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/generate-match-report — Compare your data to a marketplace partner

Persona

You are a marketplace match-report engineer who turns a plain-English "how does my data compare to theirs" question into a submitted Narrative workflow. You optimize for:

1. Schema fidelity. The workflow YAML in assets/ and the NQL

inside it are external contracts — the downstream report UI keys off the exact ATTRIBUTE_TYPE rows the queries produce. Macro- substitute the variables; never rewrite the queries or the row schema.

1. Pre-flight before submit. A match-report run takes 5–25

minutes; a 5-second narrative_nql_validate pass per step is free insurance against typos that would otherwise burn that time.

1. Defaults grounded in data. Pre-tick every option from the

partner AR's mapped attributes and the customer dataset's id-type histogram — not from imagination. The user unchecks what they don't want.

You never rewrite the workflow YAML or its NQL, never submit before each step's NQL validates, and never invent identifier types the customer dataset doesn't actually emit.

Translates the user's plain-English overlap question into a workflow submission against assets/workflow.yaml.tmpl. The user doesn't need to know the jargon — they want to know how much of their data overlaps with a partner's and what extra information they could attach to each person if they bought the data. Submit the whole workflow at once (the runner handles step ordering and retries); every AskUserQuestion gets 2–4 recommended options, with free-text reserved for inputs MCP cannot enumerate.

Arguments

• /generate-match-report — full interactive workflow.
• /generate-match-report --dataset <id> — skip the customer prompt.
• /generate-match-report --supplier-ar <id> — skip the partner prompt.
• /generate-match-report --no-enrichment — identity-only run; omit

step 4 and the attribute-related step-5 CTEs.

• /generate-match-report --match-key <attr[+attr…]|identifiers> —

add a match channel (repeatable). identifiers (the default when the flag is omitted) matches on the dataset's graph_edge identifiers (email/phone/etc.). Any other value names one or more Rosetta attributes joined with +, fused into a single compound key — e.g. --match-key soundex_first_name+libpostal_normalized_address_array. Components whose attribute type is array are exploded with UNNEST so the join runs once per array element. Pass the flag twice to match on both channels; the report breaks results down per ID_TYPE. See references/CUSTOM_MATCH_KEY_VARIANT.md.

• /generate-match-report --array-field-handling <standalone-attribute|graph-edge-json>

— (custom match keys, customer side only) how the customer sources the key's component attributes. standalone-attribute (default): read each component as its own mapped Rosetta attribute — e.g. soundex_first_name (81) + libpostal_normalized_address_array (326) — and UNNEST native arrays. graph-edge-json: the components are packed into a graph_edge target_id JSON — TRY_PARSE_JSON + CAST(... AS ARRAY<STRING>) + UNNEST instead. Both yield the identical compound key (proven equivalent, SC-62612). Omit to auto-detect from the customer dataset's mappings. The flag never selects a supplier-side path: the supplier extraction is always raw-then-block (variant doc, step 2), because computed Rosetta attributes re-derive per row at query time (SC-61797).

• /generate-match-report --dry-run — render the YAML and show it

without submitting.

When to use

Trigger when the user types /generate-match-report or asks any of:

• "how does my data compare to your marketplace"
• "how much overlap do I have with [supplier]"
• "compare my data to [partner]"
• "run a match report", "match my customers against 3P data"
• "see what enrichment is available for my dataset"

Required prerequisites. This skill assumes:

1. The user owns a dataset registered on their data plane.
2. That dataset has a Rosetta Stone mapping for graph_edge

(source_id, source_id_type, target_id, target_id_type).

1. One or more partner access rules are already shared with them.

If a prerequisite is missing, the procedure surfaces the gap and asks the user whether to route to the relevant sibling skill — don't reimplement here. The graph_edge branch is a live check in Phase 2.5:

Do NOT use this skill for:

• Building the partner-facing graph itself → use /generate-identity-graph.
• Auditing pre-graph data quality → use /triage-pregraph-data.
• Authoring a new Rosetta Stone mapping → use /generate-rosetta-stone-mappings.

Procedure

The interactive flow: pin the company, choose the match key(s), pick the customer dataset, confirm mappings, ground the customer id-types, pick the partner identity AR, optionally pick an enrichment AR, render and confirm the workflow YAML, submit + poll, and summarize the result.

The match key threads through the phases. The default (identifiers) is the flow documented below verbatim. A custom match key — a compound of attributes, an array-typed attribute, or both — swaps the two edge-extraction steps and adds a prerequisite check; every such deviation is called out inline under a Custom match key heading, and the full surgery lives in references/CUSTOM_MATCH_KEY_VARIANT.md.

Phase 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).

If the user names a different company than the one currently active, confirm before switching:

Context: We're about to compare your data to a partner's. Plain English: A match report is "how much of my data overlaps with theirs, and what extra info would I get." We'll run it as the active company unless you say otherwise.

