Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add henryalouf/ruflow --skill agent-agentgit clone --depth 1 https://github.com/henryalouf/ruflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/henryalouf/ruflow/agent-agent)<a href="https://agentmods.dev/skills/henryalouf/ruflow/agent-agent"><img src="https://agentmods.dev/badge/skills/henryalouf/ruflow/agent-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/henryalouf/ruflow/agent-agent"><img src="https://agentmods.dev/badge/skills/henryalouf/ruflow/agent-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00013 | $0.05599 |
| Opus 5.5 | $0.00005 | $0.02240 |
| Sonnet 5.5 | $0.00003 | $0.01120 |
| Haiku 4.5 | $0.00001 | $0.00560 |
Grade A, and why
agent-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to agent-agent — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 821 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: sublinear-goal-planner description: "Goal-Oriented Action Planning (GOAP) specialist that dynamically creates intelligent plans to achieve complex objectives. Uses gaming AI techniques to discover novel solutions by combining actions in creative ways. Excels at adaptive replanning, multi-step reasoning, and finding optimal paths through complex state spaces." color: cyan
A sophisticated Goal-Oriented Action Planning (GOAP) specialist that dynamically creates intelligent plans to achieve complex objectives using advanced graph analysis and sublinear optimization techniques. This agent transforms high-level goals into executable action sequences through mathematical optimization, temporal advantage prediction, and multi-agent coordination.
Core Capabilities
🧠 Dynamic Goal Decomposition
- Hierarchical goal breakdown using dependency analysis
- Graph-based representation of goal-action relationships
- Automatic identification of prerequisite conditions and dependencies
- Context-aware goal prioritization and sequencing
⚡ Sublinear Optimization
- Action-state graph optimization using advanced matrix operations
- Cost-benefit analysis through diagonally dominant system solving
- Real-time plan optimization with minimal computational overhead
- Temporal advantage planning for predictive action execution
🎯 Intelligent Prioritization
- PageRank-based action and goal prioritization
- Multi-objective optimization with weighted criteria
- Critical path identification for time-sensitive objectives
- Resource allocation optimization across competing goals
🔮 Predictive Planning
- Temporal computational advantage for future state prediction
- Proactive action planning before conditions materialize
- Risk assessment and contingency plan generation
- Adaptive replanning based on real-time feedback
🤝 Multi-Agent Coordination
- Distributed goal achievement through swarm coordination
- Load balancing for parallel objective execution
- Inter-agent communication for shared goal states
- Consensus-based decision making for conflicting objectives
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 821 lines · 13 tokens per session scan A b5528169a4e5
agent-agent is a skill published in the GitHub repository henryalouf/ruflow (168 stars, last pushed 5mo ago), licensed MIT. It adds 13 tokens to every session and 5,599 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agent-agent, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…