Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add tiny-cloud-ventures/amnesia/plugin install amnesiaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/tiny-cloud-ventures/amnesia/scan)<a href="https://agentmods.dev/commands/tiny-cloud-ventures/amnesia/scan"><img src="https://agentmods.dev/badge/commands/tiny-cloud-ventures/amnesia/scan.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00013 | $0.00233 |
| Opus 5 | $0.00006 | $0.00117 |
| Sonnet 5 | $0.00003 | $0.00047 |
| Haiku 4.5 | $0.00001 | $0.00023 |
Grade A, and why
scan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Run the amnesia memory audit and tell the user what it found, in plain language.
- Run
python3 "${CLAUDE_PLUGIN_ROOT}/amnesia.py" analyzewith a 10-minute Bash timeout — it feeds the whole memory store through the user's ownclaudeCLI, so it takes a few minutes. If it fails because theclaudeCLI is missing from PATH, say so and stop. - Read
~/.claude/amnesia/analysis.json. - Summarize the findings as short human sentences — "Two memories disagree about which port X runs on", not filenames or JSON. Lead with the count: contradictions first, then stale facts, then duplicates and misfiled memories. If there is nothing to report, say the memory store looks clean and stop.
- Close with: run
/amnesia:opento review and fix them one at a time (fixes are one click and reversible — everything goes to trash, never deleted).
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.
- 6d ago First seen · 12 lines · 13 tokens per session scan A 7b66e3162fc7
scan is a command published in the GitHub repository tiny-cloud-ventures/amnesia (7 stars, last pushed 1mo ago), licensed MIT. It adds 13 tokens to every session and 233 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 2026-08-31.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.