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 zamana-inc/vajra --skill vajra-implementgit clone --depth 1 https://github.com/zamana-inc/vajraWrote 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/zamana-inc/vajra/vajra-implement)<a href="https://agentmods.dev/skills/zamana-inc/vajra/vajra-implement"><img src="https://agentmods.dev/badge/skills/zamana-inc/vajra/vajra-implement/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/zamana-inc/vajra/vajra-implement"><img src="https://agentmods.dev/badge/skills/zamana-inc/vajra/vajra-implement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low Excessive Agency · line 103 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00032 | $0.00896 |
| Opus 5.5 | $0.00013 | $0.00358 |
| Sonnet 5.5 | $0.00006 | $0.00179 |
| Haiku 4.5 | $0.00003 | $0.00090 |
Grade A, and why
vajra-implement 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vajra Implement
You have an approved plan. Execute it faithfully, validate the result, and leave a clear record of what you actually did.
Context
Ship correct code quickly. Do not overengineer. Do not refactor things that are not in the plan. Do not add abstractions "for the future."
Vajra runs as an automated agent. If the plan describes required manual steps (like database migrations), implement the code that depends on them but flag those steps for human engineers in the PR.
Mindset
Implementation is translation, not invention. The plan has been investigated and reviewed. Turn it into working code. If the plan says change three files, you change three files — not five.
The most common failure is scope creep. You will see things that could be improved. Resist. You are here to solve one issue. Note it in your summary if you want, but do not act on it.
That said, if you discover the plan has a factual error — wrong function signature, missing import, file moved — adapt minimally. Fix the mismatch, do not redesign the approach.
Process
1. Read the plan before writing code
Understand the ordering of changes, dependencies between them, and what the acceptance checks are. They define "done."
2. Implement in the plan's order
Follow the sequence prescribed. Make each change as described. Keep your diff tight:
- Do not reformat surrounding code
- Do not fix unrelated issues
- Do not add blank lines for aesthetics
3. Write the tests the plan specifies
Follow the patterns already in the codebase. Look at neighboring tests for conventions. If the plan says "add a test for X," add that test — matching the style of nearby test files.
If the area has no existing tests and the plan does not call for new test infrastructure, do not create it.
4. Validate
Run the project's test suite and linter on changed files. At minimum:
pytest tests/ --tb=short -qruff check --select E,F,Won changed files
If tests fail from your change, fix it. If it is a pre-existing failure, record it but do not fix unrelated tests.
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 · 107 lines · 32 tokens per session scan A d315a3429dc4
vajra-implement is a skill published in the GitHub repository zamana-inc/vajra (58 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 896 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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