/slm-status
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,
$ npx -y skills add qualixar/superlocalmemory --skill slm-status --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/slm-status
Context preview
The summary Claude sees to decide when to auto-load this skill.
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,
SKILL.md
slm-status.SKILL.mdname: 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.
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"
allowed-tools: slm_optimize_stats, Bash
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 three surfaces: the MCP stats tool, the `slm status` CLI, and the `slm doctor` preflight.
Primary MCP Tool: slm_optimize_stats
slm_optimize_stats() -> dict
No arguments. Returns counters from the current daemon and MCP process session.
Return dict (all keys always present)
| Key | Type | Meaning | |-----|------|---------| | `ok` | bool | `True` on success; `False` on internal error | | `compress_runs` | int | Total compress calls recorded by the daemon (persisted across restarts) | | `tokens_saved_compress` | int | Cumulative tokens saved by compression (daemon-persisted) | | `cache_proxy_hits` | int | Proxy-layer cache hits (daemon-persisted) | | `cache_proxy_misses` | int | Proxy-layer cache misses (daemon-persisted) | | `cache_kv_hits` | int | MCP KV cache hits — **this MCP process session only**, resets on restart | | `cache_kv_misses` | int | MCP KV cache misses — **this MCP process session only**, resets on restart | | `ccr_note` | str \| None | Note about CCR entry count (not tracked per-session; see daemon `/api/v1/metrics`) | | `note` | str \| None | Scope clarification or error detail |
Important scope distinction
`compress_runs`, `tokens_saved_compress`, `cache_proxy_hits`, and `cache_proxy_misses` are **daemon-persisted** — they survive MCP restarts and accumulate over the full install lifetime.
`cache_kv_hits` and `cache_kv_misses` are **in-process counters** — they reset to 0 each time the MCP server starts. Use them to gauge cache effectiveness within the current session only.
Reading whether optimization is saving tokens
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 costsIf `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` is 0 after repeated work, verify key naming consistency (the same key string must be used for set and get).
Secondary CLI: slm status
slm status [--json] [--verbose]
Reports system-level state — not optimization counters. Canonical fields (WP-02):
- **mode** — active operation mode (e.g. `local`)
- **profile** — current memory profile name
- **DB size** — database file size on disk
- **fact count** — number of stored memory facts
- **entity count** — entity graph node count
- **edge count** — entity graph edge count
`--verbose` / `-v` adds: migration log, daemon port, disabled marker, last version.
`--json` outputs a machine-readable dict with the same fields — preferred for agent consumption.
Example agent-native invocation:
slm status --json
Typical JSON shape (exact field names depend on runtime; use `--json` and read what arrives):
{
"mode": "local",
"profile": "code",
"db_size_mb": 12.4,
"facts": 384,
"entities": 201,
"edges": 519
}Do not rely on the human-readable format for parsing — always use `--json` when the output feeds another tool.
Secondary CLI: slm doctor
slm doctor [--json] [--quick]
Preflight check covering dependencies, embedding worker, daemon connectivity, and Surface B health. The **"Optimize (Surface B)"** line (WP-03) 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.
`--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.
Secondary CLI: slm optimize status
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:
slm optimize savings [--since <days>] [--provider anthropic|openai|gemini] [--json]
`--since` defaults to 7 days. `--provider` filters by the target AI provider.
Note: the `slm optimize` subcommands have known pre-existing parse-test failures — if a subcommand errors, use `slm_optimize_stats()` via MCP as the authoritative source.
Recommended Health Workflow
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.
Fail-Open
`
Read more
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. 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" allowed-tools: slm_optimize_stats, Bash
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 three surfaces: the MCP stats tool, the `slm status` CLI, and the `slm doctor` preflight.
Primary MCP Tool: slm_optimize_stats
slm_optimize_stats() -> dict
No arguments. Returns counters from the current daemon and MCP process session.
