-english goal]
context
inherit
user-invocable
true
model
sonnet
metadata.category
workflow-automation
metadata.version
1.0.0
metadata.author
OrchestKit
metadata.complexity
medium
metadata.tags
router, intent, orchestration, discovery, meta, front-door

auto — Intent Router

The front door to OrchestKit. You describe a goal in plain English; the router classifies it and hands off to the right specialist. One entry point, many execution paths.

Why this exists: OrchestKit has 110 skills, but usage telemetry shows users fire only the handful they can name by memory (10 distinct skills across thousands of sessions). The dominant cause of "dead" skills is no front door — not low quality. This router turns "you must know the exact /ork:<name>" into "describe what you want."

Core principle: routing is a deterministic workflow, not an autonomous agent (Anthropic, Building Effective Agents). Classify → confirm → hand off. The router never does the work itself — it picks who does.

When to use

By default, for any goal-shaped request. An unambiguous goal is a 1-step route: auto classifies, confirms in one line, and hands off — no extra hops, so there is no "too obvious for auto".

Use auto for…Skip only when…
Any goal description ("fix X", "get Y to Z")Already executing inside another skill (no recursion)
The right skill isn't obviousChaining a known multi-skill workflow you're mid-way through
You think you know the skill — auto confirms & short-circuits

Design note (2026-07-12): this table previously said "Go direct when you already know the skill / the request maps unambiguously to one". That inverted instruction made the front door structurally unreachable — a competent model always believes it knows the target, so the router recorded near-zero invocations across thousands of sessions (the exact dead-skill problem the "Why this exists" note above describes). Routers must be framed as the default path, not an escape hatch for confusion.

Intent categories → OrchestKit skill

intentsignal wordsroutes to
fixfix, debug, broken, failing, error, crash, regressionfix-issue
diagnosewhy, why isn't, why does, why can't, investigatefix-issue (investigation-first)
optimizefaster, reduce, latency, bundle, minimize, below N msa /goal optimization loop (see Gaps)
covercoverage, untested, get to N%cover --target N
e2ee2e, in the browser, browser test, playwrightexpect (run on the diff); cover when no e2e tests exist yet
designdesign, architect, how should we, explore, ideabrainstorm
buildbuild, implement, create, add feature, from ticketimplement
reviewreview, PR, MR, pull request, #Nreview-pr
verifyverify, check, make sure, passes, greenverify
improve-skillimprove the skill, optimize the prompt, SKILL.mdthe holdout-promotion gate (see Gaps)
(fallback)no confident categoryclarify with ONE question

Full per-category parameter extraction + edge cases: references/routing-rules.md.

Model weight (orthogonal second dimension)

Intent picks who does the work. Weight picks how expensive that worker should be. A route is {intent} @ {weight}. Weight never changes the intent and never replaces it. The taxonomy above and the 7 disambiguation rules are untouched by it.

Tiers are the ones already declared in src/agents/*.md frontmatter (haiku 3 · sonnet 23 · opus 6 · inherit 4). No parallel taxonomy.

weighttierthe task is…
Lighthaikumechanical or IO-bound, single file, deterministic output, trivially revertible
Standardsonnetthe default: bounded judgment, known pattern
Heavyopusadversarial, security, safety, architecture, cross-cutting, or ambiguous

Resolution is asymmetric: ANY heavy signal ⇒ Heavy; Light requires ALL light signals; everything else is Standard. Under-powering a security review yields a confident wrong answer nobody catches; over-powering a rename only wastes money.

Weight is per leg, not per route. A PR review can be a Heavy security leg plus a Light lint leg. Full signal table, per-intent defaults, and the honest limits of this lever: references/routing-rules.md.

This is the selector, not the cap. src/hooks/src/pretool/task/team-size-gate.ts is an ex-post, per-session counter keyed on ORK_TEAM_OPUS_MAX (default 8). Its default posture is advisory (outputWarning); with ORK_TEAM_SIZE_HARD=1 it escalates to outputDeny and refuses the spawn outright. Either way it reads the model read-only: it can refuse a premium spawn, but it cannot choose a cheaper one for you. Routing is what chooses. The two compose, cap as backstop and routing as selector; never duplicate the cap's counting here.

The flow

  CLASSIFY  ->  CONFIRM  ->  HAND OFF
     |            |             |
  intent       show the     invoke the
  + weight     route        target skill;
  one line     + nod        follow ITS phases
1. Classify

Name the route and the one signal that decided it in a single short line. Example: "'get latency under 200ms' names a metric + a direction, so optimize, not fix."

Apply the disambiguation rules (most specific wins; explicit verb beats inferred intent). The load-bearing one: explicit verb wins — "Fix the slow query" → fix, not optimize. For the full ordered ruleset (all 7, including the truly-ambiguous fallback), references/routing-rules.md is canonical.

