Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/wuesteon/lean-memory/memory-statusgit clone --depth 1 https://github.com/Wuesteon/lean-memoryWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00021 | $0.00437 |
| Opus 5 | $0.00010 | $0.00218 |
| Sonnet 5 | $0.00004 | $0.00087 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
memory-status scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Report where memory actually lives and how to connect, so the human never inspects an empty root while the agent wrote elsewhere.
-
Print the resolved data root (the console applies
--root>LM_DATA_ROOT>~/.lean_memory):!
echo "Resolved root: ${LM_DATA_ROOT:-$HOME/.lean_memory}"Then enumerate namespace
.dbfiles under that root (skipping_*.db):!
ls -1 "${LM_DATA_ROOT:-$HOME/.lean_memory}"/*.db 2>/dev/null | grep -v '/_' || echo "(no namespaces yet)" -
./lm_data mismatch warning. The core engine's own default root is
./lm_data, not~/.lean_memory. If./lm_dataexists in the current project but is not the served root, the human would silently inspect an empty root. Warn when it exists:!
test -d ./lm_data && echo "WARNING: ./lm_data exists here. Your agent may have written memories to ./lm_data (the engine's default root), not ${LM_DATA_ROOT:-$HOME/.lean_memory}. Run '/memory:ui' with --root ./lm_data to inspect it." || echo "No ./lm_data in this directory." -
Connect snippets.
- Local observing MCP (this plugin already wires it via
.mcp.json):uvx lean-memory-console mcp - Open the console:
/memory:ui - Docker HTTP (after
/memory:server-up):claude mcp add --transport http lean-memory http://127.0.0.1:8377/mcp \ --header "Authorization: Bearer $LM_API_KEY"
- Local observing MCP (this plugin already wires it via
Guidance: use one namespace per project/session — cross-process writers on a single namespace serialize via retry, not a lock manager.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 36 lines · 21 tokens per session scan A a0c6dd165ea3
memory-status is a command published in the GitHub repository Wuesteon/lean-memory (47 stars, last pushed 25d ago), licensed Apache-2.0. It adds 21 tokens to every session and 437 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.