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Agent skill

Pin

by ThinkfleetAI in ThinkfleetAI/memmesh

Protect a critical MemMesh memory from consolidation/pruning by raising its importance and marking it high-impact — or unpin to release it.

Apache-2.0Auto-check passed

Install Pin

skills CLI
$ npx skills add ThinkfleetAI/memmesh --skill pin -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ThinkfleetAI/memmesh pin --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ThinkfleetAI/memmesh.git skills-src && mkdir -p .claude/skills && cp -r skills-src/integrations/memmesh-plugin/skills/pin .claude/skills/pin && rm -rf skills-src

Use ~/.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/

Facts

Skill name
pin
GitHub stars
419
Token cost
~370 tokens
SKILL.md length
122 words
Files
1
Skills in repo
24
Repo updated
First seen
Licence
Apache-2.0

At a glance

Protect a critical MemMesh memory from consolidation/pruning by raising its importance and marking it high-impact — or unpin to release it.

  • Works in 2 steps: Find the item (memory_search) and read… → Re-assert it at maximum durability with…
  • Architecture decisions
  • SKILL.md covers Pin, Unpin and When to pin
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Pin is an agent skill from ThinkfleetAI/memmesh. Protect a critical MemMesh memory from consolidation/pruning by raising its importance and marking it high-impact — or unpin to release it. Use for architecture decisions, security constraints, or immutable team conventions that must never be retired by a dream pass.

Its SKILL.md is about 370 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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

  • Architecture decisions
  • Security constraints
  • Immutable team conventions that must never be retired by a dream pass

Example prompts

  • “/pin”

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Find the item (memory_search) and read it (memory_recall).
  2. Re-assert it at maximum durability with memory_save (upsert on the same id)

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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are jsonc).

    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

Pin loads about 370 tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 122 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~370

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). 122 words, ~370 tokens.

Download SKILL.mdSave it as .claude/skills/pin/SKILL.md (or your agent's skills folder).
name
pin
description
Protect a critical MemMesh memory from consolidation/pruning by raising its importance and marking it high-impact — or unpin to release it. Use for architecture decisions, security constraints, or immutable team conventions that must never be retired by a `dream` pass.

pin

Mark a memory as protected. MemMesh has no separate "pinned" flag — pinning is expressed through importance + impact + confirmation, which the dream consolidation pass treats as do-not-retire.

Pin

  1. Find the item (memory_search) and read it (memory_recall).
  2. Re-assert it at maximum durability with memory_save (upsert on the same id):
jsonc
{ "name": "memory_save",
  "arguments": { "id": "<same id>", "platformId": "local", "scope": "project",
                 "type": "rule", "content": "<verbatim content>",
                 "importance": 10, "metadata": { "pinned": true, "impact": "HIGH" } } }

Setting importance: 10 and metadata.pinned: true is the signal the dream skill checks before deleting/superseding anything.

Unpin

Re-save the same id with importance back to a normal value (≈5) and metadata.pinned: false. The item stays in memory but becomes eligible for consolidation again.

When to pin

Architecture decisions, security constraints, compliance rules, immutable conventions — anything where losing it silently would cause real harm. Don't pin routine preferences; let the engine manage those.

© 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/pin of ThinkfleetAI/memmesh.

Open the folder on GitHubat commit bba48f8

Compare with similar skills

Pin 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.

Pin compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Pin this skillThinkfleetAI/memmesh419—~370Automated safety check: PassApache-2.0
Criticism Self CriticismHughYau/qiushi-skill3.8k—~423Automated safety check: PassMIT
Crossframe Criticalsickn33/agentic-awesome-skills47k1 repos~1.3kAutomated safety check: PassMIT
Critical Imagesthedaviddias/Front-End-Checklist74k—~392Automated safety check: PassMIT
CSS Criticalthedaviddias/Front-End-Checklist74k—~441Automated safety check: PassMIT
Consolidate Memoryasgeirtj/system_prompts_leaks69k—~492Automated safety check: PassCC0-1.0

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More from ThinkfleetAI/memmesh

All 24 skills in this repo

  • Behaviors

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  • Benchmark

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  • Context Loader

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  • Graph

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  • Memmesh CLI

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  • Memmesh SDK

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Questions about Pin

What does Pin do?

Protect a critical MemMesh memory from consolidation/pruning by raising its importance and marking it high-impact — or unpin to release it. Pin is an agent skill from ThinkfleetAI/memmesh. Protect a critical MemMesh memory from consolidation/pruning by raising its importance and marking it high-impact — or unpin to release it.

When should I use Pin?

Pin fits situations like: architecture decisions; security constraints; immutable team conventions that must never be retired by a dream pass.

How do I install Pin in Claude Code?

Run `npx skills add ThinkfleetAI/memmesh --skill pin -a claude-code`. Or copy the skill folder (integrations/memmesh-plugin/skills/pin in ThinkfleetAI/memmesh) into .claude/skills/pin in your project. Claude Code loads it when a task matches its description.

How do I install Pin in Codex?

Run `npx skills add ThinkfleetAI/memmesh --skill pin -a codex`. Or copy the skill folder (integrations/memmesh-plugin/skills/pin in ThinkfleetAI/memmesh) into .agents/skills/pin in your project. Codex loads it when a task matches its description.

Can I use Pin 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 pin -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pin, .gemini/skills/pin, .github/skills/pin and .opencode/skills/pin in your project.

What does Pin need to run?

SKILL.md names no scripts, command-line tools or credentials: Pin is instructions for the agent only.

Does Pin 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 Pin 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 Pin use?

Pin 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 Pin use?

About 370 tokens (SKILL.md is roughly 1.5k 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 Pin?

Skills that share tags, products or a category with Pin: Criticism Self Criticism (HughYau/qiushi-skill, 3.8k stars), Crossframe Critical (sickn33/agentic-awesome-skills, 47k stars), Critical Images (thedaviddias/Front-End-Checklist, 74k stars) and CSS Critical (thedaviddias/Front-End-Checklist, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pin?

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.