passepartout: v0.5.0 hotfix 2 — daemon stable

- Restore (in-package :passepartout) to core-reason
- Move *VAULT-MEMORY* back to core-skills
- Fix ASDF and defstruct/defpackage ordering
- Increase daemon timeout to 120s
- Handshake: 0.5.0

Verified: daemon processes messages, TUI clean, gate trace works

docs/DESIGN_DECISIONS.org

@@ -327,6 +327,8 @@ The structural multipliers are:
 
 4. *Hot state* — in a REPL-based agent, variables, file handles, sub-routine results, and memory objects are already in memory. Every turn in a standard chat agent re-sends the full conversation history. Token costs in chat agents are quadratic: a 10-turn session pays for ~55 "turns" of context (10 + 9 + 8 + ... + 1 = 55). In Passepartout, context is stored once in the Lisp image. A 10-turn session pays for ~10 turns of context. This is an ~82% reduction on protocol overhead alone, before any foveal-peripheral pruning. This argument is testable: send the same multi-turn session through both architectures and count tokens.
 
+5. *Temporal filtering* — time-scoped memory queries (what happened today? what's due in the next hour?) return only nodes matching the time window. The temporal filter is a pure-Lisp hash-table walk with a numeric comparison on ~memory-object-version~. Sub-millisecond. 0 LLM tokens. Competitors without time-indexed memory must serialize all nodes and let the LLM scan for temporal relevance — 5,000–50,000 tokens per temporal query. This is the same principle as the foveal-peripheral model applied to the time dimension.
+
 ** The Compounding Cost Curve — Unique Among Agents
 
 Every AI agent grows more expensive over time. Context histories accumulate. Safety instructions grow more elaborate. Guardrails become longer prompt paragraphs. The user's data grows. The only way to reduce cost in a standard agent is to cap context — sacrificing capability.
@@ -343,6 +345,24 @@ Passepartout has a downward cost curve. Four mechanisms compound:
 
 After 12 months of daily use, Passepartout's per-session costs are expected to be 40–60% of baseline, while competitors' costs rise to 125–140% of baseline. The crossover point is estimated at 3–6 months. This is not a model quality claim — it is a structural property of the architecture.
 
+** Time Awareness as a Structural Advantage
+:PROPERTIES:
+:ID:       design-time-awareness
+:CREATED:  [2026-05-07 Thu]
+:END:
+
+Passepartout's architecture provides three layers of time awareness, each enabled by infrastructure that competitors lack:
+
+*Level 1 — Present Awareness.* The LLM knows the current time, date, and session duration because a single ~format-time-for-llm~ call injects it into the system prompt. Most agents know the date from the OS. None know the time or session duration. The cost is ~8 incremental tokens per call (trivially prefix-cached). The saving is eliminating "I don't know the current time" preamble tokens, time-check tool calls, and incorrect temporal reasoning from a model guessing the time.
+
+*Level 2 — Temporal Memory.* Memory queries accept ~:since~ and ~:until~ parameters. "What did I work on in the last hour?" filters 500 nodes to 12 in sub-millisecond Lisp rather than serializing 500 nodes to the LLM at ~5,000 tokens for it to scan. Every memory node carries a ~memory-object-version~ timestamp (a monotonic ~get-universal-time~ value set at ingest since v0.1.0). The temporal filter is a hash-table walk with numeric comparison. 0 LLM tokens. >90% token reduction on time-scoped queries.
+
+*Level 3 — Proactive Triggers.* The heartbeat tick (existing infrastructure since v0.3.0) scans for approaching deadlines every 60 seconds. When a deadline is within the warning window (~DEADLINE_WARNING_MINUTES~, default 60), a temporal context note is injected into the awareness assembly. The LLM sees "3 deadlines today: Submit report (45min)" in its context without a triggering call. A "what should I work on today?" query is answered from pre-loaded context — 0 LLM tokens versus 1,500–4,000 for an unassisted agent.
+
+None of these three layers require new infrastructure. Time awareness is not a feature Passepartout builds — it is a feature Passepartout *unlocks* by having timestamped memory (v0.1.0), heartbeat+cron (v0.3.0), and the foveal-peripheral context pruning model (v0.2.0) already in place. Adding time awareness costs ~175 lines of Lisp. Building it in competitors would require building the heartbeat, the time-indexed memory, and the proactive context injection — 800+ lines each — and would still cost LLM tokens because their safety verification is prompt-based.
+
+The structural principle generalizes: Passepartout's infrastructure investments compound. Each new subsystem (Merkle memory, heartbeat, skill engine, embedding pipeline) lowers the cost of the next feature. Time awareness is the first demonstration of this compounding — three layers unlocked by infrastructure already built for other purposes.
+
 ** Tiered Pricing: Cheap Models for Simple Tasks, Free for Learned Patterns
 
 The model-tier router (v0.3.0) classifies every task by complexity and routes it to the cheapest capable model. Simple lookups go to tiny local models or deterministic hash table scans (0 LLM tokens). Text processing goes to mid-tier models. Complex planning and code generation go to the premium model. The consensus loop (v0.10.0) only fires for high-impact actions.