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.
npx skills add hani-q/qstack --skill qstack-slalomgit clone --depth 1 https://github.com/hani-q/qstackWrote 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.
[](https://agentmods.dev/skills/hani-q/qstack/qstack-slalom)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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 asfable-5.1orastra-6.<workers>is one model or a comma-separated list, such asopus-5orgpt-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
- 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.
- 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.
- 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 reportsclaude-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/modelin Claude Code. Do not run the task on the wrong orchestrator, and do not swap the roles around to make the arguments fit. - Resolve each worker model to an identifier the harness accepts, using
/qstack-choose-model'sresolveagainst the discovered catalogue and itsprobe-briefto 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 whoseANTHROPIC_DEFAULT_*mapping is confirmed in Claude Code. In Claude Code theAgenttool takesmodeland theWorkflowscript'sagent()takesopts.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 asopsu-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. - State the task in one to three lines, from the arguments or the conversation, not from the code. If it cannot be stated, ask.
- Read the repository's instruction files and the files the task names. You need to know the ground before you can split it.
- 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.
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.
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.
- 1mo ago Changed · +5 lines d11a74ec28c2
- 1mo ago First seen · 223 lines · 118 tokens per session scan A 87e418d7eff7
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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