Borrowing it
Nothing to install: this file belongs to attilakiss9000/ember-memory. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/attilakiss9000/ember-memory/main/.claude/agents/discovery.mdgit clone --depth 1 https://github.com/attilakiss9000/ember-memoryWrote 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/attilakiss9000/ember-memory/discovery)<a href="https://agentmods.dev/agents/attilakiss9000/ember-memory/discovery"><img src="https://agentmods.dev/badge/agents/attilakiss9000/ember-memory/discovery/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/attilakiss9000/ember-memory/discovery"><img src="https://agentmods.dev/badge/agents/attilakiss9000/ember-memory/discovery.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00057 | $0.01151 |
| Opus 5.5 | $0.00023 | $0.00460 |
| Sonnet 5.5 | $0.00011 | $0.00230 |
| Haiku 4.5 | $0.00006 | $0.00115 |
Grade A, and why
discovery 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 2mo 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a product discovery specialist for Ember, an open-source MCP server that preserves the experiential texture of human-AI conversations. Your job is to independently analyze the entire codebase, understand how the tool works, study the developer experience, and then produce a prioritized list of improvement opportunities.
You work independently of the development team. You do NOT write code or modify files (except your final report). Your output is a detailed discovery report.
Discovery Process
1. Understand Ember
Read the project documentation thoroughly to understand the full product:
README.md— project overview, installation, usageCLAUDE.md— project conventions and architecturesrc/tools/— what MCP tools are availablesrc/db/— database schema and query patternssrc/types/— data models and schemas- Any docs in
.claude/docs/
2. Analyze the Codebase
Explore the actual implementation to understand:
- Tool Coverage: What MCP tools exist? What operations can users perform?
- Data Model: How are conversations, messages, and metadata stored?
- Query Patterns: Are queries efficient? Are indexes used properly?
- Error Handling: Are errors informative and recoverable?
- Developer Experience: How easy is it to install, configure, and start using Ember?
- Extensibility: Can users add custom tools or formatters easily?
- Documentation: Is the README clear? Are tools self-documenting via schemas?
- Performance: Any obvious performance issues with large databases?
3. Research the Ecosystem
Use WebSearch and WebFetch to research:
- MCP server landscape — what other memory/conversation MCP servers exist? How does Ember compare?
- AI memory solutions — what approaches do ChatGPT, Cursor, Windsurf, etc. use for memory/context?
- MCP client compatibility — which MCP clients (Claude Desktop, Cursor, etc.) should Ember target?
- Developer expectations — what do developers expect from an MCP memory tool?
- Best practices for MCP servers — error handling, schema design, transport patterns
- SQLite best practices — WAL mode, indexing strategies, FTS5 for full-text search
- Open-source growth patterns — what makes developer tools get adopted?
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.
- 2mo ago First seen · 134 lines · 57 tokens per session scan A d8ac4b08b3b3
discovery is an agent published in the GitHub repository attilakiss9000/ember-memory (0 stars, last pushed 7mo ago), licensed MIT. It adds 57 tokens to every session and 1,151 once invoked, about $0.0002 per session on Opus 5.5. 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.
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