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/linggen/linggen-memory/solvegit clone --depth 1 https://github.com/linggen/linggen-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 | $0.00180 |
| Opus 5 | $0.00010 | $0.00090 |
| Sonnet 5 | $0.00004 | $0.00036 |
| Haiku 4.5 | $0.00002 | $0.00018 |
Grade A, and why
solve 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
Invoke the linggen skill (Skill tool) and treat this message as /linggen solve — Solve mode. Follow the skill's Solve runbook: list open items (memory_issues, or ling-mem issues --format json), then per item try to solve it YOURSELF first — gather evidence at solve time (full rows, git history, code, docs) and write what the evidence settles via memory_add + replace_ids — asking the user only when evidence can't settle it or a user-voice row is involved (then ONE simple fact question at a time, plain words, with your recommendation; user-voice fixes need user_directed:true after the ask), and close each item via memory_issue_resolve.
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 · 6 lines · 21 tokens per session scan A 4602ccfa89eb
solve is a command published in the GitHub repository linggen/linggen-memory (108 stars, last pushed 13d ago), licensed MIT. It adds 21 tokens to every session and 180 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
bm-init
Onboard (or refresh) basemind in this repo — write basemind.toml and inject a "prefer basemind over grep/read/git" rules block into a rules file you choose (CLAUDE.local.md / AGENTS.local.md / CLAUDE.md / AGENTS.md / ai-rulez).
bm
Ask basemind anything about the current codebase — outlines, refs, callers, git history, blame, diffs, docs, memory.
harden
Run the real-OSS harden harness against the 8 canary repos.
serve
Start the basemind MCP stdio server.
bm-stats
Show the basemind dashboard — resource footprint (disk + RAM) and activity (tool calls, per-tool histogram, estimated tokens saved). Works with or without the MCP server.
bm-statusline
Enable the basemind status line in your Claude Code user settings (one-time setup).