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Get Started Free →Three-tier agent memory model (hot/cold/wiki) for context reduction per spawn
.claude/skills/github-tiered-memory/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 119% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 121% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 30% | 0% |
> Status (v0.10.0): This skill describes a design proposal, not a shipped runtime. Skill files install via squad init/upgrade, but the underlying tier scaffolding (.squad/memory/hot/, cold/, wiki/), Scribe promotion logic, and spawn-template tier-aware reads are tracked in bradygaster/squad#1264. Until those land, agents continue to load full history.md + decisions.md on every spawn.
Squad agents today load their full context history on every spawn, which grows unboundedly across sessions. The Tiered Agent Memory model proposes a three-tier separation so agents only load the bytes that are actually relevant to the current task, with older context kept available on demand.
--include-cold in spawn template or add ## Cold Memory section.--include-wiki or reference specific wiki doc paths in spawn template.| Situation | Hot | Cold | Wiki | |-----------|-----|------|------| | New task, no prior context needed | ✅ | ❌ | ❌ | | Resuming interrupted work | ✅ | ✅ | ❌ | | Debugging a recurring issue | ✅ | ✅ | ❌ | | Implementing against a spec/ADR | ✅ | ❌ | ✅ | | Onboarding to unfamiliar subsystem | ✅ | ❌ | ✅ | | Post-incident review | ✅ | ✅ | ✅ |
The default spawn prompt should include Hot tier only:
## Memory Context
### Hot (current session)
{hot_context}Add --include-cold when the task needs history:
## Memory Context
### Hot (current session)
{hot_context}
### Cold (summarized history — load on demand)
See: .squad/memory/cold/{agent-name}.mdAdd --include-wiki when the task needs domain knowledge:
## Memory Context
### Hot (current session)
{hot_context}
### Wiki (durable reference)
See: .squad/memory/wiki/{topic}.mdScribe is the proposed memory coordinator for this system. Once the runtime lands, Scribe will:
Until then, see the Scribe charter for current behavior: .squad/agents/scribe/charter.md
.squad/memory/hot/{agent}.md)--include-cold / --include-wiki flags as needed.squad/memory/wiki/.squad/memory/cold/ with rolling TTLUse this template when spawning any Squad agent. By default it loads Hot tier only. Add optional sections as needed.
{task_description}
{why_this_matters}
> Paste current session context here (~2–4KB target):
Current task: {task_description}
Active decisions: {decisions_this_session}
Last actions: {last_3_to_5_actions}
Blockers: {current_blockers_or_none}
Talking to: {current_interlocutor}--include-cold)> Load on demand. Do not inline unless specifically needed.
Summarized cross-session history is at: .squad/memory/cold/{agent-name}.md
Include when:
To load cold memory, add this section and fetch the file before spawning:
## Cold Memory Summary
{contents_of_.squad/memory/cold/{agent-name}.md}--include-wiki)> Load on demand. Reference specific wiki docs by path.
Wiki entries are at: .squad/memory/wiki/
Include when:
To load wiki, add this section and reference the specific doc:
## Wiki Reference
{contents_of_.squad/memory/wiki/{topic}.md}If blocked or uncertain:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 41,983 | 11,925 | -72% | 1 | 1 | 0% | 2,556 | 3,681 | +44% | 0 | 0 | — |
case-02 | fail→pass | 35,502 | 13,417 | -62% | 1 | 1 | 0% | 3,054 | 3,916 | +28% | 0 | 0 | — |
case-03 | fail→pass | 24,481 | 13,390 | -45% | 1 | 1 | 0% | 4,233 | 3,907 | -8% | 0 | 0 | — |
case-04 | fail→fail | 47,602 | 24,043 | -49% | 1 | 1 | 0% | 3,995 | 6,321 | +58% | 0 | 0 | — |
case-05 | fail→fail | 17,257 | 29,020 | +68% | 1 | 1 | 0% | 3,716 | 5,300 | +43% | 0 | 0 | — |
case-06 | fail→fail | 21,155 | 34,710 | +64% | 1 | 1 | 0% | 3,275 | 4,226 | +29% | 0 | 0 | — |
case-07 | fail→fail | 15,617 | 14,639 | -6% | 1 | 1 | 0% | 2,320 | 4,079 | +76% | 0 | 0 | — |
case-08 | pass→pass | 11,718 | 5,010 | -57% | 1 | 1 | 0% | 1,812 | 2,475 | +37% | 0 | 0 | — |
case-09 | fail→pass | 36,924 | 9,184 | -75% | 1 | 1 | 0% | 1,527 | 3,349 | +119% | 0 | 0 | — |
case-10 | fail→pass | 20,237 | 7,828 | -61% | 1 | 1 | 0% | 1,311 | 2,902 | +121% | 0 | 0 | — |
case-11 | fail→pass | 25,505 | 4,937 | -81% | 1 | 1 | 0% | 1,846 | 2,403 | +30% | 0 | 0 | — |
case-12 | fail→pass | 11,138 | 3,904 | -65% | 1 | 1 | 0% | 1,758 | 2,339 | +33% | 0 | 0 | — |
case-13 | fail→pass | 24,038 | 5,495 | -77% | 1 | 1 | 0% | 1,633 | 2,579 | +58% | 0 | 0 | — |
case-14 | fail→pass | 7,092 | 4,564 | -36% | 1 | 1 | 0% | 974 | 2,218 | +128% | 0 | 0 | — |
case-15 | pass→pass | 12,108 | 3,404 | -72% | 1 | 1 | 0% | 1,719 | 2,143 | +25% | 0 | 0 | — |
case-16 | fail→pass | 18,946 | 8,163 | -57% | 1 | 1 | 0% | 1,940 | 3,085 | +59% | 0 | 0 | — |
case-17 | fail→pass | 8,024 | 5,987 | -25% | 1 | 1 | 0% | 1,285 | 2,456 | +91% | 0 | 0 | — |
case-18 | pass→pass | 9,597 | 4,700 | -51% | 1 | 1 | 0% | 1,170 | 2,585 | +121% | 0 | 0 | — |
case-19 | fail→pass | 8,200 | 2,977 | -64% | 1 | 1 | 0% | 1,167 | 2,210 | +89% | 0 | 0 | — |
case-20 | fail→pass | 21,790 | 3,751 | -83% | 1 | 1 | 0% | 2,634 | 2,297 | -13% | 0 | 0 | — |
case-21 | fail→pass | 21,526 | 6,483 | -70% | 1 | 1 | 0% | 944 | 3,012 | +219% | 0 | 0 | — |
case-22 | fail→pass | 13,249 | 5,093 | -62% | 1 | 1 | 0% | 1,752 | 2,676 | +53% | 0 | 0 | — |
case-23 | fail→pass | 25,397 | 4,009 | -84% | 1 | 1 | 0% | 2,180 | 2,381 | +9% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 21 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +65 percentage points is the difference between those two pass rates over the 21 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.