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 KarmaloopAI/Jiva --skill implementationgit clone --depth 1 https://github.com/KarmaloopAI/JivaWrote 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/karmaloopai/jiva/implementation)<a href="https://agentmods.dev/skills/karmaloopai/jiva/implementation"><img src="https://agentmods.dev/badge/skills/karmaloopai/jiva/implementation/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/karmaloopai/jiva/implementation"><img src="https://agentmods.dev/badge/skills/karmaloopai/jiva/implementation.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 | $0.00037 | $0.00281 |
| Opus 5.5 | $0.00015 | $0.00112 |
| Sonnet 5.5 | $0.00007 | $0.00056 |
| Haiku 4.5 | $0.00004 | $0.00028 |
Grade A, and why
feature-implementation 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.
What it actually says
Feature Implementation Skill
Overview
Write high-quality, production-ready code for features and bug fixes.
Workflow
- Understand Requirements: Parse task requirements thoroughly
- Plan Implementation: Identify files to create/modify
- Write Code: Implement with best practices:
- Clean, readable code
- Proper error handling
- Type safety
- Good variable/function names
- Verify: Test basic functionality
- Document: Add comments for complex logic
Code Quality Standards
- Follow language idioms and conventions
- Handle edge cases and errors
- Use appropriate data structures
- Keep functions focused and small
- Add inline comments for complex logic
Tools to Use
create_file: For new filesedit_file: For modificationsview: To read existing codegrep_search: To find code patternsrun_terminal_command: To run tests
Best Practices
- Read existing code before making changes
- Match the project's style and patterns
- Test changes when possible
- Leave code better than you found it
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 · 46 lines · 37 tokens per session scan A 9461bb158047
feature-implementation is a skill published in the GitHub repository KarmaloopAI/Jiva (35 stars, last pushed 9d ago), licensed MIT. It adds 37 tokens to every session and 281 once invoked, about $0.0001 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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