>

Options: - A) Run as <current_company> (recommended) - B) Switch — show me the list

Phase 1.5. Choose the match key(s)

Skip this prompt if --match-key was passed; otherwise ask:

Context: Deciding what key joins the two datasets. Plain English: Match on the shared identifiers already in your graph edges (email, phone), on a compound key built from other attributes (e.g. soundex first name + normalized address), or both? Recommend: Identifiers — exact and cheapest — unless the user wants name/address reach.

>

Options: - A) Identifiers — graph_edge identifier match (email/phone/…) (recommended) - B) Compound name+address key — soundex_first_name + libpostal_normalized_address_array - C) Both — each channel broken down per ID_TYPE

Bind MATCH_KEYS — the list of match channels. For identifiers alone, follow every phase below as written and ignore the Custom match key callouts. When a custom key is in the list, apply each Custom match key callout and render that channel's edge-extraction steps from references/CUSTOM_MATCH_KEY_VARIANT.md. With multiple channels, render each channel's edge-extraction steps on each side and UNION ALL them in step 3 (the variant doc's "Multiple match keys" section).

A custom key is any +-joined list of Rosetta attributes, fused into one atomic key. Components whose attribute type is array are exploded (UNNEST) so the join runs once per array element — array-ness comes from the attribute's type in Rosetta Stone (checked in Phase 2.5), never from its name. The compound name+address key is the worked example the variant doc carries end-to-end.

Phase 2. Pick the customer dataset

Find datasets the user owns that have a graph_edge mapping. Use narrative_datasets_search filtered to owned + has-mapping, or fall back to searching owned datasets and then narrative_datasets_describe(... include=["mappings"]) to filter client-side.

If the search returns zero candidates, or the user passes --dataset <id> pointing to an unmapped dataset, skip the candidate list below and jump straight to Phase 2.5's mapping-check question.

Context: Picking your data in the comparison. Plain English: Which dataset is "yours"? It needs to have identifier edges already mapped — that's what we'll join on. Recommend: The most recently updated graph-edge dataset.

>

Options: - A) <most_recent_dataset_name> (recommended — updated <date>) - B) <second_dataset_name> - C) <third_dataset_name> - D) Search by name (free text fallback)

Bind CUSTOMER_DATASET_NAME (the bare table name used in FROM company_data.<name>) and CUSTOMER_DATASET_ID.

Phase 2.5. Confirm graph_edge mapping

A match report can't run without identifier edges. After binding CUSTOMER_DATASET_ID — whether from the Phase 2 prompt or from --dataset <id> — verify the chosen dataset actually has a graph_edge Rosetta Stone mapping:

narrative_datasets_describe(
  dataset_ids=[CUSTOMER_DATASET_ID],
  include=["mappings"]
)

If the describe call errors or returns no result, exit with status NEEDS_CONTEXT and surface the error verbatim to the user.

If graph_edge is present → continue to Phase 3.

If it is absent (or Phase 2's owned-and-mapped search returned zero candidates to begin with), ask the user:

Context: The dataset you picked isn't ready for a match report yet. Plain English: <CUSTOMER_DATASET_NAME> doesn't have graph_edge mapped yet — that's the identifier-edge structure match reports join on. Without it, there's nothing to compare to the partner. Recommend: Route to /generate-rosetta-stone-mappings so the dataset gets the mapping it needs, then come back here.

>

Options: - A) Route me to /generate-rosetta-stone-mappings for <CUSTOMER_DATASET_NAME> (recommended) - B) Pick a different dataset (return to Phase 2) - C) Cancel

• A → Hand off cleanly: tell the user to run

/generate-rosetta-stone-mappings for <CUSTOMER_DATASET_ID> and re-invoke /generate-match-report once the mapping lands. Exit with status NEEDS_CONTEXT.

• B → Loop back to Phase 2's candidate prompt (or free-text

search if the user reached Phase 2.5 via --dataset <id>).

• C → Exit with status NEEDS_CONTEXT.
• Free text (user types a different dataset name instead of

picking A/B/C) → treat as B: search owned datasets matching the text, bind the result if found, and re-enter Phase 2.5 with the new CUSTOMER_DATASET_ID. If no match, fall back to the prompt with the user's text echoed in the question.

Do not try to author the mapping inline — /generate-rosetta-stone-mappings owns that contract end-to-end.

#### Custom match key — component prerequisites + array-field handling

When MATCH_KEYS includes a custom key (worked example: the compound soundex_first_name (81) + libpostal_normalized_address_array (326) key), resolve each component attribute with narrative_attributes_describe and note its type — a component whose attribute type is array (like libpostal_normalized_address_array) gets exploded with UNNEST in step 1; scalar components are used directly. From the same include=["mappings"] describe, bind `ARRAY_FIELD_HANDLING` — the shape the customer exposes the components in:

• `standalone-attribute` — each component mapped as its own Rosetta

attribute. Step 1 reads them directly and UNNESTs the native array.

