Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add strikersam/autonomous-ai-agency --skill memory-consolidationgit clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/strikersam/autonomous-ai-agency/memory-consolidation)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/memory-consolidation"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/memory-consolidation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/memory-consolidation"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/memory-consolidation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.00386 |
| Opus 5 | $0.00012 | $0.00193 |
| Sonnet 5 | $0.00005 | $0.00077 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
memory-consolidation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Skill: memory-consolidation (Dream Memory)
Purpose
Inspired by hippocampal replay: long-running AI systems accumulate session artifacts, and periodically consolidating them into structured memories improves future recall and context reuse.
Consolidation Lifecycle
COLLECTING → DREAMING → CONSOLIDATED
Memory Kinds
SESSION_NOTE— General session observationsLEARNED_RULE— Patterns/corrections to persistBUG_PATTERN— Recurring bug signaturesARCHITECTURAL_DECISION— ADR-like recordsCODE_SNIPPET— Reusable code fragments
Quick Start
from agents.memory_consolidation import DreamMemory, MemoryKind, PatternConsolidation
pc = PatternConsolidation()
pc.add_memory(DreamMemory("m1", MemoryKind.BUG_PATTERN, "null deref in auth", tags=["bug", "auth"]))
pc.add_memory(DreamMemory("m2", MemoryKind.BUG_PATTERN, "timeout on login", tags=["bug", "auth"]))
result = pc.consolidate()
print(result) # clusters found, memories consolidated
Testing
pytest tests/test_memory_consolidation.py -v
Branch
fix/quick-note-259-memory-dreams
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 59 lines · 24 tokens per session scan A 64a691c44917
memory-consolidation is a skill published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 386 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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