sumsub-conformance

Sumsub — Conformance (regulation ↔ deployed config)

Traces every requirement of a regulation through the live config graph and answers, per requirement: collected? → scored? → enforced? The headline finding it surfaces — that nothing else does — is COLLECTED-BUT-NOT-ENFORCED: the config gathers and scores the data a regulation needs, then doesn't act on it (e.g. an appropriateness questionnaire that's scored but never routed).

This is an advisory gap assistant, not a compliance guarantee. It verifies the config-representable parts deterministically and honestly delimits the rest (semantic intent = judgment; SDK/platform outcomes = out of scope). Get a human to sign off.

When to use

• Existing client: "here's our regulation, here's our tenant — what's actually enforced?"
• New client who configured Sumsub with the create- skills: close the generate → verify* loop —

did the assembled config end up enforcing the regulation it was built from?

Endpoints (read-only)

All via scripts/sumsub_curl.sh (App-Token HMAC). resolve_graph.sh wraps these:

Auth — App Token + secret (sandbox only)

This skill talks to the public Sumsub API and signs each request per the authentication reference. The how-it-works writeup lives in `sumsub-api-auth`.

⚠️ Sandbox tokens only. Read-only, but still: do not accept a production App Token. If the user offers one, refuse and ask for a sandbox pair at <https://cockpit.sumsub.com/checkus/home?sbx=true> (Connect Sumsub to your AI agent -> Build & configure -> Generate token). The helper rejects tokens that don't start with sbx:.

Procedure

Use the scripts — do not hand-roll the deterministic steps. The whole point of this skill is that resolution and the verdict are deterministic, not re-derived by eyeball each run: - Resolve the graph ONLY via `resolve_graph.sh`. Do not call /resources/... endpoints directly, list levels by hand, or strip the HTTP <code> trailer yourself — the script does all of that and emits one clean graph. - If the graph can't be resolved, STOP — never work around the failure. A 401/403, a 404, or 0 levels / "level not found" from resolve_graph.sh means the run cannot proceed. Report the failure plainly (likely a bad/non-sandbox token or wrong level name) and stop. Do not edit resolve_graph.sh (or any script) to route around the error, add a by-id fallback, retry a different endpoint, or otherwise improvise a graph — and never emit a report, bucket counts, or a CONFORMANT verdict from a partial or substituted graph. No resolved graph → no findings. - Derive the verdict ONLY from `trace.py` / `report.py`. Do not read the workflow/questionnaire JSON by eye and decide "enforced / not enforced" yourself — that re-introduces variance and hallucination. The COLLECTED-NOT-ENFORCED finding must come from the engine. - If a script already ran, trust its output — do not re-verify it with manual API calls. - To inspect the graph for the Map step, run `python3 scripts/graph_summary.py "$CONFIG"`. Do not hand-iterate the graph JSON: questionnaires/poaPresets/crossCheckPresets are dicts keyed by id (iterating yields id strings → 'str' has no attribute 'get') and entitlements is a list. graph_summary.py prints the section/item indices + scores you need, correctly. The agent's judgment belongs to two steps only: ingest (reading the regulation) and map (matching requirements to deployed config elements — questions, docSets, fields, countries, entitlements). Everything else is the scripts.

Artifacts — MANDATORY location & naming. Every run MUST write its artifacts into the same directory as the source regulation file, named from its stem (filename without extension). Never save in the cwd or under improvised names. Derive the exact paths once, up front: `` eval "$(python3 scripts/artifact_paths.py "<path/to/RegFile.pdf>")" # $REQUIREMENTS = <dir>/<stem>_regulations.json (the requirements you build in steps 1+3) # $CONFIG = <dir>/<stem>_config.json (resolve_graph output, step 2) # $REPORT = <dir>/<stem>_report.md (the rendered report, step 6) e.g. Level4regulations.pdf → Level4regulationsregulations.json`, `Level4regulationsconfig.json, Level4regulations_report.md. Use $REQUIREMENTS / $CONFIG / $REPORT` verbatim in the steps below.

1. Ingest the regulation (judgment). Just Read the document with the Read tool — it reads PDFs natively (renders pages + extracts text), so nothing needs to be installed. For a PDF over ~10 pages, Read it in page ranges (the Read tool's pages parameter, ≤20 pages per call). Reading visually is equal or better than a text extractor — it preserves tables, headings and layout that flat-text extraction loses. Optional, only if already installed and never a blocker: pypdf/pdftotext can pull a long, text-heavy PDF in one shot, and scripts/render_regulation.py (PyMuPDF) pre-renders a scanned/image-only PDF to PNGs for explicit DPI control — see `references/conformance-method.md` step 1. If a library is absent, just Read the PDF directly.

Then extract requirements and write them to `$REQUIREMENTS` (the <stem>_regulations.json beside the source — see the Artifacts rule above), per `references/requirements-schema.md`: each = {id, source, text, type, enforceable, …}. Always capture `source` — the citation into the original document (section number / heading / page, e.g. "§5.9.2", "Section 2 — Scoring Result", "p.21"); the report leads with it so findings trace back to the regulation, not to an artificial id. Mark outcomes that live in SDK/platform/CRM as enforceable: sdk|platform. Show the extracted requirements to the user and confirm before tracing — this is the highest-risk step.