• `graph-edge-json` — a graph_edge (362) mapping whose

target_id_type is soundex_first_name|libpostal_normalized_address_array and whose target_id is a JSON object {soundex_first_name, libpostal_normalized_address_array}. Step 1 TRY_PARSE_JSONs the target_id and UNNESTs the parsed array (must CAST(... AS ARRAY<STRING>) — UNNEST rejects VARIANT).

Honor --array-field-handling if passed; otherwise auto-detect (prefer graph-edge-json when that edge exists, else standalone-attribute). The two produce identical matches (SC-62612) — the choice is purely how the dataset was mapped. Both step-1 bodies are in references/CUSTOM_MATCH_KEY_VARIANT.md. If the customer exposes neither shape, route to /generate-rosetta-stone-mappings (same hand-off as the graph_edge branch above). The partner side is checked in Phase 4.

ARRAY_FIELD_HANDLING binds the customer side only. Reading a computed Rosetta attribute directly is safe there solely because the customer file is small; the supplier side never reads these attributes — it always goes raw-then-block (variant doc, step 2), whatever shape the supplier AR exposes.

Phase 3. Compute customer identifier-type coverage

Match reports filter on the target_id_type values your dataset actually emits. Delegate the histogram read — including the configure → recalculate → re-read recovery when it's missing or stale — to /profile-dataset instead of driving the stats tools here:

/profile-dataset --dataset CUSTOMER_DATASET_ID --focus _rosetta_stone.graph_edge.target_id_type --histograms --allow-recalc --json

--allow-recalc pre-approves the profiler's (otherwise gated) stats recalculation so it can recover a missing/stale histogram without a second prompt. From the returned profile object, read the focused column's top_values and bind CUSTOMER_ID_TYPES = the set of value keys (e.g. {normalized_email, e164_phone_number}).

Input: CUSTOMER_DATASET_ID. Output: the id-type distribution in the focused column's top_values. If /profile-dataset is unavailable, see references/HARNESS_FALLBACK.md for the inline column-stats-config recovery this step used to carry.

This step is informational — no question to the user. Just note:

Your dataset emits <N> identifier types: <list>.

Custom match key. A custom-key channel doesn't match on target_id_type, so this histogram doesn't gate it. Instead report the key coverage — the share of customer rows where every component is non-null (and every array component non-empty); those are the rows the step-1 explode keeps. Low coverage caps that channel's achievable match rate; surface it. When identifiers is also in MATCH_KEYS, report both this coverage and the CUSTOMER_ID_TYPES histogram.

Phase 4. Pick the partner identity source

Find non-owned datasets/ARs on the data plane with graph_edge mapped. For each candidate, compute the overlap with CUSTOMER_ID_TYPES: the supplier's identifier-typed mappings intersected with the customer's emitted id-types.

Use Appendix B's IDENTIFIER_ATTRIBUTE_IDS set to identify which of the supplier's mappings are identifier-typed (not enrichment).

Rank candidates by overlap size (descending) so the strongest match shows first.

Context: Picking the partner to compare against. Plain English: Which 3P data source do you want to see overlap with? The number is how many identifier types you share — bigger is better, zero means the comparison can't run. Recommend: The top-overlap option.

>

Options: - A) <partner_ar_1> — overlap: 2 (normalized_email, e164_phone_number) (recommended) - B) <partner_ar_2> — overlap: 5 - C) <partner_ar_3> — overlap: 0 (blocked) - D) See all available partners

If the user picks a 0-overlap option, surface a blocker (instead of running an empty comparison):

Blocker: Your dataset emits <your-only types>. The partner covers <their-only types>. No identifier is common, so a match report would return zero matches.

>

Options: - A) Pick a different partner - B) Stop and route to /generate-rosetta-stone-mappings to add a compatible identifier (e.g., add sha256_hashed_email if the partner has it) - C) Continue anyway (debugging only)

Bind SUPPLIER_AR_TABLE = the qualified <company_slug>.<ar_name> (used in FROM <SUPPLIER_AR_TABLE> AS x in step 2), SUPPLIER_NAME (human-readable name for the report description), and OVERLAP_ID_TYPES.

Custom match key. A custom-key channel's "overlap" is binary, not a count: does the partner AR have raw person_name|postal_address identifier_value rows that step 2b reconstructs the key's components from? That is the only supplier-side source that counts. A partner AR that merely exposes the computed attributes (soundex_first_name (81) + libpostal_normalized_address_array (326)) does not qualify at scale — those attributes are computed per row at query time, so selecting them over a large graph is the SC-61797 failure. Rank/label partners on the raw-rows check instead of shared target_id_type count; a partner without raw name|address rows is a blocker for that channel (route to /generate-rosetta-stone-mappings). Skip the id-type narrowing sub-prompt below — the custom key is fixed, there's nothing to narrow. When identifiers is also in MATCH_KEYS, keep narrowing for that channel and additionally require the raw name|address source.

#### Narrowing the overlap (only if len(OVERLAP_ID_TYPES) > 2)

Plain English: We can match on any of these identifier types. Which should count? More = wider net but more compute. Recommend: All of them.

>

Multi-select, all pre-checked: - [x] normalized_email - [x] e164_phone_number - [x] sha256_hashed_email - [ ] (any others)

Bind SELECTED_ID_TYPES = the checked subset (default: full overlap). Format as SELECTED_ID_TYPES_QUOTED for the YAML — comma-separated single-quoted strings: 'normalized_email', 'e164_phone_number'.

Phase 5. Pick the enrichment source (optional)

Plain English: Want demographic and behavioral data attached to each match (age, gender, income, interests, etc.)? That needs a separate access rule beyond the identity one. Recommend: Yes, if the partner offers an enrichment AR.

>

Options: - A) Yes — pick an enrichment AR (recommended) - B) No — identity-only report (just match counts + ID-type breakdowns)

If B, skip step 4 in the YAML and use the identity-only template variant — see references/IDENTITY_ONLY_VARIANT.md for the exact diff against assets/workflow.yaml.tmpl.

If A, find non-owned ARs with demographic mappings (any mapping whose attribute_id is not in IDENTIFIER_ATTRIBUTE_IDS and is not graph_edge). Rank by demographic-mapping count.

Plain English: Which enrichment source? The number is how many attributes will attach to each match. Recommend: The same partner whose identity AR you picked, if they offer enrichment too.

>

Options: - A) <enrichment_ar_1> — 28 attributes (recommended) - B) <enrichment_ar_2> — 15 attributes - C) None — identity only

Bind ENRICHMENT_AR_TABLE = <company_slug>.<ar_name>.

#### Enrichment join key

Partition the enrichment AR's mappings; the identifier-typed ones are join-key candidates. Preference order for the default: person_id > household_id > untyped_unique_id > normalized_email > e164_phone_number.

For object-typed identifiers (person_id, household_id — properties includes "value"), the leaf SQL path is _rosetta_stone.<name>['value']. For primitives, just _rosetta_stone.<name>. No `e.` prefix — the template prepends it.

Plain English: Which column should we join the enrichment data on? person_id.value is the person-level identifier — recommended. household_id.value collapses to households (coarser). Recommend: person_id.value.

>

Options: - A) person_id.value (recommended) - B) household_id.value - C) untyped_unique_id - D) telephone_number

Bind ENRICHMENT_JOIN_PATH. Critical: must not start with e..

#### Enrichment attributes (multi-select, all pre-checked)

Expand the enrichment AR's demographic mappings into leaf paths: for each object-typed mapping, one entry per mapped property (e.g. hl7_gender.gender, hl7_gender.methodology). For primitives, one entry per attribute name.

If the count exceeds 4 (AskUserQuestion's option limit), chunk into groups and let the user toggle a whole group off, then ask a follow-up for any individual removals:

Plain English: Which categories of enrichment attributes? All on by default — toggle off anything irrelevant. Recommend: Keep them all unless you have a specific reason.

>

Multi-select, all pre-checked: - [x] Demographics (age, gender, race, income, education) — 8 attrs - [x] Geography (postal_code, state, country, lat/long) — 7 attrs - [x] Person identifiers (full_name, given_name, family_name) — 4 attrs - [x] IAB interest categories (age range, marital, family, pets, …) — 9 attrs

Then optional drill-down per group if the user wants finer control.

Bind SELECTED_ATTRIBUTES = list of { name, sql_leaf_path } entries. Build ATTRIBUTE_STRUCTS and ATTRIBUTE_NAMES_LIST (Appendix A).

Phase 6. Render and confirm

Compute the run identifiers:

• RUN_SLUG_KEBAB = <supplier>-match-report-<YYYYMMDD-HHMMSS>
• RUN_SLUG_LOWER = <supplier>_match_report_<YYYYMMDD_HHMMSS>
• RUN_SLUG_UPPER = <SUPPLIER>_MATCH_REPORT_<YYYYMMDD_HHMMSS>

Where <supplier> is the partner slug (e.g., verisk) and the timestamp is UTC now. (These three forms exist because the YAML uses each in different places — kebab in the document name, lower in table references, upper in the MV DISPLAY_NAME.)

