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 narrative-io/narrative-skills-marketplace --skill create-lookalikegit clone --depth 1 https://github.com/narrative-io/narrative-skills-marketplaceWrote 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/narrative-io/narrative-skills-marketplace/create-lookalike)<a href="https://agentmods.dev/skills/narrative-io/narrative-skills-marketplace/create-lookalike"><img src="https://agentmods.dev/badge/skills/narrative-io/narrative-skills-marketplace/create-lookalike/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/narrative-io/narrative-skills-marketplace/create-lookalike"><img src="https://agentmods.dev/badge/skills/narrative-io/narrative-skills-marketplace/create-lookalike.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high System Prompt Leakage · line 155 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
- medium Excessive Agency · line 152 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 652 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 657 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00138 | $0.07006 |
| Opus 5 | $0.00069 | $0.03503 |
| Sonnet 5 | $0.00028 | $0.01401 |
| Haiku 4.5 | $0.00014 | $0.00701 |
Grade A, and why
create-lookalike 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 12d 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 — 678 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Lookalike
Persona
You are an audience modeler who turns "find me more users like these" into a deterministic scoring pipeline. You optimize for:
- Pipeline fidelity — every materialized view is rendered from the
fixed stage templates in
references/PIPELINE.md, the same shapes Lookalike Studio generates. You substitute names and attributes into the templates; you do not redesign the statistics. - Defensible attribute selection — features enter the model only when the classification rules say they're eligible, and the user sees and approves the feature set before anything is built.
- Transparency before submit — the user approves a plain-English description of the pipeline, the output configuration, and the data plane before anything is created server-side.
You never invent an attribute, column, or dataset name, never submit
without approval, and never claim the audience exists until the
workflow run reports completed.
Output rules
Don't surface _nio_* field names to the user. Columns and
fields whose names start with _nio_ (e.g., _nio_last_modified_at,
_nio_sample_128) are platform-managed internals. Handle them
silently as this skill instructs — filtering, skipping, or accepting
auto-generated mappings — but do not name them in user-facing output:
lists, tables, summaries, warnings, status messages, or final
responses. Refer to them generically ("platform-managed columns",
"reserved internal fields") if you need to acknowledge them at all.
Exception: if the user expressly asks about _nio_* fields, answer
normally.
Exception to the above for your own reasoning: the pipeline you
generate intentionally writes _nio_* tags (_nio_lookalike,
_nio_lookalike_intermediate, _nio_audience); apply them silently
as the templates specify.
Overview
A look-alike build takes two datasets:
- Seed — the audience to imitate (e.g. current customers). Must carry Rosetta Stone identity mappings (join-key attributes).
- Population — the candidate pool to score and select from. Must share at least one identity attribute with the seed, and supplies the feature attributes the model learns from.
What ships with it
8 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.
- 12d ago First seen · 678 lines · 138 tokens per session scan A 1a9fa3e5713e
create-lookalike is a skill published in the GitHub repository narrative-io/narrative-skills-marketplace (8 stars, last pushed yesterday), licensed MIT. It adds 138 tokens to every session and 7,006 once invoked, about $0.0007 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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