Chroma Vector Database
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
Run MemMesh's competitive benchmark harness (LOCOMO / BEAM) to compare retrieval quality, tokens, latency, and cost against Mem0, Zep, full-context, and naive-RAG baselines.
Install Benchmark
$ npx skills add ThinkfleetAI/memmesh --skill benchmark -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ThinkfleetAI/memmesh benchmark --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ThinkfleetAI/memmesh.git skills-src && mkdir -p .claude/skills && cp -r skills-src/integrations/memmesh-plugin/skills/benchmark .claude/skills/benchmark && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "benchmark" agent skill from https://github.com/ThinkfleetAI/memmesh/tree/main/integrations/memmesh-plugin/skills/benchmark into .claude/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ThinkfleetAI/memmesh/tree/main/integrations/memmesh-plugin/skills/benchmarkType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ThinkfleetAI/memmesh --skill benchmark -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ThinkfleetAI/memmesh benchmark --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ThinkfleetAI/memmesh.git skills-src && mkdir -p .agents/skills && cp -r skills-src/integrations/memmesh-plugin/skills/benchmark .agents/skills/benchmark && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "benchmark" agent skill from https://github.com/ThinkfleetAI/memmesh/tree/main/integrations/memmesh-plugin/skills/benchmark into .agents/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ThinkfleetAI/memmesh --skill benchmark -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ThinkfleetAI/memmesh benchmark --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ThinkfleetAI/memmesh.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/integrations/memmesh-plugin/skills/benchmark .cursor/skills/benchmark && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "benchmark" agent skill from https://github.com/ThinkfleetAI/memmesh/tree/main/integrations/memmesh-plugin/skills/benchmark into .cursor/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ThinkfleetAI/memmesh.git --path integrations/memmesh-plugin/skills/benchmark--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ThinkfleetAI/memmesh --skill benchmark -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ThinkfleetAI/memmesh benchmark --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ThinkfleetAI/memmesh.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/integrations/memmesh-plugin/skills/benchmark .gemini/skills/benchmark && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "benchmark" agent skill from https://github.com/ThinkfleetAI/memmesh/tree/main/integrations/memmesh-plugin/skills/benchmark into .gemini/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ThinkfleetAI/memmesh benchmarkInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ThinkfleetAI/memmesh --skill benchmark -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ThinkfleetAI/memmesh.git skills-src && mkdir -p .github/skills && cp -r skills-src/integrations/memmesh-plugin/skills/benchmark .github/skills/benchmark && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "benchmark" agent skill from https://github.com/ThinkfleetAI/memmesh/tree/main/integrations/memmesh-plugin/skills/benchmark into .github/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ThinkfleetAI/memmesh --skill benchmark -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ThinkfleetAI/memmesh benchmark --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ThinkfleetAI/memmesh.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/integrations/memmesh-plugin/skills/benchmark .opencode/skills/benchmark && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "benchmark" agent skill from https://github.com/ThinkfleetAI/memmesh/tree/main/integrations/memmesh-plugin/skills/benchmark into .opencode/skills/benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "benchmark", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
benchmark- GitHub stars
- 419
- Token cost
- ~460 tokens
- SKILL.md length
- 189 words
- Files
- 1
- Skills in repo
- 24
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Run MemMesh's competitive benchmark harness (LOCOMO / BEAM) to compare retrieval quality, tokens, latency, and cost against Mem0, Zep, full-context, and naive-RAG baselines.
- The user wants proof MemMesh is better
- SKILL.md covers What it compares, Run it, Report honestly and Cost gating
- Calls python
- Is evaluating a migration
What it does
Benchmark is an agent skill from ThinkfleetAI/memmesh. Run MemMesh's competitive benchmark harness (LOCOMO / BEAM) to compare retrieval quality, tokens, latency, and cost against Mem0, Zep, full-context, and naive-RAG baselines. Use when the user wants proof MemMesh is better, is evaluating a migration, or asks "how does this compare to mem0".
Its SKILL.md is about 460 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Retrieval-augmented generation. It works with Mem0. The repository describes itself as: Persistent, self-improving memory for AI agents. Local-first Rust memory engine with MCP support. The licence is Apache-2.0.
When your agent uses it
- The user wants proof MemMesh is better
- Is evaluating a migration
- Asks how does this compare to mem0
Example prompts
- “how does this compare to mem0”
- “/benchmark”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit bba48f8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Benchmark loads about 460 tokens when it runs. Until then it costs about 75 tokens; SKILL.md has 189 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from ThinkfleetAI/memmesh at commit bba48f8, republished under its Apache-2.0 licence (© ThinkfleetAI). 189 words, ~460 tokens.
Download SKILL.mdSave it as
.claude/skills/benchmark/SKILL.md (or your agent's skills folder).- name
- benchmark
- description
- Run MemMesh's competitive benchmark harness (LOCOMO / BEAM) to compare retrieval quality, tokens, latency, and cost against Mem0, Zep, full-context, and naive-RAG baselines. Use when the user wants proof MemMesh is better, is evaluating a migration, or asks "how does this compare to mem0".
benchmark
Put numbers on the comparison. MemMesh ships a real benchmark harness that runs the public LOCOMO dataset end-to-end against competing systems.
What it compares
Systems: thinkfleet (MemMesh) vs full_context vs naive_rag, and — with keys
— Mem0 / Zep. Metrics: answer accuracy (rubric-scored), tokens consumed, latency,
and cost per conversation.
Run it
The harness lives in the engine repo at crates/eval/competitive/:
cd crates/eval/competitive
python bench.py --systems thinkfleet,mem0,full_context --dataset locomo
# results land in results/(Set the competitors' API keys via env for a head-to-head; without them you still get MemMesh vs full-context vs naive-RAG.)
