agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Replay a recorded session trajectory against the same URL or a mutated variant; uses browser-selectors embedding similarity to recover from DOM drift
$ npx -y skills add ruvnet/ruflo --skill browser-replay --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/browser-replayContext preview
The summary Claude sees to decide when to auto-load this skill.
Replay a recorded session trajectory against the same URL or a mutated variant; uses browser-selectors embedding similarity to recover from DOM drift
name: browser-replay description: Replay a recorded session trajectory against the same URL or a mutated variant; uses browser-selectors embedding similarity to recover from DOM drift argument-hint: "<session-id> [--url <new-url>] [--mutate <json>] [--tolerance <0..1>]" allowed-tools: mcp__plugin_ruflo-core_ruflo__browser_open mcp__plugin_ruflo-core_ruflo__browser_close mcp__plugin_ruflo-core_ruflo__browser_click mcp__plugin_ruflo-core_ruflo__browser_fill mcp__plugin_ruflo-core_ruflo__browser_type mcp__plugin_ruflo-core_ruflo__browser_press mcp__plugin_ruflo-core_ruflo__browser_select mcp__plugin_ruflo-core_ruflo__browser_check mcp__plugin_ruflo-core_ruflo__browser_uncheck mcp__plugin_ruflo-core_ruflo__browser_hover mcp__plugin_ruflo-core_ruflo__browser_wait mcp__plugin_ruflo-core_ruflo__browser_screenshot mcp__plugin_ruflo-core_ruflo__browser_snapshot mcp__plugin_ruflo-core_ruflo__browser_eval Bash Read
Re-drive a recorded session trajectory. Used for regression testing, deterministic re-runs, and as the verification path that `browser-record` plus `browser-selectors` actually produces something replayable.
> **This skill is the load-bearing assumption of the v0.2.0 architecture.** ADR-0001 Verification §4 requires ≥80% replay success across 10 distinct sites of varying drift profiles before the proposal moves from `Proposed` → `Accepted`. If you find replay unreliable, capture the failure modes in `findings.md` and report them up the ADR.
1. **Locate the source session**:
npx -y ruvector@0.2.25 rvf status <session-id>.rvf
2. **Load the trajectory**:
Read .../trajectory.ndjson
Each line is `{ts, action, args, selector, result}`. 3. **Open a fresh browser** via `mcp__plugin_ruflo-core_ruflo__browser_open` (target URL = original or `--url` override). 4. **For each trajectory step**, dispatch the matching MCP tool (`browser_click`, `browser_fill`, `browser_eval`, etc.) with the recorded args. 5. **On selector miss**, do *not* fail immediately — query the `browser-selectors` namespace for an embedding-similar selector for the same `<host>:<intent>` and retry once:
npx -y @claude-flow/cli@latest memory search --namespace browser-selectors \
--query "<host> <intent>" --limit 56. **Record a new trajectory** for the replay run (allocate a fresh RVF container, lineage-tracked via `rvf derive`). 7. **Verdict**: tally matched-step / total-step ratio. Default tolerance threshold is 0.85 (configurable via `--tolerance`). Verdict goes into `findings.md`.
An agent meta-harness for Claude Code and Codex. 📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification.
Repo: ruvnet/ruflo
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and…
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use…
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing…
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG…
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination