Verify By Phone

Verify By Phone

Use this skill when an agent must establish whether stored information about an organization is still true, and a phone call to the organization's published line is the way to find out.

verify-by-phone is a verification workflow skill. It places exactly one disclosed outbound CALL-E call per record, extracts the answer from the transcript with a verbatim supporting span, reconciles it against the stored record with transparent match-weight arithmetic, and returns either a calibrated confidence or an explicit abstention. The design goal is an agent that never converts an uncertain phone answer into a confident database write.

When To Use

Use this skill for:

• verifying a provider directory listing: is this practice real, reachable, accepting new patients, taking a given insurance plan
• checking whether a stored business record (hours, services, availability) still matches reality
• refreshing any dataset where the phone is the source of truth and the record is suspected stale
• workflows where an "unknown" outcome must be recorded honestly instead of guessed
• one record at a time, with a human-authorized recipient list

When Not To Use

Do not use this skill to:

• place undisclosed or pretext calls; every call announces that it is an automated assistant and why it is calling, in the same breath
• call wireless or personal numbers; verification targets published organizational lines
• run marketing, sales, lead generation, or any promotional outreach
• batch-dial a list without per-record human authorization
• overwrite a stored record directly from a call result; the output is a verdict with evidence, and the write decision stays with the operator
• guess an answer the respondent did not give; if the call yields no usable answer, the result is an abstention, and that is the correct output

Consent And Disclosure Rules

These are load-bearing, not boilerplate:

1. The call opens by stating the AI identity and the purpose together, before anything else, and announces that the call may be recorded.
2. If the respondent objects to speaking with an automated caller, the call thanks them and ends immediately. The result records the refusal as an unverifiable outcome.
3. Hold time is capped. If the respondent asks the caller to wait, it waits briefly, then reports back rather than waiting indefinitely.
4. The agent never invents information it was not given, on the call or in the result. Concretely: a clinic will often ask the caller for a name, a date of birth, an insurance member or card number, or a reason for the visit. The agent says plainly that it does not have that information, because this is a directory verification call and not an appointment request, and then repeats the question it called to ask. A placeholder is an invention.
5. Voicemail is not an answer. On reaching voicemail or an answering machine the call ends immediately without leaving a message: a directory fact cannot be established from a recording, and nobody should find a robot message on their line.
6. Calls are informational verification, never promotional. Treat every call as recorded with all-party consent requirements in mind.

Verification Workflow

1. Calibrate the abstention threshold first, with scripts/calibrate.py on labeled scenario data, so "confident" means something measurable: at the default level, the true answer falls inside the prediction set at least 90 percent of the time on held-out data. It prints a qhat that step 6 consumes. This runs with no credentials and no calls, so it can be done once, ahead of any dialing.
2. Collect the record to verify: organization name, published phone number in E.164, and the claims to check (for example accepting new patients, accepts a named plan).
3. Confirm the operator authorizes this specific call to this specific number.
4. Build the call task with scripts/place_verify_call.py. The script defaults to a dry run that prints the exact task and recipient without dialing; pass --live only after the dry run looks right.
5. Poll for the terminal result with scripts/poll_result.py, which saves the full payload to a local file.
6. Extract the answer with scripts/extract_answer.py --qhat from step 1. Every extracted field carries the verbatim transcript span and character offsets that support it. Hedged answers ("I think so") keep their polarity at a dampened trust score. Non-responsive turns (wrong number, refusal, "call back later") never count as answers. The answer is served only when the calibrated prediction set is a single value and that value is not "unknown"; otherwise the result is an abstention. Omitting --qhat abstains on everything and labels the output uncalibrated, because a threshold with no calibration behind it guarantees nothing.
7. Reconcile against the stored record with scripts/reconcile_record.py: each agreeing field adds documented bits of evidence, each disagreeing field subtracts them, and the verdict is verified, contradicted, or unverifiable.

Maintenance Note

The scripts in this skill are self-contained copies of the reference implementation in the Attest backend (backend/app in the source repository). They are kept small on purpose so the skill installs with no dependencies on that repository.

That copy is enforced, not promised. tests/test_skill_parity.py in the source repository runs this skill's extractor and the backend's over the same transcripts and fails if they disagree on the answer, on the character offsets of the cited span, or on the cue lexicons themselves. An earlier version of this note asked a human to re-sync by hand, and the copy drifted anyway: the backend learned to trust the last cue in a turn and to read "no problem" as agreement while this script still trusted the first, so a plain "No, we are not" abstained here and answered there.