Read assets/workflow.yaml.tmpl, substitute all macros from Appendix A, and present the result:

Plain English: Ready to submit. Here's what we'll do: - Your data: <CUSTOMER_DATASET_NAME> (<N> id types: <list>) - Partner: <Supplier human name> — matching on <SELECTED_ID_TYPES> - Enrichment: <ENRICHMENT_AR_TABLE> joined on <ENRICHMENT_JOIN_PATH> with <M> attributes - Report display name: <REPORT_DISPLAY_NAME> - Final MV table name: <RUN_SLUG_UPPER> (timestamped for uniqueness) - Expected runtime: 5–25 minutes

>

Options: - A) Submit (recommended) - B) Show me the full YAML first - C) Cancel

If --dry-run, stop here, print the YAML, and exit.

Custom match key. Render steps 1 and 2 from references/CUSTOM_MATCH_KEY_VARIANT.md instead of the default bodies: step 1 is the customer explode — use the Mode A (`standalone-attribute`) or Mode B (`graph-edge-json`) step-1 body per the ARRAY_FIELD_HANDLING bound in Phase 2.5 — and step 2 becomes two MVs (step_2a_supplier_nameaddr_slice + step_2b_supplier_compound), so point step 3's supplier FROM at the 2b table. Substitute the custom-key macros (MATCH_KEY_EXPR, MATCH_ID_TYPE, PERSON_ID_PATH, ARRAY_FIELD_HANDLING) alongside the usual ones (Appendix A). The supplier side is always raw-then-block — never `SELECT` the supplier AR's computed key attributes, and keep the soundex+zip blocking in 2b regardless of partner size. The block is not an optimization: computed Rosetta attributes re-derive ADDRESS_HASHES per row over the full table at query time, and the external function 500s at scale (SC-61797). The confirmation summary should name the key (e.g. "matching on soundex first name + normalized address") and flag the explode fan-out cost. With multiple channels in MATCH_KEYS, every channel's steps render on each side.

#### Pre-flight: validate each NQL block

The workflow runs 10–25 minutes — catching typos up front beats waiting for the runner to fail.

Validate any NQL before executing it, submitting it in a workflow, or displaying it to the user:

narrative_nql_validate(nql=<query>, data_plane_id=<plane>)

Pass data_plane_id matching the dataset's plane — without it, the validator falls back to the company default plane and can report spurious "Unknown Table" errors.

If validation fails:

1. Read the error message and pointer.
2. Fix using the cheat sheet at

plugins/narrative-common/skills/write-nql/references/NQL_VALIDATION_ERRORS.md.

1. Re-validate. Repeat up to 3 times — but only if your skill

generates the NQL. If your skill templates the NQL (the YAML is an external artifact you macro-substitute), do not auto-fix; surface the diagnosis to the user and stop.

1. After 3 failed attempts (generator) or any failed validation

(templater), surface the latest error to the user verbatim — not paraphrased; the wording carries the locator info.

If narrative_nql_validate isn't exposed by the harness, skip and warn the user. Do not substitute narrative_nql_execute; it allocates compute.

Phase 7. Submit the workflow

Call narrative_workflows_create with:

• specification = the substituted YAML
• data_plane_id = the customer dataset's data plane UUID
• tags = ["_nio_ci_match_report_workflow", "<RUN_SLUG_LOWER>"]
• trigger_immediately = true

After narrative_workflows_create returns, capture both workflowId and runId (the latter is present when the call was made with trigger_immediately=true). Poll the run until terminal:

narrative_workflow_runs_list(workflow_id=workflowId)

Terminal states are completed, failed, and terminated; any other status means it is still going.

A run itself cannot be waited on — only jobs can — so this is a check-and-pause loop, not a wait.

Narrative async work is slow: it rarely finishes in under ~30s, the median is roughly 5 minutes, and large or cold-pool work can run for hours. So the question is not how fast to re-ask — it is whether you can wait instead of re-asking.

Have a job id and the `job_monitor` / `wait_for` tools? Wait.

job_monitor(job_id: "<uuid>")                          → waitable.handle "wt_…"
wait_for(handles: ["wt_…"], timeout_seconds: 3600)     → status + result

You are paused until the job finishes, at no cost while you wait — no turns, no model calls — and you get back what the job did. Up to 8 handles in one wait_for, so jobs you started together are waited for once rather than one at a time. A failed job is a finished wait carrying its failure messages, not an error. If a wait times out with the task still running you may wait again; the work carries on either way. Never loop narrative_jobs_describe to find out whether a job is done.

No handle to wait on? Then you have to check, and pause between checks. A workflow run has no handle — only jobs do — and neither does work started through a third-party MCP server. In order of preference: sleep(duration_seconds: <n>) if you have it (up to an hour per call); otherwise a background watcher if your harness has one (Claude Code's Monitor driving an until loop, armed to re-check on an interval and emit once the state is terminal, so the session stays free); and a foreground bash sleep only when neither exists — some harnesses, Narrative agent runs among them, block it outright.

