Generate Rosetta Stone Mappings

Generate Rosetta Stone Mappings

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 optimize for:

1. Evidence — every mapping is grounded in schema, sample rows, and column stats from narrative-mcp; column names alone are not enough.
2. Validity — every NQL expression is server-validated before it is suggested.
3. Calibrated confidence — low-confidence mappings are surfaced as low-confidence, not promoted to fit a quota.

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.

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.

Overview

Map columns from a Narrative source dataset to Rosetta Stone attributes via progressive calls to the narrative-mcp server. Fetch only the schema slice, sample rows, column stats, and attribute definitions you need for each decision, and validate every expression with narrative_nql_validate (and optionally narrative_nql_execute) before suggesting it.

Without this discipline an agent will either (a) write mappings from column names alone, (b) hallucinate Rosetta Stone attribute IDs, or (c) emit SQL that fails NQL validation. Don't.

When the platform-data tools above 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.

When to use

Triggers:

• "Map this dataset to Rosetta Stone" / "suggest normalized attributes for dataset N"
• "Why is mapping X low confidence?" / "evaluate the mappings on dataset N"
• "Fix this mapping expression" / "improve this NQL expression to handle X"
• "Make a value_mapping / object_mapping for this column"
• Any work involving narrative.rosetta_stone."<attribute>" and a specific source dataset

Do NOT use for:

• Pure NQL query authoring with no mapping intent — go to NQL skills.
• Custom-attribute creation — call this skill first to confirm no Rosetta Stone attribute already covers the column, then hand off.

Procedure

Run these steps in order. Steps 1-3 are mandatory context-gathering; steps 4-6 run per column being mapped; steps 7-8 finalize.

Parallelize where the calls are independent. Most steps below have fan-out points — multiple /find-attribute invocations (one per semantic cluster), a batch of narrative_attributes_describe IDs when reconfirming known attributes, a batch of narrative_nql_validate expressions. Issue these as concurrent tool calls in a single turn instead of looping serially. For very wide datasets (50+ mappable columns), consider spawning a sub-agent per column cluster so each one owns its own find → validate loop and only the final scoring is reconciled at the parent.

1. Pin the company / context

Most Narrative work is scoped to a company. Before any dataset, attribute, or workflow call:

narrative_context_get  → check the active company

If no company is set, or the user named a different one:

narrative_context_search_companies(search_term: "<name>")
narrative_context_set_company(companyId: <id>)

narrative_context_search_companies is global-admin-only. Skip the search/set entirely if the user invoked the skill from a Narrative Platform UI session where the company is implicit (narrative_context_get returns one).

2. Resolve and describe the target dataset

If the user gave a dataset ID, go straight to describe. Otherwise:

narrative_datasets_search(search_term: "<phrase from user>")

Call narrative_datasets_describe with the dataset's ID. For wide schemas (>40 columns) or when the response is truncated, see references/DATASET_DESCRIBE.md for the split-describe pattern and underscore-prefixed-column rule.

3. Profile the dataset's coverage & quality

Delegate per-column profiling to /profile-dataset rather than reading and interpreting stats inline. It owns the stats fetch, the missing/stale-stats recovery (configure → recalculate → re-read), and the null-rate / cardinality / top-value / sample-shape interpretation this skill used to carry itself:

/profile-dataset --dataset <id-from-step-2> --json

Consume the returned profile object (see that skill's output contract): per-column null_rate, distinct_count / approx_distinct, top_values, inferred_shape, and flags. This is the evidence for every mapping decision in steps 5–7 — e.g. an inferred_shape of hash:sha256 alongside a literal type-discriminator column drives an object_mapping; a high_null_rate flag tempers the confidence score.

Input: the dataset id from step 2. Output: the structured profile object. If /profile-dataset is unavailable, fall back to step 2's bundled sample + stats and interpret them inline — the heuristics live in ../profile-dataset/references/INTERPRETATION.md.

4. 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 step 5).

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> }
  - …

The schema field is the contract you need for step 5 — it includes type (primitive vs object), property paths (for object attributes — e.g., type, value, context.source), enum constraints (non-null enum array), and required vs optional flags. This is the only way to learn that detail; do NOT guess attribute IDs from memory or reason from search snippets.

When confidence: low or when alternatives cluster within 1-2 ranking points, treat all close candidates as in-play for that cluster and let step 5's value/object decision and step 6's expression generation discriminate.

When you already know the target attribute ID (the user named one explicitly, or you are evaluating existing mappings — see references/MODES.md), skip /find-attribute and call narrative_attributes_describe(attribute_ids: [<id>, ...]) directly — describing a known ID does not need the search + ranking machinery.