Then classify weight in the same pass, naming the signal that decided it: "touches auth and models an attacker → Heavy." Intent first, weight second; a weight call never rewrites the intent you just committed to.

A route: line is a prior, not a verdict. When the prompt context carries one line of the shape route: <class> -> /ork:<skill> (conf 0.xx) or route: <class> -> Agent(<name>) (conf 0.xx), it came from the Jev routing seam (ORK_ROUTE_JEV=steer, off by default, #4233): one typed judgment of the capability class, taken before you read this table. Treat it as the prior for this step, state in one sentence whether you agree and why, and then classify as above. Your reasoning still decides; a confident prior you disagree with is worth a sentence, never a silent override in either direction. The confirm step is unchanged; the hand-off tool follows the target: a /ork: skill goes through the Skill tool, an Agent(...) target through the Agent tool.

2. Confirm (low ceremony)

Show the chosen route in one line and get a nod before handing off:

Goal:   "{original goal}"
Intent: {category}
Weight: {Light|Standard|Heavy} ({tier}), decided by: {signal}
Route:  {skill or loop} {extracted args}
        [run] · [adjust] · [cancel]

For low-risk single-pass routes (verify, review), an inline "routing you to verify — ok?" is enough. Never hand off without a nod.

Premium spend is never silent. Routing down (Light/Standard) needs no approval, because spending less is not a decision the user has to make. Routing up to Heavy is premium spend and gets its own line the user must accept:

⚠️  Heavy route: {N} opus-tier leg(s), triggered by: {heavy signal}
    [approve premium] · [run Standard instead] · [cancel]

If they decline, run Standard and say plainly which check is weakened. Never upgrade mid-handoff or inside a spawned agent the user did not see.

3. Hand off

Invoke the target skill with the extracted parameters and follow that skill's own phases and guardrails — do not override them. The router's job ends at the handoff; the specialist owns execution and its own report.

A hand-off is a Skill-tool invocation, not a recommendation. The failure mode that motivated M170 (#3127): telemetry traced 43 router hand-offs and found ZERO reached an executor skill (implement, cover, fix-issue, review-pr) — the route was named in chat, then the work happened inline in the main loop, so the executors' specialist wiring (implement → backend-system-architect, cover → test-generator) never activated. Therefore:

  • Once the user nods, the SAME turn must contain the Skill-tool call for the routed skill. Never end the routing turn with only a description of what will run.
  • Doing the routed work inline "because it's faster" is a routing failure, not a shortcut — the executor's parallel specialists and guardrails are the point of routing.
  • If the routed skill genuinely cannot run (missing prerequisite, wrong repo state), say exactly that and stop; do not silently absorb the work into the main loop.
Show full SKILL.md (655 more words)Show less

Fallback + honest gaps

  • Fallback category. If no category clears a confident threshold, ask exactly ONE clarifying question rather than guessing. A rising fallback rate is the leading indicator that the taxonomy needs work — surface it, don't bury it.
  • optimize has no dedicated skill (yet). OrchestKit's metric-driven optimization runs as a /goal loop using the loop recipe library (prd-to-goal → references/recipe-library.md). Route optimize there and say so plainly — don't pretend a experiment skill exists.
  • improve-skill routes to the evolution gate. Self-optimizing a SKILL.md goes through the champion/challenger holdout-promotion gate (assess evals + evolution-engine), not a one-shot edit. It requires a benchmark + holdout set first.

Stacked invocation (CC 2.1.199+)

/skill-a /skill-b <goal> loads all leading skills (up to 5) into context at once; the trailing args belong to the whole stack. So auto brainstorm <goal> pre-loads the specialist alongside the router — useful when the user already knows part of the route. The router still owns classification and handoff; a pre-loaded specialist does not bypass the confirm step.

Guardrails

  • No recursion. auto must not route to itself, directly or via a spawned agent.
  • No bypass. Routing does not skip the target skill's guardrails, readonly enforcement, or confirmation steps.
  • Classification quality is the whole job. A misroute that fails silently is worse than a fallback question. When two categories are equally plausible, ask — don't gamble.
  • No silent upgrade. A Heavy (opus/fable) leg is premium spend and requires an explicit nod on its own line. Downgrades stay silent.
  • The cap is not the router's to move. Never read, set, or suggest raising ORK_TEAM_OPUS_MAX. That is the user's budget.
  • A gate denial is not a re-route trigger. If team-size-gate denies a spawn, do NOT relabel a Heavy leg as Standard to slip under the cap. Report the denial and let the user decide.
  • Weight is a hint, not an override. The router cannot change an agent's declared model:. It selects skills and agents, and sets the caller's model that the 4 inherit agents adopt. It never overrides a target skill's own agent selection.