Return dict (all keys always present)
| Key | Type | Meaning | |-----|------|---------| | `ok` | bool | `True` on success; `False` on internal error | | `compress_runs` | int | Total compress calls recorded by the daemon (persisted across restarts) | | `tokens_saved_compress` | int | Cumulative tokens saved by compression (daemon-persisted) | | `cache_proxy_hits` | int | Proxy-layer cache hits (daemon-persisted) | | `cache_proxy_misses` | int | Proxy-layer cache misses (daemon-persisted) | | `cache_kv_hits` | int | MCP KV cache hits — **this MCP process session only**, resets on restart | | `cache_kv_misses` | int | MCP KV cache misses — **this MCP process session only**, resets on restart | | `ccr_note` | str \| None | Note about CCR entry count (not tracked per-session; see daemon `/api/v1/metrics`) | | `note` | str \| None | Scope clarification or error detail |
Important scope distinction
`compress_runs`, `tokens_saved_compress`, `cache_proxy_hits`, and `cache_proxy_misses` are **daemon-persisted** — they survive MCP restarts and accumulate over the full install lifetime.
`cache_kv_hits` and `cache_kv_misses` are **in-process counters** — they reset to 0 each time the MCP server starts. Use them to gauge cache effectiveness within the current session only.
Reading whether optimization is saving tokens
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 costsIf `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` is 0 after repeated work, verify key naming consistency (the same key string must be used for set and get).
Secondary CLI: slm status
slm status [--json] [--verbose]
Reports system-level state — not optimization counters. Canonical fields (WP-02):
- **mode** — active operation mode (e.g. `local`)
- **profile** — current memory profile name
- **DB size** — database file size on disk
- **fact count** — number of stored memory facts
- **entity count** — entity graph node count
- **edge count** — entity graph edge count
`--verbose` / `-v` adds: migration log, daemon port, disabled marker, last version.
`--json` outputs a machine-readable dict with the same fields — preferred for agent consumption.
Example agent-native invocation:
slm status --json
Typical JSON shape (exact field names depend on runtime; use `--json` and read what arrives):
{
"mode": "local",
"profile": "code",
"db_size_mb": 12.4,
"facts": 384,
"entities": 201,
"edges": 519
}Do not rely on the human-readable format for parsing — always use `--json` when the output feeds another tool.
Secondary CLI: slm doctor
slm doctor [--json] [--quick]
Preflight check covering dependencies, embedding worker, daemon connectivity, and Surface B health. The **"Optimize (Surface B)"** line (WP-03) 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.
`--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.
Secondary CLI: slm optimize status
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:
slm optimize savings [--since <days>] [--provider anthropic|openai|gemini] [--json]
`--since` defaults to 7 days. `--provider` filters by the target AI provider.
Note: the `slm optimize` subcommands have known pre-existing parse-test failures — if a subcommand errors, use `slm_optimize_stats()` via MCP as the authoritative source.
Recommended Health Workflow
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.
Fail-Open
`
World's first local-only AI memory to break 74% retrieval and 60% zero-LLM on LoCoMo. No cloud, no APIs, no data leaves your machine. Additionally, mode C (LLM/Cloud) - 87.7% LoCoMo. Research-backed. arXiv: 2603.14588
Repo: qualixar/superlocalmemory
Other skills on superlocalmemory.
- /slm-cache
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
Open skill - /slm-compress
Compress large text, tool output, or transcripts to reduce context-window usage while keeping the full 1M window intact — call slm_compress(content, mode, reversible, ttl_seconds) to shrink content; if the result is lossy a ccr_id is returned so you can call slm_retrieve(ccr_id)
Open skill - /slm-governance
Enterprise compliance and governed workspace behavior for SuperLocalMemory. Covers role-based access (admin/member/viewer), retention policies, audit trail, GDPR data export/erase, and how agents must behave when operating under workspace governance. Requires power MCP profile
Open skill - /slm-graph
Index and query a codebase as a structural graph — build the code graph, trace blast radius of a change, find callers/callees/inheritors, semantic code search by meaning, assemble PR review context, and detect what changed since last index. Use when the user asks how code
Open skill - /slm-loop
Run gate-verified bounded loops with SuperLocalMemory as the durable ledger. Use when a task has a checkable acceptance condition (tests, schema, lint, reconciliation) and you must iterate until an INDEPENDENT gate passes — never stopping just because the agent believes it is
Open skill - /slm-mesh
Cross-session peer coordination via the SLM mesh network. Lets multiple AI agent sessions on the same machine discover each other, send messages, share lightweight state, and lock files to avoid conflicts. Requires full, power, or mesh MCP profile. All 8 tools are MCP-only —
Open skill

