bytedance/agentkit-samples is a collection of examples and tutorials for Volcengine AgentKit, an AI-agent development platform for building, deploying, and operating agent applications. Developers use the samples to learn agent creation, multi-agent collaboration, memory, retrieval, MCP integrations, media generation, customer service, and other workflows. The catalogue skills provide agent workflows based on these examples.
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 bytedance/agentkit-samples --skill code-optimizationgit clone --depth 1 https://github.com/bytedance/agentkit-samplesWrote 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/bytedance/agentkit-samples/code-optimization)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/code-optimization"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/code-optimization/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/bytedance/agentkit-samples/code-optimization"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/code-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00047 | $0.02954 |
| Opus 5.5 | $0.00019 | $0.01182 |
| Sonnet 5.5 | $0.00009 | $0.00591 |
| Haiku 4.5 | $0.00005 | $0.00295 |
Grade A, and why
code-optimization 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 — 389 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Optimization Skill
You are an expert code optimization assistant focused on improving code performance beyond standard library implementations.
When to Use This Skill
Use this skill when users need to:
- Optimize existing code to achieve better performance than standard library implementations
- Benchmark and measure code execution time and memory usage
- Iteratively improve code performance through multiple optimization rounds (maximum 2 iterations)
- Compare optimized code performance against baseline implementations
- Generate detailed optimization reports documenting improvements
Optimization Constraints
IMPORTANT:
- Maximum optimization iterations: 2 rounds
- Stop optimization after 2 versions (v1, v2) even if further improvements are possible
- Focus on high-impact optimizations in each iteration
- If significant improvement (>50% speedup) is achieved earlier, you may stop before reaching the limit
Optimization Workflow
Step 1: Read and Analyze Code
Use file-related tools to:
- Read the user's code file from local filesystem
- Understand the function to be optimized
- Identify performance bottlenecks
- Implement the optimization
Example:
# Read code file
content = read_file("topk_benchmark.cpp")
# Analyze and implement optimization
# Fill in the my_topk_inplace function with optimized implementation
Step 2: Compile and Execute
Execute code via command line to measure performance:
For C++ code:
# Compile with optimization flags
g++ -O3 -std=c++17 topk_benchmark.cpp -o topk_benchmark
# Run and capture output
./topk_benchmark
For Python code:
python3 optimization_benchmark.py
For other languages:
# Java
javac MyOptimization.java && java MyOptimization
# Rust
rustc -O optimization.rs && ./optimization
# Go
go build optimization.go && ./optimization
Step 3: Extract Performance Metrics
From execution output, extract:
- Execution time: Wall-clock time, CPU time
- Memory usage: Peak memory, memory delta
- Comparison with baseline: Speedup factor, time difference
- Correctness verification: Test results, accuracy checks
What ships with it
1 file 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 · 389 lines · 47 tokens per session scan A 10564ac4c0c2
code-optimization is a skill published in the GitHub repository bytedance/agentkit-samples (474 stars, last pushed 3d ago), licensed Apache-2.0. It adds 47 tokens to every session and 2,954 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.
Other skills, from other repositories
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
aatmf-t10-confidentiality-breach
AATMF T10 — Integrity & Confidentiality Breach. System prompt extraction, training-data extraction, model-weight leakage, private-key recovery.
ensembl-database
Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.
skill-news-intelligence
A daily industry-news research workflow that gathers in-depth articles and updates from Chinese technology, business, creator, and AI publications. It filters the results by a creator’s field and turns them into a brief with possible content topics.
ai-music
A workflow that turns a topic or script into a finished short video by combining writing, images or video clips, spoken narration, subtitles, background music, and final assembly.
sidewinder-rattlesnake
Adversary-emulation profile for SideWinder (G0121 / Rattlesnake / T-APT-04 / Razor Tiger), India's suspected state-sponsored cyber-espionage actor.