name	engram-session-promote
description	End-of-session sparse promotion — propose at most 5 high-signal Decisions/Preferences/People for Engram remember(), never dump recaps.
version	0.1.0
user-invocable	true

Engram Session Promote (sparse)

Run this at session end, before context compaction, or when the user says "promote what matters" / "save durable decisions."

Product metric is multi-agent continuity — not exhaustively storing the chat.

Hard rules

Cap: 0–5 remember() calls per compaction window (not multi-day session).
Prefer 0 when nothing is durable. Silence is fine.
Never promote session recaps ("what we did today", transcript dumps).
Types only: Decision, Preference, Person, Correction, Goal, Commitment.
You are the extractor: always pass proposed_entities + proposed_relationships with a verbatim source_span that appears in content.
Pass model_tier (opus/sonnet/haiku). After PreCompact, pass compaction_id if the harness exposed one (or rely on ~/.engram/promotion-window.json).

Procedure

Scan this conversation for portable facts that another agent would need cold (without a handoff doc).
Rank by durability:
- Identity / explicit preferences / corrections
- Product decisions with lasting consequences
- Named people the user cares about
- Active goals / commitments with owners
Keep the top ≤5. Drop noise, temporary task status, and file paths that are only true inside this repo checkout.
For each kept fact, call one remember() with short content that contains the span:

remember(
  content="<1–3 sentences including the exact decision text>",
  model_tier="sonnet",
  proposed_entities=[{
    "name": "<atomic decision or person name>",
    "entity_type": "Decision|Preference|Person|Correction|Goal|Commitment",
    "source_span": "<verbatim substring of content>",
    "summary": "<one-line why it matters later>"
  }],
  proposed_relationships=[{
    "subject": "<entity or project>",
    "predicate": "DECIDED|PREFERS|RELATED_TO|CORRECTS|COMMITS_TO",
    "object": "<other entity or decision name>",
    "source_span": "<verbatim substring of content>"
  }]
)

Read the response:
- committed_entities / committed_relationships → promotion landed
- identity_core: true → protected for merge/prune
- status: rejected + promotion_window_cap → stop; remaining facts wait for the next compaction window
Optionally call get_context once and confirm ≥1 promoted Decision surfaces.
Tell the user briefly: N promoted / M skipped (no essay).

What not to promote

Long chat summaries or "session notes"
Transient bugs already fixed in-repo
Raw log lines, stack traces, or secrets
Cadence / work-ticket noise unless the user explicitly wants it portable
More than five items "just in case"

When uncertain

Use observe() for bulk/uncertain context, or skip.
If uncertain whether it is durable — do not remember it.

Golden loop: docs/GOLDEN_LOOP.md
Main memory skill: skills/engram-memory/SKILL.md
PreCompact window reset: hooks/pre-compact.sh

Engram Session Promote (sparse)

Run this at session end, before context compaction, or when the user says "promote what matters" / "save durable decisions."

Product metric is multi-agent continuity — not exhaustively storing the chat.

Hard rules

Cap: 0–5 remember() calls per compaction window (not multi-day session).

Prefer 0 when nothing is durable. Silence is fine.

Never promote session recaps ("what we did today", transcript dumps).

Types only: Decision, Preference, Person, Correction, Goal, Commitment.

You are the extractor: always pass proposed_entities + proposed_relationships with a verbatim source_span that appears in content.

Pass model_tier (opus/sonnet/haiku). After PreCompact, pass compaction_id if the harness exposed one (or rely on ~/.engram/promotion-window.json).

Procedure

Scan this conversation for portable facts that another agent would need cold (without a handoff doc).

Rank by durability:

Identity / explicit preferences / corrections
Product decisions with lasting consequences
Named people the user cares about
Active goals / commitments with owners

Keep the top ≤5. Drop noise, temporary task status, and file paths that are only true inside this repo checkout.

For each kept fact, call one remember() with short content that contains the span:

remember( content="<1–3 sentences including the exact decision text>", model_tier="sonnet", proposed_entities=[{ "name": "<atomic decision or person name>", "entity_type": "Decision|Preference|Person|Correction|Goal|Commitment", "source_span": "<verbatim substring of content>", "summary": "<one-line why it matters later>" }], proposed_relationships=[{ "subject": "<entity or project>", "predicate": "DECIDED|PREFERS|RELATED_TO|CORRECTS|COMMITS_TO", "object": "<other entity or decision name>", "source_span": "<verbatim substring of content>" }] )

Read the response:

committed_entities / committed_relationships → promotion landed
identity_core: true → protected for merge/prune
status: rejected + promotion_window_cap → stop; remaining facts wait for the next compaction window

Optionally call get_context once and confirm ≥1 promoted Decision surfaces.

Tell the user briefly: N promoted / M skipped (no essay).

What not to promote

Long chat summaries or "session notes"

Transient bugs already fixed in-repo

Raw log lines, stack traces, or secrets

Cadence / work-ticket noise unless the user explicitly wants it portable

More than five items "just in case"

When uncertain

Use observe() for bulk/uncertain context, or skip.
If uncertain whether it is durable — do not remember it.

Golden loop: docs/GOLDEN_LOOP.md

Main memory skill: skills/engram-memory/SKILL.md

PreCompact window reset: hooks/pre-compact.sh

engram-session-promote