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/dataviking-tech/althing/pricing-probenpx skills add DataViking-Tech/Althing --skill pricing-probegit clone --depth 1 https://github.com/DataViking-Tech/AlthingWhat 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 | $0.00040 | $0.01244 |
| Opus 5 | $0.00020 | $0.00622 |
| Sonnet 5 | $0.00008 | $0.00249 |
| Haiku 4.5 | $0.00004 | $0.00124 |
Grade A, and why
pricing-probe 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 2d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are running a pricing sensitivity probe using the althing MCP tools and the bundled pricing-discovery v3 branching instrument.
What You Do
You help the user understand how a target audience reasons about price for a product or service. The pricing-discovery instrument is adaptive: it lets each panelist's discovery round drive the probe path — into pain, pricing, or alternatives — so you get signal on whichever dimension actually matters to them.
- Frame the problem — what are we pricing, for whom, and against what alternatives?
- Assemble a target-audience panel.
- Run the
pricing-discoverypack viarun_panel— with the{problem}placeholder filled in viavars(see below). - Interpret the branch — the panel that routed through
probe_painis telling you something different than one that routed throughprobe_pricingorprobe_alternatives.
Available MCP Tools
mcp__althing__run_panel— Primary tool. Thepricing-discoveryinstrument's opening question contains a{problem}placeholder. Fill it with thevarsargument: passinstrument_pack="pricing-discovery"together withvars={"problem": "..."}. (CLI equivalent:althing panel run --instrument pricing-discovery --var problem='...'.) If you omitvars, the call fails fast with a typedINVALID_TOOL_ARGerror naming the missing placeholder — it never sends literal{problem}to panelists.mcp__althing__get_instrument_pack/mcp__althing__list_instrument_packs— Inspect the bundled pricing-discovery pack (e.g. to see which placeholders it declares).mcp__althing__list_persona_packs/mcp__althing__get_persona_pack— Load a saved target-audience pack.mcp__althing__run_quick_poll— Use for a narrow follow-up question after the main run (e.g. "Would $X/month feel fair?").
Workflow
Step 1: Clarify the Pricing Context
Ask:
- What problem does the product solve? (The
pricing-discoveryinstrument substitutes this into its opening question.) - Who is it for? (shapes personas)
- Are there competitors or alternatives? (panelists will volunteer these if real)
- What price range is the user considering? (optional — don't reveal it to the panel until after discovery)
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.
- 2d ago First seen · 76 lines · 40 tokens per session scan A 9f8a244b9515
pricing-probe is a skill published in the GitHub repository DataViking-Tech/Althing (2 stars, last pushed 23d ago), licensed MIT. It adds 40 tokens to every session and 1,244 once invoked, about $0.0002 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
deepeval
DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; send results to Confident AI…
deepeval-otel
Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span. or confident.trace. attributes…
deepeval-tracing
Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangChain, OpenAI Agents…
unreal-enhanced-input
Set up player input in Unreal Engine 5 with Enhanced Input: Input Actions, Input Mapping Contexts, modifiers and triggers, adding the mapping context, and binding actions by ETriggerEvent. Use when wiring movement/look/jump input, creating IA/IMC assets, binding in C++ or Blueprints, or when the user mentions Enhanced…
reference-images
Find and actually LOOK at real photographs — keyless image-search APIs, downloaded to disk so they render as images. Use before building any place, material, vehicle, sky or lighting condition, and again when judging your own screenshots.
agent-testing-patterns
Test AI agent systems including tool use, multi-turn conversations, error recovery, and non-deterministic outputs. Covers mock strategies, evaluation metrics, and regression testing for agent workflows. Triggers on AI agent testing, LLM evaluation, or agent quality assurance requests.