Agent skill

Slm Status

by qualixar in qualixar/superlocalmemory

Health and optimization stats for SuperLocalMemory — call slmoptimizestats() for live compression and cache counters (compressruns, tokenssavedcompress, cacheproxyhits, cacheproxymisses…

AGPL-3.0Auto-check: notesAgent Workflows

Install Slm Status

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

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

GitHub CLI
$ gh skill install qualixar/superlocalmemory slm-status --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-status .claude/skills/slm-status && 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
slm-status
GitHub stars
231
Token cost
~2.4k tokens
SKILL.md length
989 words
Files
1
Skills in repo
17
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Health and optimization stats for SuperLocalMemory — call slmoptimizestats() for live compression and cache counters (compressruns, tokenssavedcompress, cacheproxyhits, cacheproxymisses…

  • Works in 5 steps: Run slm doctor --json at session start… → Call slm_optimize_stats() after a batch… → Run slm status --json when you need DB… → …
  • Agent Workflows work in your project
  • SKILL.md covers Purpose, Primary MCP Tool:…, Secondary CLI: slm status and Secondary CLI: slm doctor, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Slm Status is an agent skill from qualixar/superlocalmemory. Health and optimization stats for SuperLocalMemory — call slmoptimizestats() for live compression and cache counters (compressruns, tokenssavedcompress, cacheproxyhits, cacheproxymisses, cachekvhits, cachekvmisses); run slm status [--json] for system state (mode, profile, DB size, fact/entity/edge counts) and slm doctor [--json] for preflight including the "Optimize (Surface B)" health line; use together to confirm optimization is actually saving tokens.

Its SKILL.md is about 2.4k 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. 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

  • “Optimize (Surface B)”
  • “/slm-status”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): slm_optimize_stats, get_status, get_brain_evidence_status, Bash

Workflow steps

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

  1. Run slm doctor --json at session start to confirm all subsystems are up.
  2. Call slm_optimize_stats() after a batch of work to check token savings.
  3. Run slm status --json when you need DB size or memory counts.
  4. If ok: false on any MCP tool — check note field, then run slm doctor to isolate the failure.
  5. If recall seems to miss memories that were saved, run slm db integrity before concluding anything.

What it can do on your machine

Read from SKILL.md and the folder at commit 26f8c68. 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_optimize_stats
    • get_status
    • get_brain_evidence_status
    • 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 bash, python and json).

    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 Status loads about 2.4k tokens when it runs. Until then it costs about 121 tokens; SKILL.md has 989 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~121
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

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_optimize_stats, get_status, get_brain_evidence_status, 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 26f8c68, republished under its AGPL-3.0 licence (© qualixar). 989 words, ~2,409 tokens.

Download SKILL.mdSave it as .claude/skills/slm-status/SKILL.md (or your agent's skills folder).
name
slm-status
description
Health and optimization stats for SuperLocalMemory — call slm_optimize_stats() for live compression and cache counters (compress_runs, tokens_saved_compress, cache_proxy_hits, cache_proxy_misses, cache_kv_hits, cache_kv_misses); run slm status [--json] for system state (mode, profile, DB size, fact/entity/edge counts) and slm doctor [--json] for preflight including the "Optimize (Surface B)" health line; use together to confirm optimization is actually saving tokens.
allowed-tools
slm_optimize_stats, get_status, get_brain_evidence_status, Bash
when_to_use
check slm status, health check, is slm working, optimize stats, tokens saved, cache hits, compress runs, slm doctor, preflight, db size, slm info, diagnose slm

slm-status — Health and Optimize Stats

Purpose

Use this skill to answer: "Is SLM healthy?", "Is compression/caching actually saving tokens?", and "What does the system look like right now?" It covers the MCP stats and status tools, the slm status CLI, the slm doctor preflight, and the store-health commands.

Primary MCP Tool: slm_optimize_stats

slm_optimize_stats() -> dict

No arguments. Returns the counters the daemon has persisted.

