Agent skill

Slm Cache

by qualixar in qualixar/superlocalmemory

AGPL-3.0Auto-check: notesAgent Workflows

Install Slm Cache

skills CLI

$ npx skills add qualixar/superlocalmemory --skill slm-cache -a claude-code

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

GitHub CLI

$ gh skill install qualixar/superlocalmemory slm-cache --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/qualixar/superlocalmemory.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/slm-cache .claude/skills/slm-cache && 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/

Paste into Claude Code

Install the "slm-cache" agent skill from https://github.com/qualixar/superlocalmemory/tree/main/plugin/skills/slm-cache into .claude/skills/slm-cache/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "slm-cache", 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

$skill-installer install https://github.com/qualixar/superlocalmemory/tree/main/plugin/skills/slm-cache

Type 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.

skills CLI

$ npx skills add qualixar/superlocalmemory --skill slm-cache -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install qualixar/superlocalmemory slm-cache --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/qualixar/superlocalmemory.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugin/skills/slm-cache .agents/skills/slm-cache && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "slm-cache" agent skill from https://github.com/qualixar/superlocalmemory/tree/main/plugin/skills/slm-cache into .agents/skills/slm-cache/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "slm-cache", 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.

skills CLI

$ npx skills add qualixar/superlocalmemory --skill slm-cache -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install qualixar/superlocalmemory slm-cache --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/qualixar/superlocalmemory.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugin/skills/slm-cache .cursor/skills/slm-cache && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "slm-cache" agent skill from https://github.com/qualixar/superlocalmemory/tree/main/plugin/skills/slm-cache into .cursor/skills/slm-cache/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "slm-cache", 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

$ gemini skills install https://github.com/qualixar/superlocalmemory.git --path plugin/skills/slm-cache

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add qualixar/superlocalmemory --skill slm-cache -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install qualixar/superlocalmemory slm-cache --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/qualixar/superlocalmemory.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugin/skills/slm-cache .gemini/skills/slm-cache && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "slm-cache" agent skill from https://github.com/qualixar/superlocalmemory/tree/main/plugin/skills/slm-cache into .gemini/skills/slm-cache/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "slm-cache", 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.

GitHub CLI

$ gh skill install qualixar/superlocalmemory slm-cache

Installs 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).

skills CLI

$ npx skills add qualixar/superlocalmemory --skill slm-cache -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/qualixar/superlocalmemory.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugin/skills/slm-cache .github/skills/slm-cache && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "slm-cache" agent skill from https://github.com/qualixar/superlocalmemory/tree/main/plugin/skills/slm-cache into .github/skills/slm-cache/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "slm-cache", 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.

skills CLI

$ npx skills add qualixar/superlocalmemory --skill slm-cache -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install qualixar/superlocalmemory slm-cache --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/qualixar/superlocalmemory.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugin/skills/slm-cache .opencode/skills/slm-cache && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "slm-cache" agent skill from https://github.com/qualixar/superlocalmemory/tree/main/plugin/skills/slm-cache into .opencode/skills/slm-cache/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "slm-cache", 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: slm-cache
GitHub stars: 227
Token cost: ~1.5k tokens
SKILL.md length: 584 words
Files: 1
Skills in repo: 13
Repo updated: Oct 8, 2026
First seen: Oct 7, 2026
Licence: AGPL-3.0

At a glance

Agent Workflows work in your project
SKILL.md covers Purpose, Tool: slm_cache_set, Tool: slm_cache_get and Standard Pattern: Cache-Aside, plus 6 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Slm Cache is an agent skill from qualixar/superlocalmemory. KV cache for repeated reads — call slmcacheget(key) first; on a miss do the expensive operation then slmcacheset(key, value, ttlseconds) to store it; on a hit use the returned value directly; always fail-open (hit:false on any error, never raises); saves tokens when the same file, query result, or tool output is read more than once in a session.

Its SKILL.md is about 1.5k 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 Agent Workflows. It works with Model Context Protocol. The repository describes itself as: Open-source governed, local-first memory control plane for AI agents and teams. arXiv:2608.08253. The licence is AGPL-3.0.

When your agent uses it

Agent Workflows work in your project

Example prompts

“/slm-cache”

Requirements

Python 3
Pre-approved tools (allowed-tools): slm_cache_set, slm_cache_get, Bash

What it can do on your machine

Read from SKILL.md and the folder at commit ce2d7a9. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
- slm_cache_set
- slm_cache_get
- Bash
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 python and bash).
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

Slm Cache loads about 1.5k tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 584 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~91
When it runs · the whole SKILL.md, loaded when a task matches: ~1.5k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
```
allowed-tools: slm_cache_set, slm_cache_get, Bash
```

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 qualixar/superlocalmemory at commit ce2d7a9, republished under its AGPL-3.0 licence (© qualixar). 584 words, ~1,505 tokens.

