[FEATURE] Date/Time Injection into prompts

Preflight Checklist

• [x] I have searched existing requests and this feature hasn't been requested yet
• [x] This is a single feature request (not multiple features)

Problem Statement

Claude Code has basically no temporal awareness beyond the current date. It can't detect prompts that are coming in quick series vs hours apart, nor can it tell if a session has been running continuously for a very long time. Sonnet and Opus are both completely smart enough to make use of that information as context for response framing or inquiries as to next actions.

Proposed Solution

A standard date/time string (e.g. Tue Mar 10 10:16:46 PDT 2026) is 29 characters. Adding it to the context for each prompt would be trivial. A config flag for the behavior seems like a natural.

Alternative Solutions

I'm doing this with a hook, which isn't hard to set up, but this seems like something that should be built in and on by default. Injecting via a hook probably incurs more overhead than would be needed if it was built in.

``{"hookSpecificOutput": {"additionalContext": "currentTime": "Tue Mar 10 10:50:35 PDT 2026"}}``

Priority

Medium - Would be very helpful

Feature Category

Interactive mode (TUI)

Use Case Example

A

1. work in a 3-hour session, prompting consistently over time
2. 2 hour gap for meetings
3. return to session - Claude Code recognizes the gap, can infer that user short term memory is flushed and make fewer assumptions about the warmth of their wetware cache

B

1. work in a 12-hour continuous session
2. Claude Code may recognize signs of fatigue in decision making or responses, maybe be more cautious on destructive changes

C

1. working sporadically in multi-tasking mode for 6 hours
2. Claude Code may recognize distraction and prompt for certainty/clarity

Additional Context

This is less a user workflow feature and more a general tool quality and capability feature. The extent of current model intelligence makes it hard to predict exactly how much mileage this will provide, but as models continue to get smarter the behavior improvement around temporal awareness will grow as well.