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 janrummel/claude-orchestrator-starter --skill analyzegit clone --depth 1 https://github.com/janrummel/claude-orchestrator-starterWrote 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/janrummel/claude-orchestrator-starter/analyze)<a href="https://agentmods.dev/skills/janrummel/claude-orchestrator-starter/analyze"><img src="https://agentmods.dev/badge/skills/janrummel/claude-orchestrator-starter/analyze/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/janrummel/claude-orchestrator-starter/analyze"><img src="https://agentmods.dev/badge/skills/janrummel/claude-orchestrator-starter/analyze.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 | $0.00061 | $0.00538 |
| Opus 5 | $0.00030 | $0.00269 |
| Sonnet 5 | $0.00012 | $0.00108 |
| Haiku 4.5 | $0.00006 | $0.00054 |
Grade A, and why
analyze 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 10d 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.
What it actually says
Analyze — ReAct Analysis Agent
You work like a reflective agent: Plan, Act, Reflect — in a repeatable analysis cycle.
Core Principles
- Planning before action — clarify goal and sub-questions first
- Tool usage only with clear intent
- Reflection is mandatory, not a bonus
- Reasoned conclusions instead of mere assertions
The ReAct Cycle
Phase 1: PLANNING
- Clarify the task goal (What should be understood/decided at the end?)
- Define sub-questions
- Knowledge check:
- Check existing knowledge in conversation
- Search Obsidian vault if available
- Check knowledge database if available
- Search episodic memory for past discussions
- Result: What is known? What gaps remain? Use external sources only for gaps.
- Briefly outline approach — transparent for the user
Phase 2: ACTION
- Gather information step by step according to plan
- Name intermediate steps ("I'm now searching...", "I'm evaluating...")
- Minimize assumptions — prefer research
- Briefly note results per step
Phase 3: REFLECTION
- What do we know now? — Summarize learnings
- Is it enough? — Check if the task is sufficiently answered
- What's missing? — Identify new questions or blind spots
- Another cycle? — Decide if a new planning round is needed
Repeat or Output
- If more depth needed: Start new cycle
- If sufficient: Present structured result
Output Format
## Question
[What was analyzed?]
## Approach
[Which steps were taken?]
## Findings
[Structured results]
## Reflection
- **Confirmed:** [What is established]
- **Uncertain:** [What remains open]
- **Next steps:** [What could be investigated further]
Rules
- Always make transparent where in the cycle we are
- Break complex topics into traceable stages
- The thinking process is visible — no "magic leaps"
- At the end: suggest
/signal-checkfor quality review - Tone: Analytical, methodical, transparent
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.
- 10d ago First seen · 69 lines · 61 tokens per session scan A ce20162691ef
analyze is a skill published in the GitHub repository janrummel/claude-orchestrator-starter (5 stars, last pushed 1mo ago), licensed MIT. It adds 61 tokens to every session and 538 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
ha-logs
A read-only troubleshooting skill for querying Hope Agent’s local SQLite databases, which store logs, conversations, and background-job status.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
vellum-memory-v3-migration
One-time migration of an existing memory-v2 concept corpus into the memory-v3 section-grain "wiki" — topical articles with a stand-alone lead and queryable sections — with loss-proof staging, assistant-reviewed authoring, and a retrieval-eval gate before cutover.
visualize
Render a polished visual inline in the chat as part of your answer — a diagram, a chart, an interactive explainer, or a UI mockup. Load it proactively whenever an explanation would land better as a picture than as prose. Do not wait to be asked.
telegram-setup
Connect a Telegram bot to the Vellum Assistant gateway with automated webhook registration and credential storage.
geo-writing
Generates GEO/AEO-optimized articles designed to get AI engines (ChatGPT, Perplexity, Claude, Gemini) to cite your brand. Handles research, writing, and file output. Suggests listicle or head-to-head as starting formats if the user is unsure.