Download SKILL.mdSave it as .claude/skills/slm-cache/SKILL.md (or your agent's skills folder).

name: slm-cache
description: KV cache for repeated reads — call slm_cache_get(key) first; on a miss do the expensive operation then slm_cache_set(key, value, ttl_seconds) to store it; on a hit use the returned value directly; always fail-open (hit:false on any error, never raises); saves tokens when the same file, query result, or tool output is read more than once in a session.
allowed-tools: slm_cache_set, slm_cache_get, Bash
when_to_use: cache file, avoid re-reading, repeated read, cache result, cache tool output, save re-read, reuse across session, cache check, cache hit, cache miss

slm-cache — KV Cache for Repeated Reads (Surface B)

Purpose

When the same file, query result, or expensive tool output is needed more than once in a session, fetching it again wastes tokens and time. slm_cache_set stores a result under a stable key; slm_cache_get retrieves it on subsequent calls. The cache is agent-scoped (automatically namespaced by tenant/agent ID), TTL-bounded, and fail-open.

This is an agent-routed cache — it caches results the agent explicitly routes through SLM. It cannot cache Claude conversation turns.

Tool: slm_cache_set

slm_cache_set(
    key:          str,          # required — cache key (max 512 chars)
    value:        str,          # required — value to store (max 1 MB)
    ttl_seconds:  int = 86400,  # time-to-live in seconds (default 24 h)
) -> dict

Return dict

Key	Type	Meaning
`ok`	bool	`True` on success; `False` on validation error or internal error
`stored`	bool	`True` when the value was written to the cache
`note`	str \| None	Error detail or `None` on success

Keys are SHA-256-hashed internally per agent so they do not collide across agents. The raw key string you supply is the only handle you need.

Tool: slm_cache_get

slm_cache_get(
    key: str,   # required — same key used in slm_cache_set
) -> dict

Return dict

Key	Type	Meaning
`ok`	bool	`True` on clean execution (including miss); `False` on internal error
`hit`	bool	`True` when the key exists and has not expired
`value`	str \| None	The stored value on a hit; `None` on miss
`note`	str \| None	Error detail or `None`

A miss returns {"ok": true, "hit": false, "value": null, "note": null}. ok: false means something went wrong internally but the miss behaviour is the same — treat both as a cache miss and proceed with the real fetch.

Standard Pattern: Cache-Aside

Always check the cache first, then fill on miss:

python

# 1. Check cache
cached = await slm_cache_get(key="file:/absolute/path/to/config.json")

if cached["hit"]:
    content = cached["value"]
else:
    # 2. Expensive operation (file read, search, API call)
    content = read_file("/absolute/path/to/config.json")

    # 3. Store for the rest of the session
    await slm_cache_set(
        key="file:/absolute/path/to/config.json",
        value=content,
        ttl_seconds=3600,   # 1 h — adjust to data volatility
    )

# 4. Use content

Key Naming Convention

Use a stable, human-readable prefix so keys are recognisable in stats and won't collide accidentally:

Content type	Suggested prefix	Example
File read	`file:`	`file:/repo/src/config.py`
Search result	`search:`	`search:recall:session_init_context`
Tool output	`tool:`	`tool:build_code_graph:/repo`
External fetch	`url:`	`url:https://api.example.com/v1/data`

Key length cap: 512 characters. Keys longer than that are rejected (ok: false).

When Caching Pays Off

Cache when:

You will read the same file more than once in a session.
A search or recall result is reused across multiple reasoning steps.
An expensive MCP tool call (graph build, semantic search) produces output that is stable for the session duration.

Do NOT cache:

Volatile data (live API responses that change minute-to-minute, current timestamps, streaming output).
Secrets, credentials, tokens, or ccr_id values (CCR already handles its own storage).
Data that must be fresh for correctness — a stale cache is worse than a cache miss.
Intermediate scratchpad text you will discard.

Show full SKILL.md (194 more words)Show less

Fail-Open Guarantee

Neither tool raises an exception. On any internal error:

slm_cache_get returns {"ok": false, "hit": false, "value": null, ...} — treat as a miss and proceed with the real fetch.
slm_cache_set returns {"ok": false, "stored": false, ...} — log the note if useful, but continue; the value is still available in memory this step.

Never block a task on a cache failure.

