How do I install the Midos Memory Cascade skill?

Run npx skillmds@latest add dvcrn/midos-memory-cascade in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Midos Memory Cascade safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Midos Memory Cascade?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Midos Memory Cascade free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Midos Memory Cascade?

dvcrn (@dvcrn) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Midos Memory Cascade by dvcrn — Agent Skill

Name: Midos Memory Cascade
Rating: 4.666666666666667 (3 reviews)
Author: dvcrn

File contents

MidOS Memory Cascade

A self-tuning, auto-escalating search engine that tries each memory tier from fastest to slowest, stopping as soon as it finds a high-confidence answer.

What It Does

Instead of the agent deciding which storage layer to query, the cascade tries each tier automatically:

Tier	Storage	Latency	Strategy
T0	In-memory session cache	<1ms	Exact + fuzzy key match
T1	JSON state files	<5ms	Filename + key match
T2	SQLite (pipeline_synergy.db)	<5ms	Structured SQL LIKE
T3	SQLite FTS5	<1ms	Full-text keyword on 22K rows
T4	Grep over 46K chunks	~3s	Brute-force ripgrep fallback
T5	LanceDB keyword (BM25)	slow	670K vector rows, no embeddings
T5b	LanceDB semantic	3–30s	Embedding similarity, last resort

Question routing: Queries starting with how/what/why/etc. skip keyword tiers and route directly to semantic search.

Self-learning: The cascade records which tier resolves each query. After enough history, evolve() learns shortcuts (skip directly to the winning tier) and marks consistently-empty tiers for skip.

Usage

Python API

from tools.memory.memory_cascade import recall, store

# Search across all tiers
result = recall("adaptive alpha reranking")
# → {"answer": {...}, "tier": "T5:lancedb", "latency_ms": 340, "confidence": 0.87}

# Write to the right storage automatically
store("pattern", content="...", tags=["ml", "reranking"])

CLI

# Search
python memory_cascade.py recall "query here"

# View tier resolution stats
python memory_cascade.py stats

# Run self-evolution (learn shortcuts + tier skips)
python memory_cascade.py evolve

`recall()` Options

recall(
    query: str,
    min_confidence: float = 0.5,  # stop escalating at this threshold
    max_tier: int = 6             # 0=T0 only, 6=all tiers
)

Returns:

{
  "answer": { "source": "...", "text": "..." },
  "confidence": 0.87,
  "latency_ms": 340.2,
  "tiers_tried": 3,
  "resolved_at": "T5:lancedb",
  "shortcut": null,
  "question_routed": false,
  "escalation": [...]
}

Requirements

Python 3.10+ (stdlib only for core cascade logic)
Optional: hive_commons for LanceDB tiers (T5/T5b)
Optional: tools.memory.memory_router for store() routing

The cascade degrades gracefully — if LanceDB is unavailable, it stops at grep (T4). All stdlib tiers (T0–T4) work with zero dependencies.

Architecture Notes

Thread-safe: Session cache uses threading.Lock; stats writes use separate locks
Cross-process safe: JSONL writes use OS-level file locking (msvcrt on Windows, fcntl on Unix)
Confidence scoring: Term overlap × score × content richness → normalized 0–1
Stats persistence: knowledge/SYSTEM/cascade_stats.json accumulates hit rates per tier

Built with MidOS. 1 of 200+ skills. Full ecosystem at midos.dev/pro

---name: midos-memory-cascadedescription: Auto-escalating multi-tier memory search that cascades from in-memory cache through SQLite, grep, and LanceDB vector search to find the best answer with minimal latency.---# MidOS Memory CascadeA self-tuning, auto-escalating search engine that tries each memory tier from fastest to slowest, stopping as soon as it finds a high-confidence answer.## What It DoesInstead of the agent deciding which storage layer to query, the cascade tries each tier automatically:| Tier | Storage | Latency | Strategy ||------|---------|---------|----------|| T0 | In-memory session cache | <1ms | Exact + fuzzy key match || T1 | JSON state files | <5ms | Filename + key match || T2 | SQLite (pipeline_synergy.db) | <5ms | Structured SQL LIKE || T3 | SQLite FTS5 | <1ms | Full-text keyword on 22K rows || T4 | Grep over 46K chunks | ~3s | Brute-force ripgrep fallback || T5 | LanceDB keyword (BM25) | slow | 670K vector rows, no embeddings || T5b | LanceDB semantic | 3–30s | Embedding similarity, last resort |**Question routing:** Queries starting with how/what/why/etc. skip keyword tiers and route directly to semantic search.**Self-learning:** The cascade records which tier resolves each query. After enough history, `evolve()` learns shortcuts (skip directly to the winning tier) and marks consistently-empty tiers for skip.## Usage### Python API```pythonfrom tools.memory.memory_cascade import recall, store# Search across all tiersresult = recall("adaptive alpha reranking")# → {"answer": {...}, "tier": "T5:lancedb", "latency_ms": 340, "confidence": 0.87}# Write to the right storage automaticallystore("pattern", content="...", tags=["ml", "reranking"])```### CLI```bash# Searchpython memory_cascade.py recall "query here"# View tier resolution statspython memory_cascade.py stats# Run self-evolution (learn shortcuts + tier skips)python memory_cascade.py evolve```### `recall()` Options```pythonrecall( query: str, min_confidence: float = 0.5, # stop escalating at this threshold max_tier: int = 6 # 0=T0 only, 6=all tiers)```Returns:```json{ "answer": { "source": "...", "text": "..." }, "confidence": 0.87, "latency_ms": 340.2, "tiers_tried": 3, "resolved_at": "T5:lancedb", "shortcut": null, "question_routed": false, "escalation": [...]}```## Requirements- Python 3.10+ (stdlib only for core cascade logic)- Optional: `hive_commons` for LanceDB tiers (T5/T5b)- Optional: `tools.memory.memory_router` for `store()` routingThe cascade degrades gracefully — if LanceDB is unavailable, it stops at grep (T4). All stdlib tiers (T0–T4) work with zero dependencies.## Architecture Notes- **Thread-safe:** Session cache uses `threading.Lock`; stats writes use separate locks- **Cross-process safe:** JSONL writes use OS-level file locking (`msvcrt` on Windows, `fcntl` on Unix)- **Confidence scoring:** Term overlap × score × content richness → normalized 0–1- **Stats persistence:** `knowledge/SYSTEM/cascade_stats.json` accumulates hit rates per tierBuilt with MidOS. 1 of 200+ skills. Full ecosystem at midos.dev/pro

Frequently asked questions

Midos Memory Cascade