// Productivity

Calendar Invite Timezone Preflight

Check normalized iCalendar events for floating times, DATE mismatches, missing boundaries, sequence errors, and recurrence evidence.

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Format: Agent skill, not a browser app. You need an AI agent that can load its instructions and use the required tools.

Package requirements: Normalized UTF-8 JSON input documented in the package. Python 3.10 or newer on Windows, macOS, or Linux. Local files only; no live account or provider connection.

You get: findings.csv with stable codes and record identifiers review-manifest.csv with every reviewed item

Licence and purchase details

Review the package licence at the destination.

Review the offer and access method at your chosen marketplace. Agensi provides connected-agent access, not a download. Buying a skill does not include an AI-agent subscription or setup service.

Agensi access: This product’s bundled-script workflow through Agensi has not been verified here. Tests of other products do not establish this package’s compatibility. Local setup examples apply to separately obtained downloadable packages.

See tested hosts, versions and workflow limits.

What problem does Calendar Invite Timezone Preflight solve?

Calendar invites can render at the wrong time or span the wrong day when DTSTART, DTEND, TZID, VALUE=DATE, sequence, or recurrence evidence disagree.

Use it to

Preflight a generated ICS invite.
Find floating DATE-TIME values.
Review all-day and recurring event evidence before distribution.

What it returns

findings.csv with stable codes and record identifiers
review-manifest.csv with every reviewed item
result.json with READY, REVIEW, or BLOCK gate
report.md with a human-readable decision handoff

A representative input and result

fixture-backed sample

input {"events":[{"uid":"event-1","dtstart":"20260801T140000Z","dtend":"20260801T150000Z","value_type":"DATE-TIME","sequence":0}]}

result READY; UTC event boundaries and sequence evidence reviewed.

Access and approval boundaries

Read access to one user-supplied local JSON evidence file.
Write access only to the selected local output directory.
No browser, network, credential, account, environment-variable, or external-action permission.

Known limitations

Version 1 checks normalized core properties, not full client-specific rendering.
It does not expand complex recurrence rules or import an invite.
A passing result does not establish identical behavior across calendar clients.

// choose with context

Is Calendar Invite Timezone Preflight the right skill?

Best fit

Calendar invites can render at the wrong time or span the wrong day when DTSTART, DTEND, TZID, VALUE=DATE, sequence, or recurrence evidence disagree.

It returns

findings.csv with finding codes and record identifiers; review-manifest.csv listing reviewed items; result.json with gate, reviewed-record count and finding totals; report.md with findings and the supplied-evidence limitation.

Do not use it as

Version 1 checks normalized core properties, not full client-specific rendering.

Compatibility, access, version, and licence

Reads or accesses: Owner-supplied UTF-8 JSON containing normalized events with UID, start/end, value type, timezone and sequence/recurrence evidence. It does not connect to a calendar account.
Compatibility: Normalized UTF-8 JSON input documented in the package. Python 3.10 or newer on Windows, macOS, or Linux. Local files only; no live account or provider connection.
Version: 1.0.0
Licence: Review the package licence and seller terms at the linked destination.

What happens next

Open the Agensi listing, check the current offer and connect a supported agent. Agensi loads the skill through its connector; it no longer provides a download. Approve any purchase before use. Check Agensi’s current connection guide before changing an existing connection.

Questions

Does it change or upload anything?

No. It reads supplied local evidence and writes only to the selected output directory.

Is this an AI judgment?

No. The packaged checker is deterministic and applies documented rules to the supplied input.

What does a ready result mean?

It means no automated finding was produced. The responsible owner still reviews the original source and decides what to do.

How does malformed input behave?

Invalid JSON, duplicate identifiers, missing required arrays, and invalid core values fail closed.

Can I audit the result?

Yes. Every finding carries a stable code, record identifier, severity, source, and message.