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 agentmods add skills/jamesgray-ai/handsonai-plugins/analyzenpx skills add jamesgray-ai/handsonai-plugins --skill analyzegit clone --depth 1 https://github.com/jamesgray-ai/handsonai-pluginsWrote 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/jamesgray-ai/handsonai-plugins/analyze)<a href="https://agentmods.dev/skills/jamesgray-ai/handsonai-plugins/analyze"><img src="https://agentmods.dev/badge/skills/jamesgray-ai/handsonai-plugins/analyze.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.03517 |
| Opus 5 | $0.00039 | $0.01758 |
| Sonnet 5 | $0.00015 | $0.00703 |
| Haiku 4.5 | $0.00008 | $0.00352 |
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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Workflows
Analyze concrete opportunities where AI can improve your workflows. Produces a categorized opportunity report with a summary table, detailed opportunity cards, and a structured workflow candidate list.
Workflow
Set expectations up front (first message): tell the user this step is a guided interview that usually takes 15–30 minutes, and that stopping early is safe — everything gets saved to a file they can pick up from later.
Registry entry: the workflow's registry entry is its Workflow concept node in the workspace's
registry/bundle — seeindexing-registry/references/registry-bundle.md(in this plugin) for resolution, write rules, and your fields. If the workspace has noregistry/SCHEMA.md, offer thescaffolding-registryskill first (it also migrates legacyworkflow.yamlworkspaces); do not write registry entries until the bundle exists.
Resume orientation: if the user says "continue my workflow" (or similar), they're returning mid-journey — check registry/workflows/ for existing Workflow nodes, and outputs/ for their artifacts. If one exists, orient them ("You finished Step [N] ([name]) — next is Step [N+1]") and route to that skill instead of starting a new analysis. Run Analyze only for finding new opportunities.
Fast Path
If the user arrives with pre-identified workflows (e.g., "I already know I want to automate X, Y, and Z"), skip Steps 1-2. Infer the lens from what they describe — individual tasks (personal reporting, email triage) = Individual lens; multi-role or business-objective workflows (customer onboarding, sales pipeline) = Organizational lens. Confirm the inferred lens with the user. Go straight to Step 3 (Opportunity Analysis & Report) using what they've provided, then Step 4 (Workflow Candidate Summary).
Standard Path
Work through four steps in order:
Step 1 — Memory & History Scan
Before asking any questions, review everything you already know about the user from conversation history, memory, project files, or any other available context.
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.
- 5d ago First seen · 235 lines · 77 tokens per session scan A e3a73b08edbf
analyze is a skill published in the GitHub repository jamesgray-ai/handsonai-plugins (8 stars, last pushed 24d ago), licensed MIT. It adds 77 tokens to every session and 3,517 once invoked, about $0.0004 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.
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