Validation

Routing accuracy is gateable, not vibes. routing-benchmark.json holds 50 labeled goal → category pairs (easy + genuinely ambiguous). Validate after any change to the category table or disambiguation rules:

bash
# isolated classification check via the bare-eval harness
bare-eval   # grade router output against routing-benchmark.json

Target ≥95% category accuracy; track the fallback rate as a degradation alarm as the skill library grows.

References

  • references/routing-rules.md — per-category parameter extraction, edge cases, disambiguation
  • routing-benchmark.json — 50 labeled goal→category pairs for accuracy validation

Quality Bar

Done means all of these hold:

  • Classification reasoning is stated out loud BEFORE a route is committed, naming the chosen intent category and the signal words that triggered it.
  • Weight is classified in the same pass as intent, naming the signal that decided it, and resolves asymmetrically (ANY heavy signal ⇒ Heavy; Light needs ALL light signals; else Standard).
  • The confirm block names one of the taxonomy's intent categories, its target skill or /goal loop, and the extracted args — on one line.
  • Every Heavy (opus/fable) leg is surfaced for explicit approval before handoff; no premium spend is silent, and declining it runs Standard with the weakened check named.
  • Adversarial, security, safety, architecture, and cross-cutting work is never routed below opus tier; mechanical single-file IO-bound work is not routed above haiku tier.
  • The router neither counts spawns nor touches ORK_TEAM_OPUS_MAX, and never downgrades a Heavy leg to evade a team-size-gate denial.
  • No target skill is invoked without an explicit nod (or -y); handoff never precedes confirmation.
  • When two categories are equally plausible, exactly ONE clarifying question is asked — the fallback is never silently guessed.
  • optimize routes to a /goal loop and improve-skill to the evolution gate; neither claims a dedicated skill that does not exist.
  • The router does none of the target work itself and never routes to auto (no recursion).
  • help — static categorized directory (browse, don't route)
  • prd-to-goal — decompose a spec into a /goal line (the optimize route's engine)
  • fix-issue · cover · expect · brainstorm · implement · review-pr · verify: the route targets
  • assess — champion/challenger holdout gate (the improve-skill route)

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

Files

SKILL.md and 3 other files (references) in src/skills/auto of yonatangross/orchestkit.

  • SKILL.md
  • references/routing-rules.md
  • routing-benchmark.json
  • test-cases.json

Open the folder on GitHubat commit 0ef71d2

Compare with similar skills

Auto 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.

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Simple Issue Descriptionevery-app/open-seo23k1 repos~1.2kAutomated safety check: PassMIT
Ponytail AuditDietrichGebert/ponytail158k—~1.3kAutomated safety check: PassMIT
Technical Writing Standardcursor/plugins10k10 repos~2.4kAutomated safety check: PassNone
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Questions about Auto

What does Auto do?

Intent-classified router, the front door to OrchestKit and the DEFAULT entry point for any goal-shaped request. Auto is an agent skill from yonatangross/orchestkit. Intent-classified router, the front door to OrchestKit and the DEFAULT entry point for any goal-shaped request.

When should I use Auto?

Auto fits situations like: any goal description; tasks that involve Plain language and style rules.

How do I install Auto in Claude Code?

Run `npx skills add yonatangross/orchestkit --skill auto -a claude-code`. Or copy the skill folder (src/skills/auto in yonatangross/orchestkit) into .claude/skills/auto in your project. Claude Code loads it when a task matches its description.

How do I install Auto in Codex?

Run `npx skills add yonatangross/orchestkit --skill auto -a codex`. Or copy the skill folder (src/skills/auto in yonatangross/orchestkit) into .agents/skills/auto in your project. Codex loads it when a task matches its description.

Can I use Auto 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 yonatangross/orchestkit --skill auto -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/auto, .gemini/skills/auto, .github/skills/auto and .opencode/skills/auto in your project.

What does Auto need to run?

SKILL.md names no scripts, command-line tools or credentials: Auto is instructions for the agent only. Its frontmatter pre-approves these tools: AskUserQuestion, Read, Grep, Glob, Skill, Agent. Compatibility (from SKILL.md): Claude Code 2.1.277+..

Does Auto 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 Auto 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 Auto use?

Auto is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Auto use?

About 3.6k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.1k tokens, read only when the agent opens those files.

What are the alternatives to Auto?

Skills that share tags, products or a category with Auto: Asd Ste100 (danyuchn/asd-ste100-skill, 4k stars), Simple Issue Description (every-app/open-seo, 23k stars), Ponytail Audit (DietrichGebert/ponytail, 158k stars) and Technical Writing Standard (cursor/plugins, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Auto?

yonatangross (a GitHub user) maintains it in yonatangross/orchestkit, which has 289 GitHub stars. The repository holds 108 skills in this directory. The repository was last updated on October 7, 2026.

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