Return dict (all keys always present)
KeyTypeMeaning
okboolTrue on success; False on internal error
compress_runsintTotal compress calls recorded by the daemon (persisted across restarts)
tokens_saved_compressintCumulative tokens saved by compression (daemon-persisted)
cache_proxy_hitsintProxy-layer cache hits (daemon-persisted)
cache_proxy_missesintProxy-layer cache misses (daemon-persisted)
cache_kv_hitsintHits on the slm_cache_get key-value cache (daemon-persisted)
cache_kv_missesintMisses on the slm_cache_get key-value cache (daemon-persisted)
ccr_notestr | NoneNote about CCR entry count (not tracked per-session; see daemon /api/v1/metrics)
notestr | NoneScope clarification or error detail
Important scope distinction

All six counters are daemon-persisted: they survive MCP restarts and accumulate over the install's lifetime, so they are totals, not per-session figures. Only if the persisted KV counters cannot be read does the tool fall back to this process's own tally (the note field says so). To judge one stretch of work, call slm_optimize_stats before and after and subtract.

Reading whether optimization is saving tokens
python
stats = await slm_optimize_stats()
if stats["ok"]:
    savings = stats["tokens_saved_compress"]
    kv_hit_rate = (
        stats["cache_kv_hits"] / max(stats["cache_kv_hits"] + stats["cache_kv_misses"], 1)
    )
    # savings > 0 and kv_hit_rate > 0.5 means Surface B is actively reducing costs

If compress_runs is 0 after several sessions, compression is not being triggered — check daemon config and whether slm_compress is being called.

If cache_kv_hits does not grow after repeated work, verify key naming consistency (the same key string must be used for set and get, by the same agent).

Secondary CLI: slm status

bash
slm status [--json] [--verbose]

Reports system-level state — not optimization counters. Canonical fields:

  • mode — active operation mode (a, b or c)
  • provider — the LLM provider for modes B and C, or none
  • profile — current memory profile name
  • db_size_mb, db_path, base_dir — where the store is and how big
  • fact_count, entity_count, edge_count — counts for the active profile
  • version, profile_generation, projection_queue_depth, and (daemon running) saves_waiting and unreadable_saves

--verbose / -v adds: the disabled marker, last booted version, and daemon port.

--json prints the standard envelope {"success", "command", "version", "data": {...}}; read the fields above from data. Prefer it for agent consumption:

bash
slm status --json
json
{"success":true,"command":"status","version":"...","data":{"mode":"a","provider":"none","profile":"default","db_size_mb":12.4,"fact_count":384,"entity_count":201,"edge_count":519,"profile_generation":0,"projection_queue_depth":0,"saves_waiting":0,"unreadable_saves":0}}

The MCP equivalent is get_status(profile_id=""), which returns the same fields at the top level (it is not part of the smallest core tool set). saves_waiting above zero means saved memories are durable but still being indexed (searchable within seconds). unreadable_saves above zero means that many saves could not be read back with this computer's key and were kept unchanged in the admission journal (the daemon log has their ids); a negative value means the journal did not answer. Status does not report a user role.

Do not rely on the human-readable format for parsing — always use --json when the output feeds another tool.

Secondary CLI: slm doctor

bash
slm doctor [--json] [--quick] [--deep] [--fix]

Preflight check covering dependencies, embedding worker, daemon connectivity, and Surface B health. The "Optimize (Surface B)" line confirms whether the compression and cache subsystem initialised correctly.

--quick skips the daemon and embedding probes — runs only dependency and config checks; faster but incomplete.

--deep reads every database page (PRAGMA integrity_check) instead of the structural check; slow on a large store. --fix repairs what it can (re-downloads missing models, installs sqlite-vec) before checking, then reports.

--json outputs structured results per check — use this in automated health pipelines.

A passing doctor output confirms:

  • Python deps present
  • Embedding worker reachable
  • Daemon responding
  • Surface B (Optimize) initialised

A failing "Optimize (Surface B)" line means slm_compress, slm_cache_set, and slm_cache_get may not function correctly — investigate daemon config before relying on those tools.

Show full SKILL.md (395 more words)Show less

Secondary CLI: slm optimize status

bash
slm optimize status [--json]

Shows whether the Optimize module (cache + compress) is currently enabled or disabled at the daemon level. Available subcommands also include optimize on, optimize off, and optimize savings.

The optimize savings subcommand accepts:

bash
slm optimize savings [--since <days>] [--provider anthropic|openai|gemini] [--json]

--since defaults to 7 days. --provider filters by the target AI provider.

slm_optimize_stats() via MCP is the same data as slm optimize savings; use whichever surface you have.

