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Z User e45b68bb8b feat: IMAP mail sync with thread-to-contact matching (PRD §6.1)

Real IMAP connection via imapflow library:
- Connects to IMAP server (IMAPFlow, TLS on port 993)
- Fetches recent messages (configurable limit, default 20)
- Extracts: from, fromEmail, subject, date, snippet (200 chars)
- Thread-to-contact matching (PRD §6.1):
  1. Match sender email against persons table
  2. If no person match, extract domain and match against company.website
  3. Returns matchedCompany + matchedPerson for each email
- Audit-logged (action: crm.mailsync:imap)
- Zod validated (host, port, user, password, folder, limit)
- 502 on IMAP connection failure with detail
- 400 on validation failure

Tested: invalid creds → 502 'IMAP connection failed', missing host → 400

2026-10-27 22:50:50 +00:00

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Data Contract For Experiment Runs

Use this file whenever the run is intended to support a measured experiment, or when the agent searches for and binds a dataset on the fly.

experiment-suite is a skill system, not a fixed benchmark repository. That means data.py and config.yaml should be generated for the current run, not frozen globally. The price of this flexibility is that the run can become impossible to audit unless the dataset choice is written down explicitly. data_contract.md is the lightweight fix.

Required file

Write $RUN/data_contract.md with this structure:

# Data Contract

## Task binding
- research question:
- task type:
- target variable:

## Dataset route
- mode: user-supplied | agent-discovered | reused-public | controlled | synthetic-fallback
- source:
- repository / landing page:
- accession / DOI / stable URL:
- version / snapshot / date accessed:
- licence / access condition:
- persistent identifier quality: DOI | accession | stable URL | none

## Files used in this run
- file or subset:
- role: train / val / test / metadata / labels / source data

## Split protocol
- split source:
- split rule:
- leakage risks:

## Pre-processing contract
- raw to model input:
- normalization / filtering:
- exclusions:

## Reuse and publication notes
- can this run be marked measured:
- source-data / FAIR follow-up needed:
- unresolved risks:

Minimum rules

agent-discovered is valid. If the agent searched Hugging Face, ModelScope, GEO, SRA, or a paper-linked repository and selected one dataset, record that explicitly.
If the data are reused public data, record the source and version.
Prefer a stable identifier (DOI, accession, or repository record) over an ad hoc file link.
If the data are controlled or restricted, record the access route and do not imply open reproducibility.
If no acceptable real dataset is available, say so and fall back to synthetic-fallback.

When a run may be called measured

A run may be marked measured only if:

the code actually ran on non-simulated data,
the dataset contract names the source and split,
results.json provenance points to the measured run,
the report and paper can trace the numbers back to this run.

Measured does not mean perfect benchmark design. It only means the numbers came from real execution on bound data rather than a simulator.

FAIR minimum for this run

For experiment-suite purposes, the minimum FAIR-style bar is:

the dataset source is findable from the contract,
the access condition is explicit,
the version or snapshot is recorded,
the files used for train/val/test are named,
the preprocessing boundary is stated clearly enough for another engineer to rerun.

Red flags

data.py reads local files but data_contract.md never says what they are
dataset source is only "open-source data" with no stable route
no split rule is written down
controlled data are treated as openly shareable
report says measured, but provenance still points to a simulator

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