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 skills/shofer-dev/claude-code-live-memory/live-memorynpx skills add shofer-dev/claude-code-live-memory --skill live-memorygit clone --depth 1 https://github.com/shofer-dev/claude-code-live-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 | $0.00059 | $0.00568 |
| Opus 5 | $0.00030 | $0.00284 |
| Sonnet 5 | $0.00012 | $0.00114 |
| Haiku 4.5 | $0.00006 | $0.00057 |
Grade A, and why
live-memory 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.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Live Memory
The Live Memory is a long-running companion that holds an ever-growing, read-only understanding of this codebase. Use it to answer "where / how / what calls what" questions cheaply, without loading large files into your own context.
When to use ask_live_memory
Prefer it for codebase-knowledge questions:
- "Where is X implemented / configured / registered?"
- "How does component Y work, and what does it depend on?"
- "What calls Z / what would break if I change it?"
- "What's the convention for doing W in this repo?"
It can read files, ripgrep, and glob on its own to answer — you do not need to pre-load context for it.
When NOT to use it
- For writing or editing code (it is read-only — it answers, it never changes files).
- For facts unrelated to this repository.
- When you already have the answer in your own context.
How to call it
Call the ask_live_memory MCP tool with:
question(required) — a specific, self-contained question.cwd(required) — the absolute path of the current project/repository root (your session's working directory). Must be absolute — a relative path is rejected, since the server cannot resolve it against your session. The memory is keyed per repository, so always pass the repo root, not a subdirectory.max_answer_tokens(optional) — a hard cap on the answer length, in output tokens (~4 characters each). The Live Memory is told this budget and keeps its answer within it; beyond the cap the answer is truncated. Omit it (or pass 0) for the default, which suits normal lookups. Raise it when you deliberately want a long, detailed answer (e.g. "walk me through the whole request flow"); lower it to force a terse one.
The call blocks until the answer arrives, bounded by a server-configured time
budget (default_timeout_s, typically 1–2 minutes) — there is no timeout
argument. At the budget the Live Memory returns its best-effort answer so far.
Ask one focused question per call. The answer string is what you get back — the Live Memory's own working context is private and does not enter yours.
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 · 52 lines · 59 tokens per session scan A a99106ee0c2b
live-memory is a skill published in the GitHub repository shofer-dev/claude-code-live-memory (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 568 once invoked, about $0.0003 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…