Report honestly
MemMesh's positioning is calibration over raw accuracy — "80% means 80%" and honest abstention beat a slightly higher accuracy with overconfident wrong answers. So report the full picture:
- accuracy and calibration error,
- tokens / latency / cost (MemMesh's retrieval is far cheaper than full-context),
- where MemMesh abstained vs. where a competitor answered confidently and wrong.
Don't cherry-pick a single accuracy number. If a competitor wins on one axis, say so, and show where MemMesh's calibration/cost advantage pays off.
Cost gating
Prove the win on the cheap tiers (LOCOMO, BEAM-100K) before spending on BEAM-1M/10M — a single 10M-token conversation is expensive. Escalate tiers only once the cheaper tier shows a clear, defensible lead.
© ThinkfleetAI, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in integrations/memmesh-plugin/skills/benchmark of ThinkfleetAI/memmesh.
Open the folder on GitHubat commit bba48f8
Compare with similar skills
Benchmark next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Benchmark this skillThinkfleetAI/memmesh | 419 | — | ~460 | Automated safety check: Pass | Apache-2.0 | |
| Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~2.3k | Automated safety check: Pass | MIT | |
| LLM Application DevMoizIbnYousaf/ai-agent-skills | 1.1k | 2 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit | 259 | 4 repos | ~1.4k | Automated safety check: Pass | Custom licence | |
| MCP Local RAGshinpr/mcp-local-rag | 407 | — | ~4.4k | Automated safety check: Pass | MIT | |
| Ms Agent Framework RAGshuyu-labs/WebCode | 278 | — | ~1.1k | Automated safety check: Pass | Custom licence |
Similar skills
LLM Application Dev
MoizIbnYousaf/ai-agent-skills
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration.
Senior Prompt Engineer
maslennikov-ig/claude-code-orchestrator-kit
Provides reference guides and Python scripts for prompt optimization, RAG evaluation, and agent orchestration when building or tuning LLM systems.
MCP Local RAG
shinpr/mcp-local-rag
Searches, saves, and maintains a local document index through a local RAG MCP server.
Ms Agent Framework RAG
shuyu-labs/WebCode
Comprehensive guide for building Agentic RAG systems using Microsoft Agent Framework in C.
Local RAG Search
nkapila6/mcp-local-rag
Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking.
More from ThinkfleetAI/memmesh
All 24 skills in this repoBehaviors
ThinkfleetAI/memmesh
Surface emergent behavior patterns MemMesh has mined from a subject's history — recurring habits nobody predefined, each with prevalence, stability, and the evidence behind it.
Context Loader
ThinkfleetAI/memmesh
Load relevant MemMesh context before starting work — searches memory and, for a specific subject, assembles a token-budgeted bundle (profile + behavior patterns + forward predictions + top memories)…
Graph
ThinkfleetAI/memmesh
Query MemMesh's bi-temporal knowledge graph — multi-hop reasoning across entities, point-in-time "what did we believe on date X", and anticipatory retrieval via spreading activation.
Memmesh CLI
ThinkfleetAI/memmesh
MemMesh CLI + local MCP server — the zero-infra, no-API-key path to the same engine as the hosted SDK.
Memmesh SDK
ThinkfleetAI/memmesh
MemMesh TypeScript SDK reference (@thinkfleet/memory-sdk) for the hosted platform at app.memmesh.ai.
Predict
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Forecast what a subject will do next from their mined behavior patterns — with a calibrated, horizon-decayed confidence and provenance.
Related
Works with
Categories
Questions about Benchmark
What does Benchmark do?
Run MemMesh's competitive benchmark harness (LOCOMO / BEAM) to compare retrieval quality, tokens, latency, and cost against Mem0, Zep, full-context, and naive-RAG baselines. Benchmark is an agent skill from ThinkfleetAI/memmesh. Run MemMesh's competitive benchmark harness (LOCOMO / BEAM) to compare retrieval quality, tokens, latency, and cost against Mem0, Zep, full-context, and naive-RAG baselines.
When should I use Benchmark?
Benchmark fits situations like: the user wants proof MemMesh is better; is evaluating a migration; asks how does this compare to mem0.
How do I install Benchmark in Claude Code?
Run `npx skills add ThinkfleetAI/memmesh --skill benchmark -a claude-code`. Or copy the skill folder (integrations/memmesh-plugin/skills/benchmark in ThinkfleetAI/memmesh) into .claude/skills/benchmark in your project. Claude Code loads it when a task matches its description.
How do I install Benchmark in Codex?
Run `npx skills add ThinkfleetAI/memmesh --skill benchmark -a codex`. Or copy the skill folder (integrations/memmesh-plugin/skills/benchmark in ThinkfleetAI/memmesh) into .agents/skills/benchmark in your project. Codex loads it when a task matches its description.
Can I use Benchmark in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ThinkfleetAI/memmesh --skill benchmark -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/benchmark, .gemini/skills/benchmark, .github/skills/benchmark and .opencode/skills/benchmark in your project.
What does Benchmark need to run?
Going by SKILL.md and its folder, Benchmark needs the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Benchmark access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Benchmark safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Benchmark use?
Benchmark is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Benchmark use?
About 460 tokens (SKILL.md is roughly 1.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Benchmark?
Skills that share tags, products or a category with Benchmark: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), LLM Application Dev (MoizIbnYousaf/ai-agent-skills, 1.1k stars), Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 259 stars) and MCP Local RAG (shinpr/mcp-local-rag, 407 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Benchmark?
ThinkfleetAI (a GitHub organization) maintains it in ThinkfleetAI/memmesh, which has 419 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on August 25, 2026.
Source: ThinkfleetAI/memmesh on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.