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 agentmods add commands/basher83/agent-auditor/jina-searchgit clone --depth 1 https://github.com/basher83/agent-auditorWrote 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/commands/basher83/agent-auditor/jina-search)<a href="https://agentmods.dev/commands/basher83/agent-auditor/jina-search"><img src="https://agentmods.dev/badge/commands/basher83/agent-auditor/jina-search.svg" alt="Measured on agentmods" 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 | $0.00015 | $0.00358 |
| Opus 5 | $0.00008 | $0.00179 |
| Sonnet 5 | $0.00003 | $0.00072 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
jina-search 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 6d 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
Jina Search Command
Search the web and academic sources using Jina MCP tools and automatically read results.
Instructions
Use Jina MCP tools to search and read best practices and latest information:
- Web Search: Use
search_webfor general queries - Academic Search: Use
search_arxivfor theoretical deep learning or algorithm details - Always Read Results:
search_webandsearch_arxivcannot be used alone - always combine withread_urlorparallel_read_urlto read source content - Efficiency: Use
parallel_*versions of search and read when processing multiple sources
Query
Search for: $ARGUMENTS
Workflow
- Execute appropriate search (web or arXiv based on query)
- Read returned URLs using
read_urlorparallel_read_url - Synthesize findings from multiple sources
- Provide comprehensive answer with source citations
Output
Create a comprehensive markdown file in docs/research/ with your findings:
- Rank findings by relevance - Most important information first
- Remove duplicate information - Consolidate similar points
- Target audience: Developers - Technical depth appropriate for engineering teams
- Include actionable insights - Practical recommendations developers can implement
- Cite all sources - Link back to original URLs for verification
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.
- 6d ago First seen · 40 lines · 15 tokens per session scan A 94b1c0787212
jina-search is a command published in the GitHub repository basher83/agent-auditor (5 stars, last pushed 6mo ago), licensed MIT. It adds 15 tokens to every session and 358 once invoked, about $0.0001 per session on Opus 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.