agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Wrap getTokenOptimizer().getCompactContext() to retrieve compacted ReasoningBank context for cost-analysis queries; report bridge-reported tokensSaved
$ npx -y skills add ruvnet/claude-flow --skill cost-compact-context --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/cost-compact-contextContext preview
The summary Claude sees to decide when to auto-load this skill.
Wrap getTokenOptimizer().getCompactContext() to retrieve compacted ReasoningBank context for cost-analysis queries; report bridge-reported tokensSaved
name: cost-compact-context description: Wrap getTokenOptimizer().getCompactContext() to retrieve compacted ReasoningBank context for cost-analysis queries; report bridge-reported tokensSaved argument-hint: "<query>" allowed-tools: Bash
Wraps `getTokenOptimizer().getCompactContext()` from `@claude-flow/integration` for cost-analysis queries. The bridge dynamically imports `agentic-flow` with graceful fallback: when the package isn't installed, `tokensSaved` is `0` and the skill exits cleanly. No MCP tool wraps `getTokenOptimizer` today (ADR-0002 §"Riskiest assumption"); we shell a Node one-liner instead.
1. **Take the query** — the single argument. 2. **Invoke** — run from anywhere under `v3/` so `@claude-flow/integration` resolves:
( cd v3 && node ../plugins/ruflo-cost-tracker/scripts/compact.mjs "<QUERY>" )
The script imports `@claude-flow/integration/token-optimizer` (canonical export — **not** `dist/token-optimizer.js`, which would double the `.js` extension via Node's `./*` exports rule), calls `getCompactContext(query)`, and prints a markdown summary plus a JSON line via `COMPACT_QUIET=1`.
3. **Report** — markdown table with: memories retrieved, tokens saved (bridge-reported), agentic-flow availability, cache hit rate. The script also emits a "bridge-reported, not measured against a no-RAG baseline" disclaimer. On bridge-unavailable: prints "agentic-flow not installed — bridge returns inert results." and exits cleanly.
CLAUDE.md root claims `ReasoningBank retrieval: -32%` tokens. The bridge's `tokensSaved` is `query_tokens − compact_prompt_tokens` (token-optimizer.ts:141–143) — a heuristic, **not** a baseline-measured saving. token-optimizer.ts:9–10 itself says: *"No fabricated metrics are reported — all stats reflect real measurements"*. This skill carries that disclaimer forward.
Booster-specific availability is **not** exposed as a getter — observable only through `optimizedEdit()` returning `method: 'agent-booster'`. The canonical Tier 1 signal is `[AGENT_BOOSTER_AVAILABLE]` (see `cost-booster-route`).
`agentic-flow` not installed → `getCompactContext` returns `{tokensSaved: 0, memories: []}` (line 116–124), `optimizedEdit` returns `{method: 'traditional'}`, `getOptimalConfig` falls back to anti-drift defaults. Skill exits cleanly with the "not available" message.
ADR-0002 Decision #2 + §"Riskiest assumption" · `token-optimizer.ts:308` (singleton export) · `docs/benchmarks/0002-baseline.md` (verification findings).
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/claude-flow
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