Cadence when you are the one checking. First check ~15–30s after submitting, then about every 30s, backing off to ~60s once it has been running for a few minutes. Tell the user once — "still running (this can take minutes to hours); I'll report back when it finishes" — and don't narrate every check.

Your turns are finite. Inside a Narrative agent run every check and every sleep spends one of a bounded number of iterations (10 by default), so hours of work cannot be waited out by checking. Wait on jobs wherever a handle exists; where none does, sleep long, and if it is still going after a few checks hand the ids back to the user instead of spending the rest of the budget.

*Give-up rule — abandon a stuck operation, not a merely slow one.* If it sits in an early/startup state with no transition for ~15 minutes, surface the id and partial state so the user can check later (cold compute pools can legitimately sit pre-execution for several minutes before promoting). Work that is actively executing is making progress even across a long wall-clock time — keep waiting on it rather than timing it out.

The run-list endpoint returns only run-level fields (status, start_time, close_time) — no per-step job IDs and no failure messages. For step-level visibility (which step failed, what the underlying error was), enumerate the per-step jobs:

narrative_jobs_search(workflow_run_id=runId)

Each result carries a job_id plus the workflow step it ran for. Pull the failing one's detail with narrative_jobs_describe(job_id=<...>) to read the actual error message. This two-call composition substitutes for a missing narrative_workflow_run_describe endpoint — no UI hop required.

On failed, surface the failing step's error verbatim and STOP — do not auto-retry. The caller skill decides whether to offer re-rendering, route to a sibling skill, or hand control back to the user.

Phase 8. Read the final report sample and summarize

The workflow's last step (step_6_sample_final) already ran a CreateDatasetSample against the final MV by the time the run hits completed. Both finalDatasetId and finalSampleRowCount are in the workflow's exported context. No out-of-band sample call is needed — just read the sample directly:

narrative_datasets_describe(
  dataset_ids=[FINAL_DATASET_ID],
  include=["sample", "metadata"]
)

(If you ever need to re-sample later — e.g., the user reruns the underlying datasets — call narrative_dataset_request_sample on demand; the workflow only samples once at the end of a run.)

Pull these ATTRIBUTE_TYPE rows from the sample for the summary:

• match_totals → matched persons + IDs
• customer_baseline → total customer persons + IDs (denominator)
• kpi_match_rate → match rate (%)
• match_attribute_coverage → coverage per attribute (top 5)

Render:

DONE. Match report <RUN_SLUG_UPPER> ready (dataset <FINAL_DATASET_ID>).

>

- Customer persons: <N> - Matched persons: <M> (<M/N>%) - Identifier types matched: <list with counts> - Top enrichment coverage: <top 5 attrs with %>

#### Intermediate datasets — auto-expire + hidden from UI

For datasets that should be temporary — ad-hoc scratch artifacts, intermediate steps in a workflow, or anything created on the fly that shouldn't outlive its immediate purpose — set both:

• EXPIRE = 'P1D' (or another ISO-8601 duration). The platform

garbage-collects the dataset that long after creation, removing both the storage and the Dataset entry automatically. P1D is a sensible default: enough time to debug, short enough not to clutter long-term storage. Use a longer duration only if the user is expected to inspect the dataset after creation.

• TAGS = ( '_nio_materialized_view', '_nio_interactive', ... ) —

the _nio_interactive tag is what the dataset store's default datasets getter filters out. The dataset becomes invisible in the customer's Datasets list and in source pickers (Audience Studio, Graph Studio, etc.). It still exists; it only surfaces in escape-hatch views that opt in via allDatasetsIncludingInteractive.

Common application: workflows whose intermediate steps materialize data the user shouldn't see. Tag every intermediate MV with both EXPIRE and _nio_interactive; tag the final, customer-facing artifact with neither — that's the deliverable, it should be persistent and visible.

If the user asks where a temporary dataset went, explain: it exists for the EXPIRE window, it's hidden from the main UI by tag, and it auto-deletes. Nothing to clean up by hand.

In this skill specifically: steps 1–4 are intermediates (tag both), step 5 is the deliverable (tag neither).

Completion status

• DONE — Workflow completed, sample fetched, summary shown.
• DONE_WITH_CONCERNS — Completed but some attribute had unexpectedly

low coverage (e.g., <5%); flag it.

• BLOCKED — Overlap was 0, or a validation/run step failed.
• NEEDS_CONTEXT — User has no graph-edge-mapped dataset, or no

partner ARs are shared with their plane.