5. Decide value_mapping vs object_mapping

value_mapping shape:

{
  "attribute_id": 123,
  "mapping": {
    "type": "value_mapping",
    "expression": "LOWER(email_column)"
  },
  "confidence": 95,
  "reasoning": "Column name and '@' pattern in all sampled values clearly indicate email.",
  "warnings": []
}

object_mapping shape:

{
  "attribute_id": 456,
  "mapping": {
    "type": "object_mapping",
    "property_mappings": [
      { "path": "type",  "expression": "'sha256_email'", "confidence": 100, "reasoning": "Literal discriminator; all sampled hashes are 64 chars." },
      { "path": "value", "expression": "LOWER(hashed_email)", "confidence": 92, "reasoning": "Lowercase normalization for SHA256." }
    ]
  },
  "warnings": []
}

6. Validate every expression with NQL

narrative_nql_validate takes a full NQL query (parameter name nql), not a bare expression. To check an expression against a dataset's schema, wrap it as a select against the dataset's table reference company_data."<dataset_id>":

narrative_nql_validate(
  nql: 'select <your expression> from company_data."<dataset_id>"'
)

A success response means the expression compiles against the dataset's schema. A structured error points at the offending token. If validation fails, fix the expression (see references/EXPRESSION_SYNTAX.md) and re-validate. When the local reference doesn't cover the symptom, hit the KB gotchas catalog — references/KB_RESEARCH.md lists the troubleshooting entry points (/guides/nql/troubleshooting/unsupported-type-error, /guides/nql/troubleshooting/cross-data-plane-queries, /nql/general/explicit-columns, /nql/general/reserved-keywords, /cookbooks/nql/performance-patterns). Do not suggest a mapping with an expression that has not been validated.

Validates are cheap and independent — fire all candidate expressions as concurrent tool calls in a single turn rather than serializing them.

Optionally, for high-stakes mappings or when the user asked to test, run the expression against real rows. narrative_nql_execute is asynchronous, and it hands back a workflow and its run rather than rows: find the job that run enqueued with narrative_jobs_search(workflow_run_id: "<run_id>"), then follow narrative_jobs_describe(job_ids: ["<job_id>"], include: ["result"]) until state is completed, failed, or cancelled:

narrative_nql_execute(
  nql: 'select <expression> as mapped, "<source_column>" as source from company_data."<dataset_id>" limit 25'
)

Use the run results to:

• Confirm the transformation produces what you expected on real data
• Catch silent type coercions (e.g., string → null because of a malformed cast)
• Drop confidence by ≥20 points and add an enum_mismatch warning if the output values don't match the target attribute's enum

7. Score confidence

For object_mappings, the mapping's confidence is the minimum of its property confidences. A high-confidence type literal cannot rescue a low-confidence value expression.

8. Present the mappings to the user

Show a human-readable summary, not raw JSON. Three parts:

1. A 2-4 sentence overview in first person ("I analyzed 12 columns…").
2. One row per suggested mapping — a markdown table or bulleted list with these fields:
   • Source — the source column(s); for object_mappings, list each contributing column.
   • Target — the Rosetta Stone attribute's display_name, with attribute_id in parentheses.
   • Expression — the NQL expression for value_mapping, or one path → expression line per property for object_mapping.
   • Confidence — the score from step 7.
   • Reasoning — one short line.
3. Any dataset-wide warnings (e.g., "Stats unavailable for 12 columns; expressions validated against sample rows only.").

Do NOT print the underlying JSON. The user reviews the table above; the structured array is held internally for the apply hand-off in step 9 and surfaced only if the user explicitly asks for it.

Sort by confidence descending; for object_mappings, sort by the minimum property confidence.

Hold the structured mapping list in memory for step 9. Each entry follows the value_mapping or object_mapping shape from step 5 (an array of those entries, no envelope):

[
  {
    "attribute_id": 123,
    "mapping": { "type": "value_mapping", "expression": "LOWER(email_column)" },
    "confidence": 95,
    "reasoning": "…",
    "warnings": []
  }
]

If nothing is mappable, say so plainly in the summary, recommend defining a custom attribute, and name the specific columns with no Rosetta Stone equivalent. Skip step 9.

9. Offer to apply — opt-in hand-off

Once the human-readable summary is on screen and the structured mapping list is non-empty, ask the user whether to apply now via AskUserQuestion:

"Apply these mappings to <dataset> now?"

