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 xiaolai/echo-sleuth-for-claude/plugin install echo-sleuthWrote 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/xiaolai/echo-sleuth-for-claude/dashboard)<a href="https://agentmods.dev/commands/xiaolai/echo-sleuth-for-claude/dashboard"><img src="https://agentmods.dev/badge/commands/xiaolai/echo-sleuth-for-claude/dashboard.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 | $0.00019 | $0.00245 |
| Opus 5 | $0.00010 | $0.00122 |
| Sonnet 5 | $0.00004 | $0.00049 |
| Haiku 4.5 | $0.00002 | $0.00024 |
Grade A, and why
dashboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Show a global overview of Claude Code memories across all projects.
Run the memory dashboard script to get the overview:
bash "${CLAUDE_PLUGIN_ROOT}/scripts/memory-dashboard.sh"
Present the output to the user. If there are staleness alerts, suggest running /audit or /audit --deep for detailed analysis. If token costs are high, suggest /prune for cleanup.
Output Format
Memory Dashboard — Global Overview
Project Memories Lines Stale Last Updated
────────────────────────────────────────────────────────────────────
project-a 12 340 2 2026-03-20
project-b 8 180 0 2026-03-28
project-c 5 95 3 2026-02-15
Total: 25 memories, 615 lines, ~2,153 tokens
Stale: 5 memories across 2 projects (review with /echo-sleuth:prune)
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 · 30 lines · 19 tokens per session scan A 64cf98c4a9a5
dashboard is a command published in the GitHub repository xiaolai/echo-sleuth-for-claude (9 stars, last pushed 12d ago), licensed ISC. It adds 19 tokens to every session and 245 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.
session-end
I'll summarize this coding session and update the memory system with our accomplishments.
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.