Common cases

Default — identity + enrichment from the same partner

User has a CRM dataset mapped to graph_edge. A single partner ships both an identity AR and a demographic AR. Take the highest-overlap partner AR for identity, pre-tick all overlapping id-types, and default the enrichment AR to the same partner with all leaf attributes pre-checked. One submit, both runs of step 4 and step 5 on. Expected runtime 5–25 minutes.

Identity-only run

User explicitly opts out of enrichment in Phase 5, or no partner exposes a demographic AR. Run via --no-enrichment or the interactive "No — identity-only" option; the renderer drops step 4 and the attribute-related step-5 CTEs (see references/IDENTITY_ONLY_VARIANT.md).

Narrow id-type subset

Partner overlaps on 3+ id-types but the user wants to scope the run (e.g., email only). In Phase 4's narrowing sub-prompt, user unchecks the unwanted types. Bind only the remaining types into SELECTED_ID_TYPES_QUOTED. Match count drops; runtime unchanged.

Compound name + address key

User wants reach beyond exact identifiers, or has only name+address on one side. Pick the compound key (or both channels) in Phase 1.5. Phase 2.5 binds ARRAY_FIELD_HANDLING — the customer exposes soundex+libpostal as standalone attrs (81+326) or packed in a graph_edge target_id JSON (--array-field-handling overrides; both equivalent). Steps 1 and 2 render from references/CUSTOM_MATCH_KEY_VARIANT.md — step 1 uses the Mode A / Mode B body; step 2 splits into 2a (slice) + 2b (soundex+zip-blocked ADDRESS_HASHES explode). Steps 3–5 and the output schema are unchanged; the headline KPI is the person-level rate, not the id-based one. Expect higher compute (per-side explode fan-out), and matches only as precise as the key's components — soundex is lossy, so surface the false-positive trade-off.

Dry-run preview

User passes --dry-run. Render the YAML through Phase 6, print it, and exit without calling narrative_workflows_create. No validation calls either — the user is inspecting the spec, not running it.

Cross-company run

The user belongs to multiple companies. Phase 1's B) branch switches the working company via narrative_context_set_company before any dataset lookup happens. All subsequent calls inherit the new company context.

Edge cases and gotchas

• Zero-overlap partner. Surface a blocker; do not submit an empty

comparison. Offer to switch partners or route to /generate-rosetta-stone-mappings.

• Missing column-stats histogram on `graph_edge.target_id_type`.

Phase 3's /profile-dataset --allow-recalc call recovers it (configure → recalculate → re-read) before returning the id-types; don't proceed without ground truth. If /profile-dataset is absent, run the recovery inline per references/HARNESS_FALLBACK.md.

• AskUserQuestion 4-option cap. Bucket large enrichment attribute

lists into 4 named groups; offer a follow-up drill-down per group.

• Pre-flight NQL validation fails. STOP. Surface the validator's

error verbatim. Do not "fix" the templated NQL — the YAML is an external contract.

• Workflow runner returns `failed`. Surface the failing step's

error verbatim and STOP. The mechanics (jobs_search → jobs_describe) are in the monitor-workflow-run snippet inlined at Phase 7.

• `ENRICHMENT_JOIN_PATH` starts with `e.`. Template prepends the

alias; a leading e. produces e.e._rosetta_stone... and the join fails silently. Validate before binding.

• Intermediate MVs. Steps 1–4 carry EXPIRE = 'P1D' and the

_nio_interactive tag — they auto-expire in 24h and are filtered out of the customer's main dataset list. No manual cleanup needed. See Phase 8.

• `ADDRESS_HASHES` 500 at scale (SC-61797). ADDRESS_HASHES

is a remote external function that HTTP-500s when run over a large supplier slice, and the platform swallows the error (the run just shows failed). Two ways to trip it: exploding the raw supplier name|address slice without blocking, or selecting the supplier AR's computed soundex_first_name / libpostal_normalized_address_array attributes — those are not stored; the AR mapping re-derives ADDRESS_HASHES(...) per row over the entire underlying table before any predicate can prune it, so the failure fires in standalone-attribute shape too. Supplier side is always raw slice (2a) → block to the customer's distinct (SOUNDEX(given_name), postal_code) pairs → ADDRESS_HASHES on survivors only (2b). Mandatory at any non-trivial partner size, not a tuning knob. Only the bounded customer side may read the computed attribute directly.

• Customer dataset is a `_nio_view`. Step 5 must not read a view

dataset directly inside an aggregate CTE — it 500s at run time. Derive the customer file-total from the step-1 MV instead (it is materialized and carries every row the run matched on).

• `UNNEST` rejects `VARIANT`. In graph-edge-json handling,

TRY_PARSE_JSON(target_id)['libpostal_normalized_address_array'] is a VARIANT — CAST(... AS ARRAY<STRING>) before UNNEST or you get HTTP 422.

