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 agentmods add skills/szarkans/multi/asknpx skills add szarkans/multi --skill askgit clone --depth 1 https://github.com/szarkans/multiWhat 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 | $0.00083 | $0.00883 |
| Opus 5 | $0.00042 | $0.00441 |
| Sonnet 5 | $0.00017 | $0.00177 |
| Haiku 4.5 | $0.00008 | $0.00088 |
Grade A, and why
ask scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ask several models
!sh -c 'for p in "$CLAUDE_PLUGIN_ROOT/scripts" "$HOME/.claude/skills/multi/scripts" "./.claude/skills/multi/scripts"; do [ -x "$p/probe.sh" ] && { "$p/probe.sh"; echo "scripts-dir: $p"; exit 0; }; done; echo "probe: NOT FOUND — locate scripts/probe.sh in this plugin and run it yourself"'
One model's answer is one model's priors. Three answers from different families show you where the question is actually settled and where it only looked settled.
This is not a review and not a vote. You do not pick a winner and you do not merge them into one answer — that throws away the only thing the user came for. Show what each said, then say where they differ.
$SCRIPTS is whatever the probe printed as scripts-dir:.
Run it
Start the external models first — they take 30–90 seconds and OpenCode spends most of a minute just waking up. Answer the question yourself while they run.
RUN="$($SCRIPTS/run-dir.sh --slug <two-to-four words: the project and the job, e.g. skills-fixing-multi>)"
$SCRIPTS/ask.sh --question "<the user's question, verbatim>" \
--out-prefix "$RUN/ask" \
--backend "codex,opencode:<model from probe>" \
[--fallback <fallback model from probe>] [--effort <low|medium|high|xhigh|max>]
Pass the question as the user asked it. Do not rewrite it into a better prompt: the point is what different models do with the same words. Add context they would need and could not see — the file you are both looking at, what was already ruled out — but leave the question itself alone.
Effort defaults to high. Raise it for a hard design question, drop it to
medium or low for something factual.
If neither external model is available, say so and just answer normally. This
skill has nothing to add without them, and pretending otherwise is worse than
a plain answer. Point them at /multi:setup to connect one.
Report
Your own answer is one of the three, not the frame around the other two. Write it before you read theirs — otherwise it is not an independent answer.
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.
- 2d ago First seen · 80 lines · 83 tokens per session scan A b67d65d954c2
ask is a skill published in the GitHub repository szarkans/multi (8 stars, last pushed 2d ago), licensed MIT. It adds 83 tokens to every session and 883 once invoked, about $0.0004 per session on Opus 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 2026-08-31.
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