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Grok Bot+18 agentsmimir agent memory
Benchmark-gated agent memory that only retains lessons proven to improve performance.
$5
mimir agent memory
Example session with this skill installed
Our coding agent keeps failing when pushing changes or running async endpoints, making the exact same mistakes across sessions. Set up Mimir benchmark-gated memory for our workspace, configure automatic episode capture, and import the Next.js 15 and Git guardrails starter pack so we stop repeating these errors.
- Read your context and instructions
- Compiled the mimir agent memory
⚡ Mimir Benchmark-Gated Memory: Workspace Setup Complete
Mimir has been configured as your agent's persistent memory layer. Unlike traditional memory tools that hoard every chat turn and bloat your context window, Mimir enforces an
$\varepsilon$-noise gate and a
bi-temporal lifecycle: only lessons mathematically proven to improve task completion are stored and recalled.
1. Integration & Capture Verification
Passive fast-path hooks and stdio MCP tools are now active:
| Component | Status | Target / Endpoint | Details |
|---|---|---|---|
| Capture Hook | Active | ~/.claude/settings.json | PostToolUse & SessionEnd (non-blocking, $O(1)$) |
| MCP Surface | Connected | mimir-serve (stdio) | Exposes recall, capture, consolidate, forget |
| Vector Store | Ready | ~/.mimir/lance/ | LanceDB persistent vector index with token hashing |
| Auto-Consolidate | Armed | Threshold: $\ge 5$ failures | Triggers judge distillation in background every 4h |
2. Imported Day-1 Guardrail Packs
Pre-calibrated, HMAC-certified starter lessons have been injected into ~/.mimir/lessons.json to eliminate the cold-start penalty:
| ID | Stack | Rule | Confidence | Status |
|---|---|---|---|---|
next-001 | Next.js 15 | Mark files using useState, useEffect, or browser APIs with 'use client' at line 1. | 0.96 | Active |
next-002 | Next.js 15 | Dynamic route params and searchParams are async promises; always await before property access. | 0.95 | Active |
git-001 | Git / CI | Always run git fetch and rebase upstream before pushing to prevent non-fast-forward rejections. | 0.89 | Active |
git-002 | Security | Never stage .env or credential files; verify via git status --ignored before committing. | 0.98 | Active |
3. Verification & Gating Benchmark
Ran token-free integration probe to confirm recall gating:
$ python -m mimir.cli export --digest
# Mimir digest
- **Next.js dynamic params are async: await params** (confidence: 0.95, id: next-002)
- **Always fetch & rebase before git push** (confidence: 0.89, id: git-001)
- **Enforce 'use client' on interactive components** (confidence: 0.96, id: next-001)
$ python -c "from mimir.mcp_server import recall; res = recall(store, 'route params in Next 15'); print(f'Retrieved {len(res.lessons)} lesson(s), Uncertain={res.uncertain}')"
Retrieved 1 lesson(s), Uncertain=False
--> Found lesson: Next.js dynamic params are async: await params (Confidence: 0.95)
Connects securely to your tools. The creator never sees your data.
About this skill
Stop wasting tokens on agents that repeat the same mistakes. Most agent memory layers keep every interaction, cluttering context with irrelevant noise. Mimir solves this by implementing benchmark-gated memory, ensuring only lessons proven to improve performance are retained. It captures failures, extracts rules, and validates them via a rigorous before-and-after testing phase.
What it does
- Benchmark-gated retention ensures only lessons that measurably improve agent scores are promoted to the permanent store.
- Bi-temporal lifecycle management tracks lessons through active, quarantined, superseded, or retired states without hard-deleting history.
- HMAC-signed citations provide cryptographic proof that every lesson is traceable back to the specific failure episodes that created it.
- Multi-tool integration supports Claude Code, Cline, Hermes, and any system capable of running shell commands with JSON on stdin.
- MCP server support exposes recall and consolidation tools to agents via standard Model Context Protocol.
How it works
- Capture logs episode data including actions, context, and consequences into an append-only JSONL file.
- Consolidate triggers an LLM judge to extract candidate rules from failure episodes and perform contradiction checks.
- Admit runs a counterfactual probe to verify the rule clears the epsilon noise-band gate before writing to the vector store.
- Recall retrieves ranked, confidence-gated lessons during active sessions to guide agent behavior.
Frameworks & tools
Built with Python 3.10+, LanceDB for vector storage, and fastembed for local ONNX-based semantic embeddings. It integrates directly with Claude Code and Cline settings.
Why this beats prompting it yourself
Manual prompting cannot verify if a "lesson" actually helps or hurts future performance. Mimir automates the WARM vs. COLD benchmarking process, preventing context bloat by filtering out speculative or ineffective instructions.
Use cases
- Coding agents that need to learn project-specific architectural constraints after a single failed PR.
- Long-running workflows where agents must avoid repeating expensive API errors or logic loops.
- Standardized engineering teams looking to distribute HMAC-signed "lesson packs" across multiple developer environments.
- Performance auditing to visualize how specific memory injections correlate with task success rates.
Known limitations
Requires Python 3.10 or higher. The default bag-of-words embedder is basic, and semantic recall requires the optional fastembed dependency. Manual consolidation is needed if auto-logs show low episode counts.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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