jcode is a command-line coding-agent harness that lets developers run AI-assisted programming sessions while using relatively little memory. It is designed for developers who need efficient single- or multi-session workflows with language-model providers and related tools. The catalogue includes a skill and an instruction for working with jcode.
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 1jehuang/jcode --skill optimizationgit clone --depth 1 https://github.com/1jehuang/jcodeWrote 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/1jehuang/jcode/optimization)<a href="https://agentmods.dev/skills/1jehuang/jcode/optimization"><img src="https://agentmods.dev/badge/skills/1jehuang/jcode/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/1jehuang/jcode/optimization"><img src="https://agentmods.dev/badge/skills/1jehuang/jcode/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 | $0.00052 | $0.00692 |
| Opus 5.5 | $0.00021 | $0.00277 |
| Sonnet 5.5 | $0.00010 | $0.00138 |
| Haiku 4.5 | $0.00005 | $0.00069 |
Grade A, and why
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.
Copies of this mod
3 near-identical copies found in the catalogue:
- optimization — 100% identical, 0 lines differ
- optimization — 100% identical, 0 lines differ
- optimization — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimization
Use this skill when the task is about making a system faster, lighter, more scalable, or otherwise more efficient.
Core principle
To optimize properly, you must know:
- What metrics you are chasing
- What your real bottlenecks are
Do not optimize blindly.
1. Define the target metrics first
Before changing code, make sure you have the right measurements.
- Identify the exact metrics that matter: latency, throughput, memory, CPU, startup time, compile time, query count, token usage, cost, etc.
- Measure comprehensively, not just a convenient subset.
- Make sure the metrics are accurate and representative of the real workload.
- Prefer measurements that are fast to run so you can iterate quickly.
- If possible, create repeatable benchmarks or scripts so improvements are verifiable.
2. Get full bottleneck attribution
You should have strong attribution for what each part of the system is doing.
- Instrument the system so you can see where time and resources are going.
- Prefer both:
- Ad hoc inspection for quick debugging
- Logged measurements for later analysis and comparison
- Attribute work across the full path, not just the obviously slow component.
- Make sure the data is detailed enough to explain where the cost comes from.
If you can analyze runs after the fact with logs or traces, that is often much more powerful than relying only on live inspection.
3. Use static analysis too
Not every optimization problem needs runtime profiling first. Often, code inspection reveals the issue.
Check for:
- Wrong asymptotic complexity
- The wrong algorithm or data structure
- Unnecessary repeated work
- Work happening in the wrong layer
- Inefficient architecture or control flow
- Directionally incorrect approaches
Make sure your asymptotics are right and the overall algorithm makes sense before tuning small details.
4. Macro-optimize before micro-optimizing
Prioritize the largest wins first.
- Remove whole classes of work before making existing work slightly cheaper.
- Fix architecture, batching, caching, query patterns, algorithm choice, parallelism, and data movement before focusing on tiny low-level tweaks.
- If you are very far from the expected metrics, spend more time on macro-optimization.
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 · 85 lines · 52 tokens per session scan A fe0427bb421c
optimization is a skill published in the GitHub repository 1jehuang/jcode (20,877 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 692 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 2026-08-30.
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