MemPalace Memory Search
MemPalace/mempalace
Mines project files and conversation exports into a local, searchable memory palace and recalls past work by semantic search through the mempalace CLI.
This skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences…
$ npx skills add benchflow-ai/skillsbench --skill lean4-memories -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench lean4-memories --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/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/lean4-proof/environment/skills/lean4-memories .claude/skills/lean4-memories && 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 "lean4-memories" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lean4-proof/environment/skills/lean4-memories into .claude/skills/lean4-memories/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean4-memories", 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/benchflow-ai/skillsbench/tree/main/tasks/lean4-proof/environment/skills/lean4-memoriesType 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 benchflow-ai/skillsbench --skill lean4-memories -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench lean4-memories --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/lean4-proof/environment/skills/lean4-memories .agents/skills/lean4-memories && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "lean4-memories" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lean4-proof/environment/skills/lean4-memories into .agents/skills/lean4-memories/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean4-memories", 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 benchflow-ai/skillsbench --skill lean4-memories -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench lean4-memories --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/lean4-proof/environment/skills/lean4-memories .cursor/skills/lean4-memories && 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 "lean4-memories" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lean4-proof/environment/skills/lean4-memories into .cursor/skills/lean4-memories/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean4-memories", 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/benchflow-ai/skillsbench.git --path tasks/lean4-proof/environment/skills/lean4-memories--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 benchflow-ai/skillsbench --skill lean4-memories -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench lean4-memories --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/lean4-proof/environment/skills/lean4-memories .gemini/skills/lean4-memories && 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 "lean4-memories" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lean4-proof/environment/skills/lean4-memories into .gemini/skills/lean4-memories/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean4-memories", 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 benchflow-ai/skillsbench lean4-memoriesInstalls 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 benchflow-ai/skillsbench --skill lean4-memories -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/lean4-proof/environment/skills/lean4-memories .github/skills/lean4-memories && 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 "lean4-memories" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lean4-proof/environment/skills/lean4-memories into .github/skills/lean4-memories/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean4-memories", 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 benchflow-ai/skillsbench --skill lean4-memories -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install benchflow-ai/skillsbench lean4-memories --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/lean4-proof/environment/skills/lean4-memories .opencode/skills/lean4-memories && 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 "lean4-memories" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lean4-proof/environment/skills/lean4-memories into .opencode/skills/lean4-memories/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lean4-memories", 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.
lean4-memoriesThis skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences…
Lean4 Memories is an agent skill from benchflow-ai/skillsbench. This skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences across sessions using MCP memory server integration
Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts and reference files (for example `references/memory-patterns.md` and `scripts/memory_helper.py`).
It sits in Agent Workflows, covering Agent memory. It works with Model Context Protocol. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
modelcontextprotocol.ioFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Lean4 Memories loads about 3.2k tokens when it runs, and up to ~7.6k if it reads all its reference files. Until then it costs about 65 tokens; SKILL.md has 934 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.
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); the scripts in this folder are not scanned.
The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 934 words, ~3,187 tokens.
.claude/skills/lean4-memories/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.This skill enables persistent learning and knowledge accumulation across Lean 4 formalization sessions by leveraging MCP (Model Context Protocol) memory servers. It transforms stateless proof assistance into a learning system that remembers successful patterns, avoids known dead-ends, and adapts to project-specific conventions.
Core principle: Learn from each proof session and apply accumulated knowledge to accelerate future work.
This skill applies when working on Lean 4 formalization projects, especially:
Especially important when:
All memories are scoped by:
lean4-memoriesExample scoping:
Project: /Users/freer/work/exch-repos/exchangeability-cursor
Skill: lean4-memories
Entity: ProofPattern:condExp_unique_pattern1. ProofPattern - Successful proof strategies
Store when: Proof completes successfully after exploration
Retrieve when: Similar goal pattern detected2. FailedApproach - Known dead-ends to avoid
Store when: Approach attempted but failed/looped/errored
Retrieve when: About to try similar approach3. ProjectConvention - Code style and patterns
Store when: Consistent pattern observed (naming, structure, tactics)
Retrieve when: Creating new definitions/theorems4. UserPreference - Workflow customization
Store when: User expresses preference (verbose output, specific tools, etc.)
