agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
5-dimension harness readiness scorecard from `metaharness score <path>`. Returns harnessFit / compileConfidence / taskCoverage / toolSafety / memoryUsefulness + estCostPerRunUsd + scaffoldReady. Pure-read; subprocess invocation; degrades gracefully when MetaHarness is absent
$ npx -y skills add ruvnet/ruflo --skill harness-score --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/harness-scoreContext preview
The summary Claude sees to decide when to auto-load this skill.
5-dimension harness readiness scorecard from `metaharness score <path>`. Returns harnessFit / compileConfidence / taskCoverage / toolSafety / memoryUsefulness + estCostPerRunUsd + scaffoldReady. Pure-read; subprocess invocation; degrades gracefully when MetaHarness is absent
name: harness-score description: 5-dimension harness readiness scorecard from `metaharness score <path>`. Returns harnessFit / compileConfidence / taskCoverage / toolSafety / memoryUsefulness + estCostPerRunUsd + scaffoldReady. Pure-read; subprocess invocation; degrades gracefully when MetaHarness is absent (ADR-150 architectural constraint). argument-hint: "[--path .] [--alert-on-fit-below 70] [--format table|json]" allowed-tools: Bash
Surfaces the upstream `metaharness score` CLI as a ruflo skill. Use when Claude Code needs to assess whether a repo is ready for harness adoption before recommending the user run `npx ruflo init` or `harness-mint`.
Implementation: [`scripts/score.mjs`](../../scripts/score.mjs).
1. Shell out to `npx metaharness score <path> --json` (single subprocess, 60s hard timeout). 2. Parse the JSON shape: `{ harnessFit, compileConfidence, taskCoverage, toolSafety, memoryUsefulness, estCostPerRunUsd, recommendedMode, archetype, template, scaffoldReady, hardConstraints }`. 3. If `--alert-on-fit-below N`: exit 1 when `harnessFit < N`. 4. Output JSON (default) or markdown table.
| Dimension | Value | |---|---:| | harnessFit | 82/100 | | compileConfidence | 100 | | taskCoverage | 79 | | toolSafety | 100 | | memoryUsefulness | 40 | | estCostPerRunUsd | $0.048 | | recommendedMode | CLI + MCP | | archetype | typescript-sdk-harness | | template | vertical:coding | | scaffoldReady | true |
Ruflo passes its own readiness check. `memoryUsefulness: 40` is the weakest dimension — track this as a leading indicator for future memory work in the AgentDB layer.
node plugins/ruflo-metaharness/scripts/score.mjs --alert-on-fit-below 70 --format json
Exit 1 fails the build. Pair with `harness-genome` for the full 7-section view.
When `metaharness` is not installed and `npx` can't fetch it (offline, no network, registry unreachable), the script emits:
{
"degraded": true,
"reason": "metaharness-not-available",
"hint": "Install with `npm i -D metaharness@~0.3.0` (pinned range — this plugin never fetches @latest) or verify network access for the one-time cache install."
}and exits 0. Ruflo continues to function — this is the architectural constraint in action.
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