Requirements

There are two different floors, and conflating them is how you get a confusing failure:

So calibrate, extract, reconcile and every dry run work on 3.9. The moment you actually dial, you need 3.11 or newer, because that is the SDK's own floor and not a choice this skill makes. Both numbers were read from the source rather than assumed: 3.9.6 by running it, 3.11 from the published package metadata.

Two dependency notes worth stating rather than leaving to be discovered:

• Step 3 needs calle-ai and a CALLE_API_KEY. Every other step runs with no credentials and places no call.
• scripts/verify_attestation.py needs cryptography. It is the only other script with a third-party import, and it is optional: it checks an attestation signature and is not part of the verification workflow.

Quick Start

pip install calle-ai

# 1. Calibrate the gate first. Prints the qhat step 5 needs.
python3 scripts/calibrate.py --data references/sample-scenarios.jsonl --alpha 0.1

# 2. Dry run: prints the task and masked recipient, dials nothing.
python3 scripts/place_verify_call.py \
  --org "Example Counseling Center" \
  --phone "+15550101234" \
  --claim-accepting-new-patients yes \
  --claim-plan "Example Health PPO"

# 3. Place the call for real (requires CALLE_API_KEY).
python3 scripts/place_verify_call.py ... --live

# 4. Wait for the terminal payload.
python3 scripts/poll_result.py --call-id call_abc123 --out result.json

# 5. Extract the span-grounded answer. --org is required: an answer is only
#    evidence about this listing if the respondent confirmed they ARE it.
python3 scripts/extract_answer.py --payload result.json --qhat 0.750 \
  --org "Example Counseling Center"

# 6. Reconcile against the stored record. Same --qhat and --org as step 5:
#    reconciliation runs extraction itself, and without either every field abstains.
python3 scripts/reconcile_record.py --payload result.json --qhat 0.750 \
  --org "Example Counseling Center" \
  --claim-accepting-new-patients yes --claim-plan-accepted yes

Steps 5 and 6 run against the bundled references/sample-call.json if you want to see real output before placing any call.

All sample numbers in this skill are reserved fictional numbers. The dry run path and the bundled sample data mean everything except step 3 runs with no credentials and no real call.

Three things make a claim abstain no matter how clearly it was answered, and all three are deliberate:

1. No calibrated threshold (--qhat missing). There is no coverage guarantee to answer behind.
2. Identity not positively confirmed. Absence of a denial is not confirmation. Wrong numbers, answering services and reassigned lines all produce cooperative respondents who are not the listing.
3. Both questions asked in one turn. A single "Yes" cannot be split between two claims after the fact, so it is attributed to neither. The call script asks one question at a time to avoid this.

What The Output Looks Like

extract_answer.py emits one JSON object per claim:

{
  "claim": "accepting_new_patients",
  "answer": "yes",
  "trust_score": 0.9,
  "hedged": false,
  "span": {"turn": 6, "text": "Yep.", "char_start": 0, "char_end": 3},
  "abstain": false,
  "gate": "conformal(qhat=0.750)"
}

An abstention keeps the same shape with "abstain": true, either because the calibrated prediction set held more than one label or because the extractor found no answer at all ("answer": "unknown"). The gate field names the threshold that made the decision, and reads uncalibrated when no --qhat was supplied, which is the one case where every claim abstains regardless of what was said. reconcile_record.py adds the match-weight arithmetic and a verdict. No field ever appears without either a supporting span or an explicit abstention.

Side Effects And Cancellation

• Side effect: exactly one outbound phone call per --live invocation, to the number the operator supplied. Nothing recurs; there is no scheduler in this skill.
• Cost: one billable CALL-E call per live run. Dry runs are free.
• Cancellation: CALL-E does not expose call cancellation, so the moment to stop is before --live. The dry-run default exists for exactly that reason.
• Data: payloads are written to local files the operator names. Phone numbers are masked in console output. Nothing in this skill transmits results anywhere except the CALL-E API itself.

References

• references/api-notes.md: the empirically observed CALL-E payload shape and API behaviors this skill relies on, including facts that were discovered by testing rather than documentation.
• references/verification-protocol.md: the full disclosure script, the legal posture for outbound verification calls, and why abstention is the core design decision.
• references/sample-scenarios.jsonl: labeled fictional scenario data used by scripts/calibrate.py, so calibration runs with no credentials and no calls.