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/xiaolai/echo-sleuth-for-claude/lessonsgit clone --depth 1 https://github.com/xiaolai/echo-sleuth-for-claudeWrote 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/lessons)<a href="https://agentmods.dev/commands/xiaolai/echo-sleuth-for-claude/lessons"><img src="https://agentmods.dev/badge/commands/xiaolai/echo-sleuth-for-claude/lessons.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 | $0.00000 | $0.00247 |
| Opus 5 | $0.00000 | $0.00123 |
| Sonnet 5 | $0.00000 | $0.00049 |
| Haiku 4.5 | $0.00000 | $0.00025 |
Grade A, and why
lessons 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 4d 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
Extract lessons and wisdom from past Claude Code conversation sessions.
Arguments: $ARGUMENTS
Default: analyze the current project across all categories. If a topic is provided, focus on lessons related to that topic. If --category is specified, focus on that insight category only.
Launch the analyze agent via the Task tool with the following context:
- Task: Extract accumulated wisdom and lessons
- Scope: current project (or all if
--scope allspecified) - Topic filter: from $ARGUMENTS if provided
- Category filter: from --category flag if provided
- Current working directory: for project identification
The analyze agent will:
- Survey all sessions for the project
- Strategically sample high-signal sessions (longest, most errors, most recent)
- Extract insights across all categories from the experience-synthesis taxonomy
- Cross-reference with git history if available
- Produce a structured wisdom report with confidence levels
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.
- 4d ago First seen · 29 lines · 0 tokens per session scan A e6eae60445f4
lessons is a command published in the GitHub repository xiaolai/echo-sleuth-for-claude (9 stars, last pushed 11d ago), licensed ISC. It costs nothing until one of its globs matches a file; then it loads 247 tokens. 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.