Retrieve when: Choosing between options5. TheoremDependency - Relationships between theorems
Store when: One theorem proves useful for proving another
Retrieve when: Looking for helper lemmasAfter successful proof:
-- Just proved: exchangeable_iff_fullyExchangeable
-- Store the successful patternStore:
exchangeable X ↔ fullyExchangeable X[apply measure_eq_of_fin_marginals_eq, intro, simp][prefixCylinder_measurable, isPiSystem_prefixCylinders]After failed approach:
-- Attempted: simp only [condExp_indicator, mul_comm]
-- Result: infinite loop, build timeoutStore:
simp only [condExp_indicator, mul_comm]Project conventions observed:
-- Pattern: All measure theory proofs start with haveI
haveI : MeasurableSpace Ω := inferInstanceStore:
haveI : MeasurableSpace ΩStarting new proof session:
Encountering similar goal:
⊢ condExp μ m X =ᵐ[μ] condExp μ m Y
Memory retrieved: "Similar goals proved using condExp_unique"
Pattern: "Show ae_eq, verify measurability, apply condExp_unique"
Success rate: 3/3 in this projectBefore trying a tactic:
About to: simp only [condExp_indicator, mul_comm]
Memory retrieved: ⚠️ WARNING - This combination causes infinite loop
Failed in: ViaL2.lean:2830 (2025-10-17)
Alternative: Use simp only [condExp_indicator], then ringThe lean4-memories skill complements (doesn't replace) lean4-theorem-proving:
lean4-theorem-proving provides:
lean4-memories adds:
Use together:
After completing a proof, store the pattern using MCP memory:
What to capture:
When to store:
Storage format:
Entity type: ProofPattern
Name: {descriptive_name}
Attributes:
- project: {absolute_path}
- goal_pattern: {pattern_description}
- tactics: [list, of, tactics]
- helper_lemmas: [lemma1, lemma2]
- difficulty: {small|medium|large}
- confidence: {0.0-1.0}
- file: {filename}
- timestamp: {date}When an approach fails (error, loop, timeout), store to avoid repeating:
What to capture:
When to store:
Storage format:
Entity type: FailedApproach
Name: {descriptive_name}
Attributes:
- project: {absolute_path}
- failed_tactic: {tactic_text}
- error: {error_description}
- context: {what_was_being_proved}
- alternative: {what_worked}
- timestamp: {date}Track consistent patterns that emerge:
What to capture:
When to store:
Before starting proof:
1. Query for similar goal patterns
2. Surface successful tactics for this pattern
3. Check for known issues with current context
4. Suggest helper lemmas from similar proofsDuring proof:
1. Before each major tactic, check for known failures
2. When stuck, retrieve alternative approaches
3. Suggest next tactics based on past successQuery patterns:
# Find similar proofs
search_entities(
query="condExp equality goal",
filters={"project": current_project, "entity_type": "ProofPattern"}
)
# Check for failures
search_entities(
query="simp only condExp_indicator",
filters={"project": current_project, "entity_type": "FailedApproach"}
)
# Get conventions
search_entities(
query="naming conventions measure theory",
filters={"project": current_project, "entity_type": "ProjectConvention"}
)DO store:
DON'T store:
Confidence scoring:
Aging:
Pruning:
Users can:
Session 1: First proof
-- Proving: measure_eq_of_fin_marginals_eq
-- No memories yet, explore from scratch
-- [After 30 minutes of exploration]
-- ✅ Success with π-system uniqueness approach
Store: ProofPattern "pi_system_uniqueness"
- Works for: measure equality via finite marginals
- Tactics: [isPiSystem, generateFrom_eq, measure_eq_on_piSystem]
- Confidence: 0.9Session 2: Similar theorem (weeks later)
-- Proving: fullyExchangeable_via_pathLaw
-- Goal: Show two measures equal
-- System: "Similar to measure_eq_of_fin_marginals_eq"
-- Retrieve memory: pi_system_uniqueness pattern
-- Suggestion: "Try isPiSystem approach?"
