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THE AGENSI STORE
36 skills found
An adversarial reviewer for Dockerfiles and container builds. It flags root users, image bloat, unpinned or cache-busting layers, leaked secrets, and missing hardening, then returns a PASS / FIX / BLOCK verdict — before you build or push the image.
Audit your project's dependencies for supply-chain risk before they ship. Detects the ecosystem, runs the right vulnerability scanners against live advisory data, and adds the checks tooling misses — outdated or abandoned packages, typosquatted or suspicious names, risky install scripts, and license conflicts — then returns a prioritized fix list and a PASS / REVIEW / BLOCK verdict. It's npm audit with triage and judgment on top.
List your AI subscriptions and usage; it finds the overlap and returns a cut/keep/downgrade verdict with estimated monthly dollar savings.
An adversarial reviewer for cold emails, follow-ups, and outreach sequences. It flags weak subject lines, unclear asks, spam-filter triggers, broken or generic personalization, and pushy tone, then returns a SEND / REVISE / HOLD verdict — before you hit send.
Audit AI-assisted medical and pharma content for compliance-readiness before it enters formal MLR review or journal submission. It checks claim substantiation and on-label scope, reference integrity (the acute AI risk: fabricated or misrepresented citations), fair balance and safety, AI-use disclosure, ICMJE authorship and GPP, COI and funding, data integrity and patient privacy, and adverse-event flags — then returns a PASS / REVISE / BLOCK verdict with the must-fix list. A readiness pre-check built for the regulated reality of medical communications — not a replacement for formal review.
A senior WordPress security auditor that reasons about WP-API taint flow — not regex hits — to find the 8 real plugin/theme vulnerability classes a generic scanner misses, and returns scored findings with ready-to-merge before→after patches.
Review a database schema, queries, or migration for the mistakes that get expensive in production — bad table design, missing or wrong indexes, slow and N+1 queries, SQL injection, and migrations that lock or break prod. Engine-aware (PostgreSQL, MySQL, SQLite, SQL Server), it runs an ordered review and returns a PASS/REVIEW/BLOCK verdict with prioritized fixes. Schema mistakes are the most expensive kind — this catches them before they ship.
Scaffold a secure, spec-compliant MCP server from a description of the tools you want to expose. Sets up the official SDK (TypeScript or Python/FastMCP), defines tools/resources/prompts with strict JSON Schema, wires the right transport (stdio or Streamable HTTP), adds OAuth 2.1 for remote, and hardens against the MCP-specific footguns — prompt injection via tool output, token passthrough, over-broad scopes, command/path/SSRF injection, leaked secrets — before it ships. Returns a runnable skeleton plus a security checklist. Built by someone who's shipped production MCP servers.
Audit any page, article, or doc for how likely AI answer engines — ChatGPT, Perplexity, Google AI Overviews, Claude — are to surface and cite it. Checks answer-first extractability, question-shaped structure, quotable units, factual specificity, sourcing, schema, and freshness, then returns a PASS/REVISE verdict with the prioritized fixes. This is Answer Engine Optimization, not classic SEO — it optimizes content to be quoted inside AI answers.
A retrieval architect that diagnoses why RAG returns confident-but-wrong answers, picks the right context architecture (RAG vs knowledge graph vs structured/temporal retrieval) instead of defaulting to vector search, and designs the institutional-memory schema embeddings throw away.
by Shandra
Automates repetitive e-commerce support for order tracking, shipping explanations, returns, exchanges, refunds, damaged items, wrong items, missing items, and escalation routing.
A pre-submission gate for medical and scientific manuscripts. It audits your near-final draft, abstract, poster, or congress submission for the disclosures journals now screen on intake — AI/LLM-use disclosure (ICMJE-aligned), authorship against the four ICMJE criteria, conflict-of-interest and funding statements, and data availability — then returns a PASS/REVISE verdict naming every gap, plus a ready-to-paste AI-use disclosure statement. Built by a CMPP-certified medical writer.