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 skills add existential-birds/beagle --skill elixir-performance-reviewgit clone --depth 1 https://github.com/existential-birds/beagleWrote 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/skills/existential-birds/beagle/elixir-performance-review)<a href="https://agentmods.dev/skills/existential-birds/beagle/elixir-performance-review"><img src="https://agentmods.dev/badge/skills/existential-birds/beagle/elixir-performance-review/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/skills/existential-birds/beagle/elixir-performance-review"><img src="https://agentmods.dev/badge/skills/existential-birds/beagle/elixir-performance-review.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 | $0.00038 | $0.00745 |
| Opus 5.5 | $0.00015 | $0.00298 |
| Sonnet 5.5 | $0.00008 | $0.00149 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
elixir-performance-review 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 1mo 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Elixir Performance Review
Quick Reference
| Issue Type | Reference |
|---|---|
| Mailbox overflow, blocking calls | references/genserver-bottlenecks.md |
| When to use ETS, read/write concurrency | references/ets-patterns.md |
| Binary handling, large messages | references/memory.md |
| Task patterns, flow control | references/concurrency.md |
Review Checklist
GenServer
- Not a single-process bottleneck for all requests
- No blocking operations in handle_call/cast
- Proper timeout configuration
- Consider ETS for read-heavy state
Memory
- Large binaries not copied between processes
- Streams used for large data transformations
- No unbounded data accumulation
Concurrency
- Task.Supervisor for dynamic tasks (not raw Task.async)
- No unbounded process spawning
- Proper backpressure for message producers
Database
- Preloading to avoid N+1 queries
- Pagination for large result sets
- Indexes for frequent queries
Valid Patterns (Do NOT Flag)
- Single GenServer for low-throughput - Not all state needs horizontal scaling
- Synchronous calls for critical paths - Consistency may require it
- In-memory state without ETS - ETS has overhead for small state
- Enum over Stream for small collections - Stream overhead not worth it
Context-Sensitive Rules
| Issue | Flag ONLY IF |
|---|---|
| GenServer bottleneck | Handles > 1000 req/sec OR blocking I/O in callbacks |
| Use streams | Processing > 10k items OR reading large files |
| Use ETS | Read:write ratio > 10:1 AND concurrent access |
Gates — before reporting
Do these in order for the performance review. Do not publish findings until each step passes.
- Protocol loaded — Read review-verification-protocol and apply its checks for each finding (hot paths, concurrency, resource use). Pass: For every substantive finding, you can name which protocol subsection you satisfied or state N/A with reason (e.g. pure reference to this skill’s Valid Patterns).
- Anchored evidence — Pass: Each finding includes a concrete locator:
path:line(or line range), orModule.function/arityplus a short quoted snippet from the file. - Performance claims — For anything under Context-Sensitive Rules, or any claim of bottleneck, N+1, unbounded growth, or heavy memory/binary cost, Pass: You state the observed or measured fact that meets “Flag ONLY IF” (e.g. rate, item count, ratio), or attach an artifact (profiler output, SQL/log excerpt,
grep/search scope)—otherwise downgrade to question / suspected with what was not verified.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 66 lines · 38 tokens per session scan A f7d29b6ece7b
elixir-performance-review is a skill published in the GitHub repository existential-birds/beagle (82 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 745 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 2026-09-16.
Other skills, from other repositories
dotnet-reverse
A guide for analyzing compiled .NET and C# programs, including managed Windows executables and libraries. Reverse engineering means studying compiled software to understand how it works, and decompiling turns it back into readable approximate source code.
dart-run-static-analysis
Execute dart analyze to identify warnings and errors, and use dart fix --apply to automatically resolve mechanical lint issues. Use during development to ensure code quality and before committing changes.
agents-sdk-dotnet-debugging
Use when troubleshooting an agent built with the Microsoft Agents SDK (Microsoft.Agents.Hosting.AspNetCore and related packages) in C# / .NET. Trigger on any of these symptoms: build or C# compile errors, crashes on startup, 401 or auth errors on incoming requests, the bot not responding to messages, appsettings.json…
memory-optimization
Optimize Python code for reduced memory usage and improved memory efficiency. Use when asked to reduce memory footprint, fix memory leaks, optimize data structures for memory, handle large datasets efficiently, or diagnose memory issues. Covers object sizing, generator patterns, efficient data structures, and memory…
prismer-python-debugpy
Debug Python: pdb REPL + debugpy remote (DAP).
golang-error-handling
Idiomatic Golang error handling — creation, wrapping with %w, errors.Is/As, errors.Join, custom error types, sentinel errors, panic/recover, the single handling rule, structured logging with slog, HTTP request logging middleware, and samber/oops for production errors. Built to make logs usable at scale with log…