/cost-summary
Single-shot programmatic dump of all cost data — total spend, per-tier, top session, budget status, federation aggregate. JSON or markdown.
$ npx -y skills add ruvnet/ruflo --skill cost-summary --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/cost-summary
Context preview
The summary Claude sees to decide when to auto-load this skill.
Single-shot programmatic dump of all cost data — total spend, per-tier, top session, budget status, federation aggregate. JSON or markdown.
SKILL.md
cost-summary.SKILL.mdname: cost-summary
description: Single-shot programmatic dump of all cost data — total spend, per-tier, top session, budget status, federation aggregate. JSON or markdown.
argument-hint: "[--format json|markdown]"
allowed-tools: Bash
Cost Summary
A stable single-call interface that other plugins / scripts / dashboards can shell out to and parse. ADR-0002 considered exposing `cost_report` / `cost_summary` as proper MCP tools but **deferred that** — adding MCP tools requires modifying `@claude-flow/cli` source, out of scope for plugin-local work. This script is the equivalent: same data, exposed via a stdout JSON contract.
When to use
- Another plugin needs a snapshot of cost state — it shells out to `summary.mjs --format json` and parses.
- A dashboard / Slackbot fetches a one-line cost rollup.
- Quick "where am I right now?" view that pulls from every cost-tracker source (cost-tracking + federation-spend).
Output contract (JSON, stable)
{
"exportedAt": "<ISO>",
"total_cost_usd": 1546.36,
"sessionCount": 1,
"conversationCount": 1,
"byTier": { "haiku": 0, "sonnet": 0, "opus": 1546.36, "unknown": 0 },
"byModel": {
"claude-opus-4-7": {
"tier": "opus",
"cost_usd": 1546.36,
"messages": 1597,
"input_tokens": 3090,
"output_tokens": 3295940,
"cache_creation_input_tokens": 10659599,
"cache_read_input_tokens": 732833690
}
},
"topSession": {
"sessionId": "1dba3b8c-...",
"total_cost_usd": 1546.36,
"messageCount": 1597
},
"budget": {
"budget_usd": 2500.00,
"setAt": "<ISO>",
"spent_usd": 1546.36,
"utilization": 0.6185,
"level": "INFO"
},
"federation": {
"eventCount": 0,
"peerCount": 0,
"totalUsd24h": 0
}
}Steps
# Markdown (default)
node plugins/ruflo-cost-tracker/scripts/summary.mjs
# JSON for programmatic consumption
node plugins/ruflo-cost-tracker/scripts/summary.mjs --format json
Optional env: `SUMMARY_NAMESPACE=cost-tracking`, `SUMMARY_FED_NAMESPACE=federation-spend`, `SUMMARY_FORMAT=json`.
Cross-references
- `cost-report` — narrative report (per-agent / per-model lens; uses same data)
- `cost-export` — pushes to Prometheus / webhook (this skill is pull-style; export is push-style)
- ADR-0002 §"Decision" — explicitly defers proper MCP-tool registration to a future ADR
Read more
name: cost-summary description: Single-shot programmatic dump of all cost data — total spend, per-tier, top session, budget status, federation aggregate. JSON or markdown. argument-hint: "[--format json|markdown]" allowed-tools: Bash
Cost Summary
A stable single-call interface that other plugins / scripts / dashboards can shell out to and parse. ADR-0002 considered exposing `cost_report` / `cost_summary` as proper MCP tools but **deferred that** — adding MCP tools requires modifying `@claude-flow/cli` source, out of scope for plugin-local work. This script is the equivalent: same data, exposed via a stdout JSON contract.
When to use
- Another plugin needs a snapshot of cost state — it shells out to `summary.mjs --format json` and parses.
- A dashboard / Slackbot fetches a one-line cost rollup.
- Quick "where am I right now?" view that pulls from every cost-tracker source (cost-tracking + federation-spend).
Output contract (JSON, stable)
{
"exportedAt": "<ISO>",
"total_cost_usd": 1546.36,
"sessionCount": 1,
"conversationCount": 1,
"byTier": { "haiku": 0, "sonnet": 0, "opus": 1546.36, "unknown": 0 },
"byModel": {
"claude-opus-4-7": {
"tier": "opus",
"cost_usd": 1546.36,
"messages": 1597,
"input_tokens": 3090,
"output_tokens": 3295940,
"cache_creation_input_tokens": 10659599,
"cache_read_input_tokens": 732833690
}
},
"topSession": {
"sessionId": "1dba3b8c-...",
"total_cost_usd": 1546.36,
"messageCount": 1597
},
"budget": {
"budget_usd": 2500.00,
"setAt": "<ISO>",
"spent_usd": 1546.36,
"utilization": 0.6185,
"level": "INFO"
},
"federation": {
"eventCount": 0,
"peerCount": 0,
"totalUsd24h": 0
}
}Steps
# Markdown (default) node plugins/ruflo-cost-tracker/scripts/summary.mjs # JSON for programmatic consumption node plugins/ruflo-cost-tracker/scripts/summary.mjs --format json
Optional env: `SUMMARY_NAMESPACE=cost-tracking`, `SUMMARY_FED_NAMESPACE=federation-spend`, `SUMMARY_FORMAT=json`.
Cross-references
- `cost-report` — narrative report (per-agent / per-model lens; uses same data)
- `cost-export` — pushes to Prometheus / webhook (this skill is pull-style; export is push-style)
- ADR-0002 §"Decision" — explicitly defers proper MCP-tool registration to a future ADR
An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.
Repo: ruvnet/ruflo
Other skills on claude-flow.
- /agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Open skill - /agentdb-learning
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
Open skill - /agentdb-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Open skill - /agentdb-optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
Open skill - /agentdb-vector-search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Open skill - /agentic-jujutsu
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Open skill