Store health and recovery

bash
slm db integrity [--pages] [--json]   # read-only, safe while SLM runs
slm db repair [--json]                # preview of what a repair would do (read-only)
slm db repair --apply --root <data folder> [--batch-size N] [--pause-ms MS] [--max-seconds S]
slm db repair --undo <run_id> --root <data folder>
slm ops list | status | resolve <operation_id> --action retry|force_reconcile|cancel
slm brain status [--json]             # observation-only Living Brain evidence totals
slm embedder status                   # progress of an embedding-model switch
slm models                            # recommended and installed local models

slm db integrity answers five separate questions so one cannot hide another: page integrity (only with --pages), relational integrity (orphan rows, erased words still stored), source fidelity (facts withheld from answers or no longer saying what their memory said), projection readiness (keyword, vector and date search work still owed), and any repair running or last run. It prints counts only, never memory text.

slm db repair fixes leftover rows, erased-word leftovers, unfinished deletes and memories that lost their searchable fact, with receipts and an undo. It previews by default; --apply and --undo insist on --root naming the data folder you mean, and refuse any other. A repair never brings back anything that was erased, deleted or withheld. slm ops lists failed, stuck or degraded operations and, for an owner or admin, resolves them. The MCP counterpart for Living Brain totals is get_brain_evidence_status(profile_id=""); it only observes and does not change recall, ranking or review.

  1. Run slm doctor --json at session start to confirm all subsystems are up.
  2. Call slm_optimize_stats() after a batch of work to check token savings.
  3. Run slm status --json when you need DB size or memory counts.
  4. If ok: false on any MCP tool — check note field, then run slm doctor to isolate the failure.
  5. If recall seems to miss memories that were saved, run slm db integrity before concluding anything.

Fail-Open

slm_optimize_stats() never raises. On internal error it returns ok: false with all counters at 0. Continue the session — stats unavailability does not affect compression or caching operations.


Profile-aware status

slm status --json reports the active profile in data.profile. Use it to confirm which workspace is active before starting work on a multi-profile setup. get_status(profile_id="<name>") counts another profile without moving the active one. To change the active profile, see slm-profile.


  • slm-session — session lifecycle (session_init/close_session)
  • slm-profile — workspace isolation and profile switching
  • slm-cache — KV cache performance metrics
  • slm-compress — reversible context compression

SuperLocalMemory v4.1.24 · Qualixar · AGPL-3.0-or-later

© qualixar, AGPL-3.0. 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 plugin/skills/slm-status of qualixar/superlocalmemory.

Open the folder on GitHubat commit 26f8c68

Compare with similar skills

Slm Status 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 Status compared with similar skills
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Slm Status this skillqualixar/superlocalmemory231—~2.4kAutomated safety check: NotesAGPL-3.0
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Hook Development for Claude Code Pluginsanthropics/claude-plugins-official38k10 repos~4.1kAutomated safety check: NotesApache-2.0
Using Superpowersfarm-fe/farm5.6k35 repos~1.4kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers297k2 repos~5.1kAutomated safety check: PassMIT
Skill CreatorAzure/azqr79589 repos~8.2kAutomated safety check: PassApache-2.0

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Categories

Questions about Slm Status

What does Slm Status do?

Health and optimization stats for SuperLocalMemory — call slmoptimizestats() for live compression and cache counters (compressruns, tokenssavedcompress, cacheproxyhits, cacheproxymisses…. Slm Status is an agent skill from qualixar/superlocalmemory. Health and optimization stats for SuperLocalMemory — call slmoptimizestats() for live compression and cache counters (compressruns, tokenssavedcompress, cacheproxyhits, cacheproxymisses, cachekvhits, cachekvmisses); run slm status [--json] for system state (mode, profile, DB size, fact/entity/edge counts) and slm doctor [--json] for preflight including the "Optimize (Surface B)" health line; use together to confirm optimization is actually saving tokens.

When should I use Slm Status?

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

How do I install Slm Status in Claude Code?

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

How do I install Slm Status in Codex?

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

Can I use Slm Status 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-status -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-status, .gemini/skills/slm-status, .github/skills/slm-status and .opencode/skills/slm-status in your project.

What does Slm Status need to run?

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

Does Slm Status 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 Status 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 Status use?

Slm Status 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 Status use?

About 2.4k tokens (SKILL.md is roughly 9.6k 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 Status?

Skills that share tags, products or a category with Slm Status: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 297k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Slm Status?

qualixar (a GitHub organization) maintains it in qualixar/superlocalmemory, which has 231 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on October 9, 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.