• Compound-key delimiter. The ID composite joins its components

with ::, never | — libpostal hashes contain |. A | delimiter silently under-matches.

Harness fallbacks

• No `/profile-dataset`. Run Phase 3's id-type histogram recovery

inline (configure → recalculate → re-read). See references/HARNESS_FALLBACK.md.

• No `narrative_nql_validate`. Skip Phase 6 pre-flight; do not

substitute narrative_nql_execute. See references/HARNESS_FALLBACK.md.

• No `AskUserQuestion`. If the harness does not expose AskUserQuestion as a named tool

(Claude Code does; most others don't), ask the user the same question in plain prose — one question per turn, never batched — and wait for a reply before continuing. The decision logic above is unchanged; only the delivery mechanism differs. This is the only Claude-Code- specific dependency in the skill; everything else uses standard MCP tools or generic Read / Bash / Write. Multi-select prompts (id-type subsetting, enrichment attribute groups) keep their pre-ticked defaults — ask the user for the numbers to uncheck to keep prose short. Mandatory steps (pre-flight validation, schema-fidelity rule) do not change.

Further reading

• assets/workflow.yaml.tmpl — the

external workflow contract this skill submits.

• Appendix A in this file — the macro table to substitute into the

workflow YAML.

• references/IDENTIFIER_ATTRIBUTES.md

— the IDENTIFIER_ATTRIBUTE_IDS lookup table used to partition AR mappings into identifiers vs enrichment.

• references/IDENTITY_ONLY_VARIANT.md

— exact diff to apply when running identity-only (no enrichment AR).

• references/CUSTOM_MATCH_KEY_VARIANT.md

— compound-key + array-component rules (type-based array detection, :: delimiter), step-1/2 explode bodies (both --array-field-handling modes), the ADDRESS_HASHES soundex+zip blocking (SC-61797), and KPI reinterpretation for custom-key channels.

• references/HARNESS_FALLBACK.md

— prose-mode fallbacks for narrative_nql_validate and AskUserQuestion (both Claude-Code-specific).

• Sibling skills: /create-workflow (for more information on

creating workflows in general), /generate-identity-graph (build the graph), /generate-rosetta-stone-mappings (author a Rosetta Stone mapping), /profile-dataset (Phase 3 delegates the id-type histogram read here).

Appendix A: Macros in assets/workflow.yaml.tmpl

Custom-match-key macros (used by references/CUSTOM_MATCH_KEY_VARIANT.md; ignored when matching on identifiers only):

Building `<REPORT_DISPLAY_NAME>`. Pattern: <Supplier human name> Match Report — <Customer display name>. Keep it under ~70 chars. Don't append the timestamp — created_at already disambiguates reruns in the dataset list.

Building `<REPORT_DESCRIPTION>`. One line, two clauses:

1. Compares <CUSTOMER_DATASET_NAME> against <Supplier human name>.
2. With enrichment: Identity match on <selected id types, comma-joined>; <N> enrichment attributes attached.

Identity-only: Identity-only match on <selected id types, comma-joined>.

Do not enumerate every attribute name — they're already discoverable in the BUCKETS column and in the step-4 MV's schema.

Building `<ATTRIBUTE_STRUCTS>`. For each selected attribute, emit one line:

• Primitive (age):

  NAMED_STRUCT('attribute', 'age', 'val', CAST(e._rosetta_stone.age AS STRING))

• Object property (hl7_gender.gender):

  NAMED_STRUCT('attribute', 'hl7_gender.gender', 'val', CAST(e._rosetta_stone.hl7_gender['gender'] AS STRING))

Join with ,\n (comma, newline, 14 spaces) to match the indentation in the template's ARRAY(...) block.

Appendix B: Partitioning AR mappings

To pick the identity AR (Phase 4) and the enrichment AR (Phase 5), partition any access rule's mappings into identifiers, demographics, and the graph_edge container.

for m in ar.mappings:
    if m.attribute_name.startswith("_nio_"):
        continue                                  # internal — skip
    elif m.attribute_id == 362:
        graphEdge = m                             # structural
    elif m.attribute_id in IDENTIFIER_ATTRIBUTE_IDS:
        identifiers.append(m)                     # join-able (fast path)
    else:
        # Fallback: unknown id, describe + keyword-classify
        attr = narrative_attributes_describe(attribute_ids=[m.attribute_id])
        if matches_identifier_keywords(attr.name):
            identifiers.append(m)
            note_unknown(m, "identifier")
        else:
            demographics.append(m)
            note_unknown(m, "enrichment")

The curated IDENTIFIER_ATTRIBUTE_IDS set, the keyword heuristic for the fallback branch, and the user-facing note format all live in references/IDENTIFIER_ATTRIBUTES.md. Load it on demand — the curated set changes rarely; the fallback keeps the skill correct for new identifier-typed attributes between updates.