temporal-awareness

name: temporal-awareness description: "Use for latest/current/recent/today/yesterday/tomorrow questions, changing models, versions, prices, schedules, executives, laws, weather, scores, or other time-sensitive facts. Ground answers in clock and timezone, verify live when needed, and convert relative dates. Do NOT use for stable history." compatibility: "Requires: python3. Optional: web/search tooling for live verification of volatile claims."

Ground the session in the real clock before answering anything that could have changed.

Verified locally on April 9, 2026 in Pacific/Auckland (NZST, UTC+12). The motivating failure mode is real: OpenAI's official Help Center now documents GPT-5.3 and GPT-5.4 in ChatGPT, while stale assistants still default to GPT-4o-era wording from older memory.

Decision Tree

What is the time-sensitive failure mode?

Need a clean session anchor before answering
- Run python3 scripts/capture_temporal_context.py --format markdown
- Read references/bootstrap.md
Need to decide whether a prompt can be answered from stable knowledge or needs live verification
- Run python3 scripts/recency_guard.py --prompt "..." --format markdown
- Read references/recency-triage.md
Need patterns for latest, today, yesterday, tomorrow, timezone math, or source selection
- Read references/verification-patterns.md
Need refresh rules for long-running sessions, day rollovers, or recurring work
- Read references/long-horizon.md
Need to debug stale assumptions, outdated model names, or relative-date mistakes
- Read references/gotchas.md

Quick Reference

Task	Command or file	Why
Capture local and UTC time context	`python3 scripts/capture_temporal_context.py --format markdown`	Load the real clock into the session before answering
Compare the current moment across zones	`python3 scripts/capture_temporal_context.py --format markdown --extra-zone America/New_York --extra-zone Europe/London`	Avoid silent timezone drift
Classify whether a prompt needs live verification	`python3 scripts/recency_guard.py --prompt "What is the latest OpenAI model for coding?" --format markdown`	Separate stable questions from volatile ones
Run the deterministic probe suite	`python3 scripts/probe_temporal_awareness.py --format pretty`	Verify the skill still behaves as designed
Learn the startup workflow	`references/bootstrap.md`	Session-load order and exact commands
Handle relative dates and volatile facts	`references/verification-patterns.md`	Absolute-date conversion and source selection

Reading Guide

If the user says...	Read
"Before you answer, what date is it here and what timezone are we using?"	`references/bootstrap.md`
"Do I need to browse for this or is it stable?"	`references/recency-triage.md`
"What does latest mean here?"	`references/verification-patterns.md`
"This session has been running for hours, should we refresh the date context?"	`references/long-horizon.md`
"Why are agents still mentioning GPT-4o / old CEOs / stale prices?"	`references/gotchas.md`

Operational Rules

Run scripts/capture_temporal_context.py before answering any prompt that uses relative time language or depends on the current date, time, or timezone.
Run scripts/recency_guard.py on prompts containing latest, current, today, yesterday, tomorrow, recent, as of, or volatile domains such as models, prices, laws, schedules, weather, executives, or live events.
Treat the system clock as authoritative for local date and time, but treat external current facts as untrusted until verified against live sources.
Convert relative dates into absolute dates before answering whenever there is even a small chance the user is thinking in a different timezone.
Refresh the temporal anchor when the session crosses midnight, spans multiple hours, changes timezone context, or revisits rolling external data.

Verified Behaviors

scripts/capture_temporal_context.py emits local and UTC timestamps, timezone name candidates, UTC offset, locale, platform, and optional extra-zone snapshots.
scripts/recency_guard.py detects relative-date language and volatile domains, and distinguishes stable historical prompts from current-state prompts.
scripts/probe_temporal_awareness.py runs deterministic checks across smoke, edge, negative, and disclosure-style scenarios.
The skill explicitly treats model families and product versions as volatile; this prevents stale answers like defaulting to GPT-4o when the official docs have moved on.
Long-running sessions get explicit refresh rules instead of assuming the first temporal anchor stays valid forever.

Gotchas

Do not confuse clock grounding with live verification: the system clock can tell you what day it is locally, but it cannot tell you today's stock price or the current CEO.
Relative dates are timezone-dependent: today, yesterday, and tomorrow are wrong if you silently assume UTC while the user is thinking locally.
Historical facts are not the same as current-state facts: Who won the 2024 election? is usually stable; Who is president now? is not.
Model names drift faster than agents admit: treat model families, versions, limits, and availability as volatile unless you just verified them.
Long sessions go stale: if work spans a day boundary or a DST change, rerun the capture step and restate the new absolute date.

Helper Scripts

scripts/capture_temporal_context.py prints a session-ready time anchor in JSON, text, or Markdown.
scripts/recency_guard.py classifies a prompt as stable, system-clock-sensitive, or live-verification-required.
scripts/probe_temporal_awareness.py runs deterministic regression checks across the core heuristics.
scripts/validate.py validates structure, references, eval coverage, and Python syntax.
scripts/test_skill.py validates the packaging and runs the temporal probe suite.