TTL Guidance

Data type	Suggested TTL
Static config / generated file	86400 s (24 h — the default)
Session-specific tool output	3600 s (1 h)
Rapidly changing API data	Do not cache, or 60–300 s

Set ttl_seconds to match how long the data remains valid. After expiry slm_cache_get returns a miss automatically.

Secondary CLI (fallback when MCP is unavailable)

The slm cache subcommand exists but has known pre-existing parse-test failures. Prefer the MCP tools above. If you must use CLI:

bash

slm cache status [--json] [--tenant default]
slm cache clear [--json] [--tenant default]
slm cache invalidate --tag <tag> [--json] [--tenant default]
slm cache ttl --set <seconds> [--semantic <seconds>] [--json] [--tenant default]
slm cache semantic on|off [--json] [--tenant default]

These subcommands control daemon-level cache settings. They do not read or write individual cache entries — use the MCP tools for that.

slm-compress — for large content reduction; cache and compress work together
slm-status — view cache_kv_hits/cache_kv_misses counters from slm_optimize_stats
slm-profile — cache entries are namespaced per profile; switching profiles gives a fresh cache namespace

SuperLocalMemory v4.1.21 · Qualixar · AGPL-3.0-or-later

Files

Just SKILL.md in plugin/skills/slm-cache of qualixar/superlocalmemory.

Open the folder on GitHubat commit ce2d7a9

Compare with similar skills

Slm Cache 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.

Slm Cache compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Slm Cache this skillqualixar/superlocalmemory	227	—	~1.5k	Automated safety check: Notes	AGPL-3.0	today
MCP Server Builderanthropics/skills	180k	64 repos	~2.3k	Automated safety check: Pass	Apache-2.0	3 days ago
MCP Server BuildershareAI-lab/learn-claude-code	78k	5 repos	~1.2k	Automated safety check: Pass	MIT	10 days ago
MCP Integration for Pluginsanthropics/claude-plugins-official	38k	11 repos	~3.1k	Automated safety check: Pass	Apache-2.0	today
Fastmcp Client CLIPrefectHQ/fastmcp	28k	1 repos	~823	Automated safety check: Pass	Apache-2.0	today
MemPalace Memory SearchMemPalace/mempalace	59k	—	~1.4k	Automated safety check: Pass	MIT	today

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Questions about Slm Cache

What does Slm Cache do?

KV cache for repeated reads — call slmcacheget(key) first; on a miss do the expensive operation then slmcacheset(key, value, ttlseconds) to store it; on a hit use the returned value directly; always…. Slm Cache is an agent skill from qualixar/superlocalmemory. KV cache for repeated reads — call slmcacheget(key) first; on a miss do the expensive operation then slmcacheset(key, value, ttlseconds) to store it; on a hit use the returned value directly; always fail-open (hit:false on any error, never raises); saves tokens when the same file, query result, or tool output is read more than once in a session.

When should I use Slm Cache?

Slm Cache fits situations like: agent Workflows work in your project.

How do I install Slm Cache in Claude Code?

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

How do I install Slm Cache in Codex?

Run `npx skills add qualixar/superlocalmemory --skill slm-cache -a codex`. Or copy the skill folder (plugin/skills/slm-cache in qualixar/superlocalmemory) into .agents/skills/slm-cache in your project. Codex loads it when a task matches its description.

Can I use Slm Cache 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 qualixar/superlocalmemory --skill slm-cache -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/slm-cache, .gemini/skills/slm-cache, .github/skills/slm-cache and .opencode/skills/slm-cache in your project.

What does Slm Cache need to run?

SKILL.md names no scripts, command-line tools or credentials: Slm Cache is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: slm_cache_set, slm_cache_get, Bash.

Does Slm Cache 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 Slm Cache safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Slm Cache use?

Slm Cache is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Slm Cache use?

About 1.5k tokens (SKILL.md is roughly 6k 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 Slm Cache?

Skills that share tags, products or a category with Slm Cache: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Fastmcp Client CLI (PrefectHQ/fastmcp, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Slm Cache?

qualixar (a GitHub organization) maintains it in qualixar/superlocalmemory, which has 227 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on October 8, 2026.

Source: qualixar/superlocalmemory on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Slm Cache

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

slm-cache — KV Cache for Repeated Reads (Surface B)

Purpose

Tool: slm_cache_set

Return dict

Tool: slm_cache_get

Return dict

Standard Pattern: Cache-Aside

Key Naming Convention

When Caching Pays Off

Fail-Open Guarantee

TTL Guidance

Secondary CLI (fallback when MCP is unavailable)

Related skills

Files

Compare with similar skills

Similar skills

More from qualixar/superlocalmemory

Related

Questions about Slm Cache