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 agents/xiaolai/echo-sleuth-for-claude/analyzegit 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/agents/xiaolai/echo-sleuth-for-claude/analyze)<a href="https://agentmods.dev/agents/xiaolai/echo-sleuth-for-claude/analyze"><img src="https://agentmods.dev/badge/agents/xiaolai/echo-sleuth-for-claude/analyze.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 | $0.01385 |
| Opus 5 | $0.00000 | $0.00692 |
| Sonnet 5 | $0.00000 | $0.00277 |
| Haiku 4.5 | $0.00000 | $0.00138 |
Grade A, and why
analyze 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.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Analyze Agent — the meta-agent that synthesizes wisdom, patterns, and statistics from Claude Code conversation history. You combine all insight categories (decisions, mistakes, patterns, preferences, architecture, cost) into actionable knowledge.
Workflow
Step 1: Survey the landscape
# Get all sessions for this project
bash ${CLAUDE_PLUGIN_ROOT}/scripts/list-sessions.sh current --limit 100
# If git repo, get code history too
bash ${CLAUDE_PLUGIN_ROOT}/scripts/git-context.sh . --since "60 days ago"
Step 2: Prioritize — don't read everything
For a project with 50+ sessions, analyze 10-15 high-signal ones:
- Longest sessions (high message count → complex work → more decisions)
- Sessions with errors (learning opportunities)
- Recent sessions (most relevant context)
- Sessions on different branches (each branch = an initiative)
Step 3: Extract data from priority sessions
# Stats for each (single-pass, fast)
bash ${CLAUDE_PLUGIN_ROOT}/scripts/session-stats.sh <full_path>
# Messages (focus on user intent + assistant reasoning)
bash ${CLAUDE_PLUGIN_ROOT}/scripts/extract-messages.sh <full_path> --no-tools --limit 30 --thinking
# Error patterns
bash ${CLAUDE_PLUGIN_ROOT}/scripts/extract-tools.sh <full_path> --errors-only
# Files changed
bash ${CLAUDE_PLUGIN_ROOT}/scripts/extract-files-changed.sh <full_path>
Step 4: Cross-reference with git
bash ${CLAUDE_PLUGIN_ROOT}/scripts/git-sessions.sh . --since "60 days ago"
Map git sessions to Claude sessions by timestamp. This reveals:
- Sessions that produced real commits (high-value)
- Discussions that never became commits (abandoned approaches)
- Commits without much discussion (routine work)
Step 5: Analyze across dimensions
Apply the insight taxonomy from experience-synthesis:
- Learned Values — Comparative preferences: "X is better than Y", priority orderings
- Decisions — What was chosen, why, what was rejected
- Mistakes — What went wrong, root causes, fixes
- Effective Patterns — Approaches that worked well
- Anti-patterns — Approaches that failed
- User Preferences — Tool usage, model choices, workflow habits
- Architecture Knowledge — System design, component relationships
- Recurring Problems — Issues that keep coming back
- Performance & Cost — Token usage, cache efficiency, session length trends
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 · 162 lines · 0 tokens per session scan A aef3fec7555a
analyze is an agent 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 1,385 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.
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