• Apply now — invoke /apply-rosetta-stone-mappings with this list against <dataset>.
• Apply with a dry-run first — same call with --dry-run so the rendered workflow is shown but not submitted.
• Not yet — the user will apply later (offer to print the JSON on request).

On "Apply now" or "Apply with a dry-run first", hand off by calling /apply-rosetta-stone-mappings --dataset <id-from-step-2> --no-revalidate --mappings '<the bare-array JSON from step 8>' (add --dry-run for the second choice). The bare array is one of the accepted input shapes — see ../apply-rosetta-stone-mappings/references/INPUT_FORMAT.md. Pass --no-revalidate because step 6 already validated every expression against the dataset's current schema in this same conversation; the apply skill's Phase 5 re-validation would be a redundant round-trip. Do not re-render anything yourself — the downstream skill owns input normalization, the approval gate, and run polling.

Skip this step entirely when:

• The skill was invoked in one of the alternate modes in references/MODES.md — "Evaluate existing mappings" (the user wanted a scorecard, not a deploy) or "Improve a single mapping expression" (the output is one revised expression, not a full apply set).
• The structured mapping list is empty (nothing to apply).

Common cases

Mapping generation (no existing mappings)

The default. Follow steps 1-9 in order — gather context, validate expressions, present a human-readable summary in step 8, and offer the apply hand-off in step 9.

For alternate entry points — evaluating existing mappings or improving a single mapping expression — see references/MODES.md.

Voice

Use first person ("I analyzed 12 columns…") and conversational language ("cleaned up", not "normalized") in the summary field and in reasoning fields — these strings are user-facing in the Narrative Platform UI's Rosetta Stone normalization tab.

References

• references/DATASET_DESCRIBE.md — narrative_datasets_describe deep semantics: the include allowlist, the split-describe pattern for wide schemas (>40 columns), and the underscore-prefixed-column rule. Read when step 2's happy-path describe isn't enough.
• ../profile-dataset/SKILL.md — step 3 delegates per-column coverage & quality profiling here (stats fetch, missing/stale-stats recovery, null-rate / cardinality / top-value / shape interpretation). Its references/INTERPRETATION.md holds the heuristics this skill used to carry inline.
• references/MODES.md — alternate entry modes: evaluate existing mappings, improve a single mapping. Read if the user is touching up prior work rather than mapping a fresh dataset.
• references/EDGE_CASES.md — reserved-identifier quoting, enum case sensitivity, null handling, object-mapping replace-all semantics, custom-attribute fallback, confidence-vs-validity, token economy, underscore-prefixed columns. Read when an expression won't validate or a mapping feels off.
• references/HARNESS_FALLBACK.md — narrative-mcp unavailable (paste-driven schema + sample, confidence haircut), AskUserQuestion fallback. Read when a tool call errors or the user is outside the Narrative Platform UI.
• references/EXPRESSION_SYNTAX.md — SQL/NQL quoting, function, and CASE WHEN rules. Read when an expression fails narrative_nql_validate or when mapping a reserved-word column.
• references/ENUM_HANDLING.md — generation-vs-evaluation rules for enum-constrained attributes. Read when narrative_attributes_describe shows {value1|value2|...} constraints.
• references/KB_RESEARCH.md — how to query narrative-knowledge-base for Rosetta Stone best practices and NQL references when local files aren't enough.
• ../find-attribute/SKILL.md — step 4 defers to this skill per cluster, --no-confirm, structured results.
• ../apply-rosetta-stone-mappings/SKILL.md — downstream consumer of the bare mapping array from step 8.

Feedback (only if something could be improved)

Silence is success. Do not call submit_feedback when the skill worked. No "great skill!" submissions, no positive feedback, no "FYI" notes.

When friction does occur, submit immediately and without asking the user. submit_feedback is append-only telemetry — it is not a user-visible action and does not require confirmation. If you noticed something missing, unclear, incorrect, surprising, or that wasted your time, file it the moment you've worked around it. Do not defer the submission to a post-task recap, and do not ask the user "want me to submit feedback?" — that's the wrong default for this tool.

One submission per distinct friction point. Submit liberally.

Fields that matter most:

• skill_name: narrative-common:generate-rosetta-stone-mappings (use this verbatim).
• severity: info (nit) | friction (slowed you down) | blocker (stopped you).
• category: missing_info | unclear_instructions | incorrect_instructions | unexpected_behavior | tool_failure | other.
• summary: one concrete line — what went wrong, not how you felt.
• suggested_improvement: the sentence or paragraph that, if added to this skill, would have eliminated the friction. This is the highest-value field — be specific, quote the skill text you'd change.

Optional but useful when known: details, task_context, agent_model, time_lost_minutes.