1. Resolve the deployed graph (deterministic). bash scripts/resolve_graph.sh <levelNameOrId> > "$CONFIG"

(level + questionnaires-with-scores + presets + auto-found workflow + entitlements). This one command replaces any manual level-listing / curl / HTTP-trailer stripping — don't do those by hand.

1. Map (judgment). First inspect the graph: `python3 scripts/graph_summary.py "$CONFIG"` — it

prints questionnaires with [section]/[item] indices + option scores, docSets, APPLICANT_DATA fields, geo, entitlements, and the workflow (don't hand-iterate the JSON). Then for each requirement pick the right type and fill its match — not everything is a questionnaire question. The per-type match shapes are in `references/requirements-schema.md`:

• questionnaire question/score → match: {questionnaire, section, item} (+ expectScores);
• document collected → match: {docSet: "IDENTITY", types: [...]};
• applicant-data field present → type: data-field, match: {field: "dob"} / {fields: [...]};
• country allowlist / prohibited jurisdictions → type: country-eligibility, match: {mustHaveAllowlist: true} / {mustExclude: [...]};
• selfie liveness → type: liveness, match: {};
• capability provisioned → type: entitlement, match: {entitlement: "WATCHLISTS"};
• screening/routing outcome → set a discriminating enforcementSignal (NOT a generic token).

Be conservative: if unsure a thing exists, leave match: null (→ MISSING) rather than guess.

1. Trace (deterministic). python3 scripts/trace.py --requirements "$REQUIREMENTS" --graph "$CONFIG"

→ findings bucketed MISSING / SCORING-MISMATCH / COLLECTED-NOT-ENFORCED / NOT-CONFIG-REPRESENTABLE / CONFORMANT, each with evidence + a basis (deterministic vs judgment). The bucket verdict comes from here — do not decide "enforced / not enforced" by reading the workflow JSON yourself.

1. (Optional) hygiene sub-pass. Pipe the same graph through the bundled linter to catch silent

misconfigurations (entitlement gaps, dangling preset/questionnaire refs, deprecated SDK, invalid AML provider) alongside conformance — entitlements are read straight from the graph: bash scripts/resolve_graph.sh <level> | python3 scripts/lint_config.py. Rule catalog in `references/lint-rules.md` (generated from references/rules.json).

1. Report — render with the canonical formatter to `$REPORT`, do NOT hand-format. The format is

fixed so every run looks identical: python3 scripts/trace.py --requirements "$REQUIREMENTS" --graph "$CONFIG" | python3 scripts/report.py --date <today> > "$REPORT" → Markdown with a headline status, a fixed bucket-count table, findings grouped in fixed order (each with evidence + basis), and a fixed caveats footer. Present $REPORT verbatim. Do not re-summarise it in prose — that's what made past runs differ. (The trace.py JSON is the other canonical format for tooling — save it beside the others if a machine consumer needs it.)

What it does NOT do (say it)

• Not a compliance guarantee or a replacement for a compliance officer.
• Does not verify semantic correctness beyond presence + scores + routing-enforcement

(e.g. whether a question's wording is legally adequate is judgment).

• Cannot verify SDK/platform/CRM outcomes (disclosure pop-ups, demo accounts, read-only mode,

the 25%/10% restriction, CRM timestamps) — these are bucketed NOT-CONFIG-REPRESENTABLE.

Guardrails

• Keep deterministic facts separate from judgment (the basis field). Never present a mapping

guess as a verified finding.

• The trace walks requirements → deployment only — it never flags a deployed question as

"extraneous" (avoids the false-positive class). Report extra questions, if at all, as an explicit advisory note.

• enforcementSignal must be discriminating — a generic token (country) matches catch-all

edges and yields false CONFORMANT. See `references/requirements-schema.md`.

• Fail honestly — never work around a resolution failure. If resolve_graph.sh returns an auth

error / 404 / 0 levels, stop and report it. Do not edit the scripts to bypass it or fabricate a report from a partial graph (see the Procedure callout).

See also

• references/requirements-schema.md — the requirement model + buckets.
• references/conformance-method.md — the five steps, guardrails, scope.
• references/lint-rules.md — the bundled hygiene-lint rule catalog (sub-pass).
• `sumsub-analyze-regulation` — the opposite direction (regulation → build plan).

Worked examples (examples/)

• sample-requirements.json + sample-graph.json — synthetic, exercise every bucket (drive test_trace.py).
• fiu-india-vasp-mapped.json + vasp-graph.json — a broad AML guideline mapped against a hypothetical

VASP level; self-contained, trace them together offline: python3 scripts/trace.py --requirements examples/fiu-india-vasp-mapped.json --graph examples/vasp-graph.json | python3 scripts/report.py.

• cfd-requirements.json — real CFD/MiFID requirements (run against a live cfd-onboarding-eu graph).
• fiu-india-requirements.json — the ingest-stage output for that broad guideline (before mapping).
• audit-input{,-dirty}.json — hygiene-linter fixtures (drive test_lint.py).