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/noibu/ai-plugin/segment-analysisnpx skills add Noibu/ai-plugin --skill segment-analysisgit clone --depth 1 https://github.com/Noibu/ai-pluginWhat 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 | $0.00061 | $0.00884 |
| Opus 5 | $0.00030 | $0.00442 |
| Sonnet 5 | $0.00012 | $0.00177 |
| Haiku 4.5 | $0.00006 | $0.00088 |
Grade A, and why
segment-analysis 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Noibu Segment Conversion Analysis
How it works
- Quick answer (focused question) → run 1–2 queries, answer directly, offer to go deeper.
- Full analysis (broad request, bare invocation, or "yes" to the offer) → the workflow below.
Setup — before any query
Work quietly. The first thing the user sees is the triage board widget — no "let me…" commentary before it.
- Resolve the domain first. Use what the user gave (name or UUID). If nothing, ask via
AskUserQuestion— don't ask about anything else. If exactly one domain, skip the question. - Call
list_scheduled_tasksnow — store whether a task already exists for this domain. This sets the action bar button label at render time ("Edit schedule" vs "Schedule Insights") without blocking queries. - Confirm every field name by role before using it. Reference files name fields by role, never hard-coded column names.
- Default window: last 30 days unless the user specifies otherwise.
- If the dataset is near-zero (total sessions 0 or a handful), say plainly that the domain has no traffic in this window and offer to widen the range.
Quick answer
For focused questions ("which channel converts best?", "how does mobile compare to desktop?", "which countries are underperforming?"):
- Run only the 1–2 queries needed and answer directly.
- Offer full analysis via
AskUserQuestion(not prose):- "Yes — survey device, country, and channel, then dig into anomalies"
- "No thanks"
Don't load the triage-board or scheduling references for a quick answer.
Full analysis
Loading reference files: Use the Read tool. All files live in a references/ subdirectory next to this SKILL.md — derive the base path from wherever this file was loaded from.
- Read
references/queries.mdANDreferences/triage-board.mdnow, before running any queries. - Run the workflow from queries.md.
- Render the overview card, priority cards, and action bar as one
show_widgetusing triage-board.md.
What ships with it
5 files 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.
- 2d ago First seen · 83 lines · 61 tokens per session scan A df2f03fcd2c7
segment-analysis is a skill published in the GitHub repository Noibu/ai-plugin (5 stars, last pushed 6d ago), licensed MIT. It adds 61 tokens to every session and 884 once invoked, about $0.0003 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 2026-08-30.
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