| name | memory-informed-longitudinal-work |
| description | Long-running multi-session work (research, eval loops, iterative benchmarks) with continuity -- resume prior lessons, capture per-run outcomes, build up stable truths over time. Use for eval/research/benchmark work that spans sessions. |
| version | 1.0.0 |
Memory-Informed Longitudinal Work
Multi-session engineering work (research, eval loops, iterative benchmarks) where lessons must accumulate across runs. The motivating case: improving a core algorithm over dozens of eval iterations without starting from scratch each session.
Instructions
Step 1: Resume context (session start)
Rely on the SessionStart hook's thread-continuity check for the primer. If this session is a continuation, you'll see a concise "Last session lessons" surface. Treat those as working knowledge — do not re-ask the user about things the primer already stated.
Step 2: Proactively recall per topic shift
When the session pivots to a new sub-topic (new phase, new eval subset, new failure mode), call pensyve_recall:
query: short description of the new sub-topicentity: project + sub-topic entity (per skills/shared/entity-detection.md canonicalization rules)limit: 5
No types filter — longitudinal work benefits from all three memory types; omit to query all. (Passing all three explicitly is equivalent to no filter; omitting is cleaner.)
Surface: Recalled N prior findings on <sub-topic>.
Step 3: Capture three types of memory per run
During the session, classify emerging knowledge into the three memory types and capture accordingly:
| What you learned | Type | Example |
|---|
| Per-run outcome (what this run showed) | episodic | "Run N+1: V7r accuracy improved +3.5% with new threshold" |
| Stable truth about the system | semantic | "Haiku classifier plateaus above temp 0.3" |
| Reusable experiment procedure | procedural | "To calibrate V7r: freeze Haiku config, run suite, diff baseline" |
Apply the memory reflex the moment a finding is confirmed. Do not batch — capture at landing.
For procedural captures: pensyve_observe with episode_id: <session episode_id>, source_entity: "claude-code", about_entity: <relevant entity>, content: "[procedural] [proactive/in-flight/tier-1] trigger=..., action=..., outcome=...", content_type: "text".
Step 4: Capture open questions
When a run ends with an unresolved question ("X improved but we don't know why"), capture it as an episodic observation with open-question provenance:
pensyve_observe(
episode_id: <session episode_id>,
source_entity: "claude-code",
about_entity: <entity>,
content: "[proactive/in-flight/open-question] <question>",
content_type: "text"
)
This populates the "open questions" surface in the next session's primer.
Step 5: End-of-session summary
Before Stop fires, briefly summarize (inline, 3-5 lines): what this run taught us vs. prior runs, what's still open. This creates a natural handoff for the next session.
Constraints
- Recall on every topic shift — not just at session start.
- Capture at landing, not batch.
- Respect
max_auto_memories_per_session — in longitudinal work this can be raised; suggest the user consider 20-30 for heavy eval sessions.
- Never store run artifacts or large data blobs — summarize.
