cost-booster-route
Route tasks through hooks_route, partition by Agent Booster availability, and report Tier 1 bypass utilization with $0 cost
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Newest skills
3209 skills
cost-booster-edit
Apply a simple code transform via agent-booster's WASM engine — sub-millisecond, deterministic, $0 (no LLM call). Companion to cost-booster-route.
cost-benchmark
Run the corpus benchmark — booster locally, optional Gemini/Sonnet/Opus baselines — and persist a verifiable measured-vs-claimed table
cost-anomaly
MAD-based outlier detection on session spend. Robust to the very outliers it hunts (unlike mean+sigma). Surfaces specific anomalous sessions with modified-z scores; optional --alert-on-outliers exit code for CI gates. Distinct from cost-burn (aggregate trend) — this answers "which INDIVIDUAL session is the outlier?".
ruflo-status
Diagnose Ruflo health, then report system, MCP server, and active-agent status without changing the installation
init-project
Initialize a new Ruflo project with MCP tools, hooks, and agent configuration. Use when setting up Ruflo in a fresh repo, or when the user says "init ruflo", "set up ruflo", or asks how to bootstrap the MCP server, hooks, and agent configs from scratch.
discover-plugins
Discover and recommend ruflo plugins based on your workflow, installed MCP tools, and current task
pod-sales
Run one tick of the sales business-pod (ADR-164 §4.1, Phase 2). Loads templates/sales.json, validates it against the pod-schema, resolves agents against ruflo's agent registry, reserves budget via the Phase-2 file-based stub ledger (atomic SQLite tracker is Phase 3 per ADR-164.1), constructs per-agent dry-run prompts, posts a summary envelope to room "sales" via the federation_bbs_publish JSONL backing store, and emits a structured {podName, tickId, agentsRan, totalUsd, envelopeId, status} line for /loop ingestion. Dry-run by default; --live is reserved for Phase 3.
browser-test
UI test recipe -- composes browser-record (capture) + browser-replay (verify) so every test produces a replayable RVF artifact, not an ephemeral run
browser-screenshot-diff
Visual + DOM diff between two recorded sessions at matching trajectory step ids; used for visual regression and replay verification
browser-scrape
DEPRECATED in v0.2.0 -- use browser-extract instead; this is a thin shim for backward compatibility, removed in v0.3.0
browser-replay
Replay a recorded session trajectory against the same URL or a mutated variant; uses browser-selectors embedding similarity to recover from DOM drift
browser-record
Open a named, traced browser session into an RVF cognitive container with a ruvector trajectory recording every action
browser-login
Drive an authentication flow once, sanitize cookies through AIDefence, and vault a reusable cookie handle in browser-cookies for future sessions
browser-intent
Execute a natural-language browser intent via page-agent (browser_act) when the target is easier to describe than to select — degrades gracefully when page-agent or an OpenAI-compatible LLM provider isn't configured
browser-form-fill
Fill a web form by mapping field-name → value, with optional template lookup from browser-templates for known forms
browser-extract
Extract structured data via stored browser-templates or one-shot DOM queries, with mandatory AIDefence PII + prompt-injection gates before content reaches the model
browser-auth-flow
Probe a site's authentication flow for redirect leaks, missing CSRF, weak session cookies, and OAuth misconfiguration; produces an auth findings.md
cross-host-federation
Join and operate a signed cross-host agentbbs federation, and coordinate work claims across nodes. Use when: connecting ruflo agents across machines, sharing status/tasks/results between hosts, propagating work claims across a swarm, or standing up a federation hub. Skip when: single-host local work with no other nodes to coordinate with.
autopilot-predict
Use learned patterns and current state to predict the optimal next action
autopilot-loop
Run an autonomous /loop iteration -- check progress, work on next task, schedule next wake
safety-scan
Scan inputs for prompt injection, unsafe content, and adversarial attacks using AIDefence. Use when processing untrusted input (user submissions, API payloads, webhook data, tool outputs) before passing it to a model or executing it.