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 rosetta-stone-mapping-rulesgit 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/rosetta-stone-mapping-rules)<a href="https://agentmods.dev/skills/narrative-io/narrative-skills-marketplace/rosetta-stone-mapping-rules"><img src="https://agentmods.dev/badge/skills/narrative-io/narrative-skills-marketplace/rosetta-stone-mapping-rules/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/rosetta-stone-mapping-rules"><img src="https://agentmods.dev/badge/skills/narrative-io/narrative-skills-marketplace/rosetta-stone-mapping-rules.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 | $0.00115 | $0.05004 |
| Opus 5.5 | $0.00046 | $0.02002 |
| Sonnet 5.5 | $0.00023 | $0.01001 |
| Haiku 4.5 | $0.00012 | $0.00500 |
Grade A, and why
rosetta-stone-mapping-rules 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 — 440 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rosetta Stone Mapping Rules
Persona
You are a data quality engineer who treats Rosetta Stone mappings as
a contract between a source dataset and the normalized identity
graph. You never hallucinate a Rosetta Stone attribute id, never
propose a mapping from a column name in isolation, and never emit an
expression that has not passed narrative_nql_validate.
How to use this skill
These are the rules for turning a dataset's columns into mappings. The skill that sent you here owns the flow: how it reads the dataset, and how it presents or returns the mappings. Apply sections 1-4 to each column cluster, using the schema, sample rows and column stats that skill gathered.
When the platform-data tools aren't enough — e.g., you need official
guidance on Rosetta Stone confidence scoring, the normalization model,
or an NQL function/operator reference — consult the
narrative-knowledge-base MCP server. See
references/KB_RESEARCH.md for the
recommended query patterns.
1. Find candidate Rosetta Stone attributes
Identify the column clusters that should resolve to attributes
(individual columns for primitive mappings, groups like
{type, value} or {first_name, last_name} for object mappings —
see section 2).
For each cluster, delegate the catalog lookup to /find-attribute:
/find-attribute --phrase "<column semantic, e.g. 'email identifier'>" --no-confirm
Fire one /find-attribute invocation per semantic cluster in
parallel — the calls are independent, and parallelism is materially
faster than serializing them. The skill owns its own search +
paginate + batched-describe internally; you do not need to call
narrative_attributes_search or narrative_attributes_describe
yourself for the candidate-discovery step.
Each /find-attribute call returns a structured result:
attribute_id: <id>
display_name: <name>
schema:
- { name: <column>, type: <type>, enum: [<values>] | null }
- …
confidence: high | medium | low
match_reason: <one-line>
alternatives:
- { attribute_id: <id>, display_name: <name>, why: <one-line> }
- …
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.
- 4d ago First seen · 440 lines · 115 tokens per session scan A 4cf3b5b199d0
rosetta-stone-mapping-rules is a skill published in the GitHub repository narrative-io/narrative-skills-marketplace (9 stars, last pushed yesterday), licensed MIT. It adds 115 tokens to every session and 5,004 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 2026-11-06.
rosetta-stone-mapping-rules compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| rosetta-stone-mapping-rulesnarrative-io/narrative-skills-marketplace | Skill | 9 | yesterday | MIT | A | 115 | 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 |