-- ✅ Success in 5 minutes using remembered patternSession 3: Avoiding failure
-- Proving: condIndep_of_condExp_eq
-- About to: simp only [condExp_indicator, mul_comm]
-- ⚠️ Memory: This causes infinite loop (stored Session 1)
-- Alternative: simp only [condExp_indicator], then ring
-- Avoid 20-minute debugging session by using memoryEnsure MCP memory server is configured:
// In Claude Desktop config
{
"mcpServers": {
"memory": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-memory"]
}
}
}Memories are automatically scoped by project path. To work across multiple projects:
Same formalization, different repos:
# Link memories using project aliases
# (Future enhancement - not yet implemented)Sharing memories with team:
# Export/import functionality
# (Future enhancement - not yet implemented)Memories enhance script usage:
proof_templates.sh:
suggest_tactics.sh:
sorry_analyzer.py:
What memories DON'T replace:
Potential issues:
Mitigation:
Planned features:
© benchflow-ai, 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
SKILL.md and 2 other files (scripts, references) in tasks/lean4-proof/environment/skills/lean4-memories of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Lean4 Memories 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 |
|---|---|---|---|---|---|---|
| Lean4 Memories this skillbenchflow-ai/skillsbench | 1.8k | — | ~3.2k | Automated safety check: Pass | Apache-2.0 | |
| MemPalace Memory SearchMemPalace/mempalace | 59k | — | ~1.4k | Automated safety check: Pass | MIT | |
| MemPalace Setup and OperationMemPalace/mempalace | 59k | — | ~2.2k | Automated safety check: Pass | MIT | |
| agentmemory Setup and Diagnosticsrohitg00/agentmemory | 29k | — | ~1k | Automated safety check: Notes | Apache-2.0 | |
| Claude-Mem Install for Grok Botthedotmack/claude-mem | 99k | — | ~440 | Automated safety check: Pass | Apache-2.0 | |
| Qmdbreferrari/obsidian-mind | 5k | — | ~1.7k | Automated safety check: Pass | MIT |
MemPalace/mempalace
Mines project files and conversation exports into a local, searchable memory palace and recalls past work by semantic search through the mempalace CLI.
MemPalace/mempalace
Installs and configures MemPalace as a private local palace, a shared-brain hub or a client of an existing hub, including MCP registration and version-correct initialization.
rohitg00/agentmemory
Sets up and troubleshoots a local agentmemory install, covering the MCP connection, environment variables, ports, authentication and optional feature flags.
thedotmack/claude-mem
Use this when setting up claude-mem on Grok Bot: local worker plus CMEM Pro observer (default), optional host-login observer, or remote cmem.ai. No Cursor…
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Works with
Categories
This skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences…. Lean4 Memories is an agent skill from benchflow-ai/skillsbench.
Lean4 Memories fits situations like: tasks that involve Agent memory.
Run `npx skills add benchflow-ai/skillsbench --skill lean4-memories -a claude-code`. Or copy the skill folder (tasks/lean4-proof/environment/skills/lean4-memories in benchflow-ai/skillsbench) into .claude/skills/lean4-memories in your project. Claude Code loads it when a task matches its description.
Run `npx skills add benchflow-ai/skillsbench --skill lean4-memories -a codex`. Or copy the skill folder (tasks/lean4-proof/environment/skills/lean4-memories in benchflow-ai/skillsbench) into .agents/skills/lean4-memories in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill lean4-memories -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lean4-memories, .gemini/skills/lean4-memories, .github/skills/lean4-memories and .opencode/skills/lean4-memories in your project.
Going by SKILL.md and its folder, Lean4 Memories needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md names 1 domain. As links in the text: modelcontextprotocol.io. This is read from the text; nothing was executed.
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Lean4 Memories 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.
About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.4k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Lean4 Memories: MemPalace Memory Search (MemPalace/mempalace, 59k stars), MemPalace Setup and Operation (MemPalace/mempalace, 59k stars), agentmemory Setup and Diagnostics (rohitg00/agentmemory, 29k stars) and Claude-Mem Install for Grok Bot (thedotmack/claude-mem, 99k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,835 GitHub stars. The repository holds 189 skills in this directory. The repository was last updated on July 23, 2026.
Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.