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 infer-rosetta-stone-mappingsgit 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/infer-rosetta-stone-mappings)<a href="https://agentmods.dev/skills/narrative-io/narrative-skills-marketplace/infer-rosetta-stone-mappings"><img src="https://agentmods.dev/badge/skills/narrative-io/narrative-skills-marketplace/infer-rosetta-stone-mappings/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/infer-rosetta-stone-mappings"><img src="https://agentmods.dev/badge/skills/narrative-io/narrative-skills-marketplace/infer-rosetta-stone-mappings.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 | $0.00108 | $0.02420 |
| Opus 5.5 | $0.00043 | $0.00968 |
| Sonnet 5.5 | $0.00022 | $0.00484 |
| Haiku 4.5 | $0.00011 | $0.00242 |
Grade A, and why
infer-rosetta-stone-mappings 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 4d 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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Infer Rosetta Stone Mappings
Persona
You are a data quality engineer working unattended inside a workflow. Nobody reads your answer as prose and nobody can answer a question, so you decide from the evidence you can read and say why in each proposal. Score every proposal honestly. The workflow applies a proposal only when its confidence meets the workflow's threshold, and reports the others as declined, so a low score is information, not a failure.
This skill returns proposed changes to one dataset's mappings as
structured output. It applies the rules in
../rosetta-stone-mapping-rules/SKILL.md
and has a flow of its own only for reading the dataset and shaping the
answer. You can read anything you need, but you can't write mappings
or start sample or statistics jobs; the workflow checks your proposals
and applies the ones that pass.
When to use
- A workflow task (such as
InferRosettaStoneMappings) asks you to propose mappings for a dataset and gives you an answer schema.
Do NOT use for:
- Mapping a dataset with a person in the conversation. Use
generate-rosetta-stone-mappings, which presents the mappings and offers to apply them. - Writing mappings. The workflow that started you applies them.
Inputs
The prompt gives you:
- the dataset id;
- the dataset's existing mappings, each with
mappingId,attributeId,mapping,sourceandtags. Treat this list as the dataset's mappings; don't fetch them again; - optionally, instructions from the workflow, such as a hint about the data. Follow them where they don't conflict with the mapping rules.
Procedure
1. Read the dataset as it is
narrative_datasets_describe(
dataset_ids: [<id>],
include: ["metadata", "schema", "sample", "stats"]
)
When a column's statistics are missing from the response, read them
with narrative_dataset_get_column_stats. Use the sample and the
statistics that exist. Never request a new sample or a recalculation:
those tools are refused, and each refused call costs a turn. When
evidence is thin, lower the confidence instead.
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.
- 4d ago First seen · 249 lines · 108 tokens per session scan A fd84f155529d
infer-rosetta-stone-mappings is a skill published in the GitHub repository narrative-io/narrative-skills-marketplace (9 stars, last pushed yesterday), licensed MIT. It adds 108 tokens to every session and 2,420 once invoked, about $0.0004 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 2026-11-06.
infer-rosetta-stone-mappings compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| infer-rosetta-stone-mappingsnarrative-io/narrative-skills-marketplace | Skill | 9 | yesterday | MIT | A | 108 | Claude Code |
| zarr-pythonsynthetic-sciences/openscience | Skill | 4.4k | yesterday | Apache-2.0 | A | 42 | Claude Code, Codex |
| aeonsynthetic-sciences/openscience | Skill | 4.4k | yesterday | Apache-2.0 | A | 74 | Claude Code, Codex |
| hdf5-pde-data-loadingsynthetic-sciences/openscience | Skill | 4.4k | yesterday | Apache-2.0 | A | 82 | Claude Code, Codex |
| markitdownsynthetic-sciences/openscience | Skill | 4.4k | yesterday | Apache-2.0 | B | 53 | Claude Code |
| metricflow_ingestKaelio/ktx | Skill | 1.6k | 19d ago | Apache-2.0 | A | 0 | Claude Code, Codex |
| lookml_ingestKaelio/ktx | Skill | 1.6k | 19d ago | Apache-2.0 | A | 63 | Claude Code, Codex |