StarNose

Adversarial-test your agent against prompt injection — indirect injection probes across tool outputs, webpages, and files, with defense scoring.

Make your agent learn from corrections — capture mistakes, extract durable rules, regression-test them, and store them where they stick.

Stop drowning your agent in context — budget every token across instructions, tools, and history with a real allocation model and compaction strategy.

Design human approval gates for agents — what needs sign-off, escalation tiers, interrupt/resume, and audit trails that satisfy compliance.

Keep agent runs on budget — token/cost budgets, model-tier routing, burn-rate alerts, and per-task cost attribution.

Turn a repeated workflow into a shippable agent skill — guided interview, SKILL.md draft, evals, and marketplace packaging with pricing.

Build bulletproof webhook receivers for agent connectors — signature verification, idempotency, retries, dead-lettering, and event routing.

Add OAuth 2.0 to agent connectors the right way — PKCE, token refresh, secure storage, and provider quirks for Google, GitHub, Slack, and more.

Turn any REST API into MCP tools automatically — feed it an OpenAPI spec or endpoint list, get a working MCP server with clean tool schemas.

Scaffold a production-ready MCP server from a plain-English tool description — Python (FastMCP) or TypeScript SDK, with auth, errors, and packaging.

A daily support operator for hard times — gentle check-ins, local alerts, a verified support directory, sleep nudges, a panic protocol, and real support groups

Design a persistent AI companion — partner, lover, or friend — through a warm interview, daily rituals, living memory, and honest repair

Turn scattered prompts into a versioned, reusable library.

Turn messy meeting notes into owned action items.

Build a phased migration runbook with rollback triggers.

Turn any meeting invite into a sharp 5-minute prep brief

Draft honest customer-facing incident updates.

Clean up messy branch history before review or merge.

Ship a reusable prompt eval harness with golden sets

Audit Python dependencies for staleness and license risks.

Audit and clean messy CSVs with a full change report

API contract review: ranked findings plus a clear verdict.

Builds a custom code-review checklist for YOUR stack. Use when "review the code" means different things on every team: answer a short questionnaire about language, framework, and team conventions, get a checklist of the pitfalls that actually bite your stack (plus your team's own rules), and reuse i

Turns a throwaway Python script into a proper installable CLI. Use when a script that "just works on my machine" needs to survive other people: it audits the script for CLI-readiness (argparse, logging, exit codes, main guard, packaging), tells you exactly what's missing, and walks the conversion st

Makes the severity call in the first 5 minutes of a production incident. Use when something is broken in prod and people are arguing about how bad it is: a SEV1–SEV4 rubric with concrete examples, a 5-minute triage script, who to page at each level, and comms templates so customers hear one clear vo

Learns YOUR writing voice from a sample and builds a reusable voice profile. Use when AI drafts don't sound like you: feed it something you wrote, get a voice DNA card (rhythm, diction, signature moves, tics), then use the rewrite checklist to bend any draft toward your voice without sanding it into

Turns a feature spec into a test plan with a traceability matrix. Use when a spec exists and someone needs to prove it works: every requirement gets test cases, every test case traces back to a requirement, edge cases are generated systematically, and acceptance criteria are explicit before a line o

Turns a pasted stack trace or error log into a plain-English diagnosis. Use when something crashed and the traceback looks like alphabet soup: it extracts what broke, where, and the ranked likely causes, then tells you exactly what to check next and what to paste back if you're still stuck.

Turns a question into a decision-grade research report. Use when research spans sources, claims compete, or a consequential decision needs evidence. Clarifies the real decision, searches primary and independent sources, verifies key facts against two sources, separates fact from inference, and deliv

Builds structured handoff packets so work survives passing between agents, sessions, or teammates. Use when one agent's work continues in another agent, when a long task spans sessions, or when handing work to a person. Captures objective, state, decisions, open questions, and the single next action

Regression-tests an agent skill before and after you change it, so updates never silently break what worked. Captures baseline behavior, re-runs after your edit, and diffs the results automatically — regressions show up as data, not user complaints.

Runs AI-generated work through an adversarial acceptance gate before you use it. Use when an agent hands you code, a document, a plan, research, or data and you need to know whether to trust it. Maps every claim to its evidence, attacks the weak points, and returns an accept/rework verdict with seve