qstack-slalom

qstack-slalom is a skill for Claude Code, Codex from hani-q/qstack. It costs 118 tokens per session (2,673 once invoked), scanned A, original, MIT.

A task-running skill that splits one discussed coding job into independent parts and sends them to several lower-cost AI workers, while a chosen model coordinates and checks the result.

In plain words
What is it for?
Use it for independent coding changes such as renaming a configuration loader and its callers, or adding a helper in separate areas of a codebase. You provide the coordinator model, worker model or models, and optionally the task.
Why use it?
It avoids making one expensive AI model do every small piece of a task. It also handles work that is ready to run without requiring a written plan or task board.

Skill for Claude CodeCodex

Written for Claude Code and Codex: disable-model-invocation in frontmatter, but also agents/openai.yaml present. Also seen: mentions subagents; mentions Claude Code; mentions Codex.

Good fit Use it for independent coding changes such as renaming a configuration loader and its callers, or adding a helper in separate areas of a codebase. You provide the coordinator model, worker model or models, and optionally the task.

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Install with agentmods
npx agentmods add skills/hani-q/qstack/qstack-slalom
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add hani-q/qstack --skill qstack-slalom
Clone the repo
git clone --depth 1 https://github.com/hani-q/qstack

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for qstack-slalom

README.md
[![agentmods](https://agentmods.dev/badge/skills/hani-q/qstack/qstack-slalom/github.svg)](https://agentmods.dev/skills/hani-q/qstack/qstack-slalom)
Your own site
<a href="https://agentmods.dev/skills/hani-q/qstack/qstack-slalom"><img src="https://agentmods.dev/badge/skills/hani-q/qstack/qstack-slalom/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for qstack-slalom

Your own site · 80×15
<a href="https://agentmods.dev/skills/hani-q/qstack/qstack-slalom"><img src="https://agentmods.dev/badge/skills/hani-q/qstack/qstack-slalom.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,673 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00118 $0.02673
Opus 5.5 $0.00047 $0.01069
Sonnet 5.5 $0.00024 $0.00535
Haiku 4.5 $0.00012 $0.00267

Measured 1mo ago against content hash d11a74ec28c2, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

qstack-slalom scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/qstack-slalom/SKILL.md · 228 lines

How it starts

The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/qstack-slalom

One player carries the ball through the whole defence while the rest of the team stays behind the line. Here the expensive model carries the task: it reads, splits, dispatches, integrates, and verifies. Cheaper models do the running.

Use this for a chunk of work you talked through and do not want to plan. It writes no plan, no board, and no execution record. For work that needs those, use /qstack-plan-to-html and a loop.

Invocation

/qstack-slalom <orchestrator> <workers> [task]
  • <orchestrator> is the model this session must be running on, such as fable-5.1 or astra-6.
  • <workers> is one model or a comma-separated list, such as opus-5 or gpt-5.6-sol,opus-5. Every subagent runs on one of these.
  • [task] is free text. When absent, the task is the one settled in the conversation so far.

Examples:

/qstack-slalom fable-5.1 opus-5
/qstack-slalom astra-6 gpt-5.6-sol rename the config loader and update its callers
/qstack-slalom fable-5.1 opus-5,sonnet-5 add the retry helper we discussed

Both model arguments are required. With fewer than two, stop and show the usage line.

Two rules that never bend

  1. No subagent runs on the orchestrator model. Pin every launch to a worker model explicitly. Never rely on inheritance, because an inherited model is the session model, which is the orchestrator.
  2. The orchestrator does not implement. It may read any file and run any check. It edits only to integrate results, and only when a conflict or a one-line fix at the seam is cheaper than another dispatch. It never takes a unit for itself because the unit looked small.

Preflight

Do all of this before launching anything.

  1. Confirm the session model is the orchestrator. Read the model this session reports it is running as. The two spellings differ: a user types fable-5.1, a Claude Code session reports claude-fable-5-1[1m]. Lower both, strip spaces and punctuation, and treat them as the same model when one contains the other. Anything else is a mismatch: stop and say which model the session is on and how to switch, such as /model in Claude Code. Do not run the task on the wrong orchestrator, and do not swap the roles around to make the arguments fit.
  2. Resolve each worker model to an identifier the harness accepts, using /qstack-choose-model's resolve against the discovered catalogue and its probe-brief to prove the launch before any unit is dispatched. Its "Launching a tier in each harness" says what that identifier is: a custom agent name in Codex, an alias whose ANTHROPIC_DEFAULT_* mapping is confirmed in Claude Code. In Claude Code the Agent tool takes model and the Workflow script's agent() takes opts.model; in Codex the subagent launcher takes a model in its own form. Map the user's spelling to that form with the same normalized containment match as step 1. A name that matches nothing, such as opsu-5, is not guessed at: ask, through the host's structured question tool when it has one and in plain text otherwise, with the models the harness accepts as the options and the closest spelling first. One question per unresolved name. If a worker model cannot be expressed in this harness, or the harness has no per-agent model setting at all, stop and say so. A launch that cannot be pinned would run on the orchestrator, and rule 1 forbids that.
  3. State the task in one to three lines, from the arguments or the conversation, not from the code. If it cannot be stated, ask.
  4. Read the repository's instruction files and the files the task names. You need to know the ground before you can split it.
  5. Ask how hard the workers should think. The arguments name the models, never the reasoning effort, so ask for it every run through the host's structured question tool, in plain text when the host has none. Ask once, after the task is stated and before the split, so the question can say what the work is. Do not carry an answer over from an earlier run or infer one from the task; a cheap model thinking hard and an expensive one thinking little are different bills, and the choice is the user's.

Read the full file on GitHub · 228 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +5 lines d11a74ec28c2
  2. 1mo ago First seen · 223 lines · 118 tokens per session scan A 87e418d7eff7

Subscribe to this mod's changes

qstack-slalom is a skill published in the GitHub repository hani-q/qstack (7 stars, last pushed 3d ago), licensed MIT. It adds 118 tokens to every session and 2,673 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-17.

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