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Get Started Free →Use when operating a live Runtype account through MCP or Code Mode MCP: inspect resources, debug failed flows or agents, read logs and traces, manage products, surfaces, records, schedules, secrets, models, evals, batches, conversations, client tokens, or make safe account mutations. Includes search/execute patterns, read-before-write, validation, pagination, and conservative destructive-change policy.
.claude/skills/hashgraph-online-runtype-admin/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -40% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 168% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -14% | 0% |
Use this skill for live account operations. All mutations affect the user's Runtype workspace unless explicitly run against a disposable test account.
state and validating payloads.
runtype-build-product.For direct MCP tools, discover before acting:
get_me, list_products, list_agents, list_flows,list_tools, list_records, list_schedules, list_secrets, list_model_configs, list_conversations.
list_logs, get_log_stats, trace_execution, trace_conversation,list_agent_executions, get_record_results, get_record_step_results, get_record_costs.
validate_flow, validate_code, validate_product,validate_product_flow, validate_product_agent, validate_product_surface, validate_product_tool.
submit_eval, list_eval_batches, get_eval_results,compare_eval, compare_eval_record, analyze_eval_steps, submit_batch, get_batch_status, get_batch_summary, cancel_batch.
Surfaces are product-scoped. List or mutate them with the product id in hand.
Code Mode MCP exposes search, execute, get_platform_documentation, get_build_instructions, generate_persona_embed_code, and get_persona_theme_reference.
Use search first when unsure:
js;(spec) => spec.categories
Then inspect one method:
js;(spec) => spec.methods.updateAgent
Use execute with shaped results. Filter, project, slice, and aggregate inside the function before returning data so large responses do not consume context:
js;async (runtype) => { const products = await runtype.listProducts() return products.products.map((p) => ({ id: p.id, name: p.name })).slice(0, 20) }
trace_execution or trace_conversation.list_logs only after the trace, filtering by execution id, conversation id,level, status, category, or time window.
Common causes: missing secret, wrong secret reference syntax, unresolved template variable, tool schema mismatch, wrong surface behavior config, model timeout, or a flow that should be an agent.
search or get_platform_documentation(topic="platform-catalog") before guessing.
get_platform_documentation(topic="dashboard-links") when giving the user links.get_secret_intake_manifest, submit_secret_intake, and check_secrets ratherthan asking the model to handle credential values.
If the umbrella runtype skill is installed alongside this focused skill, its durable references provide deeper fallback notes. This skill must still work when installed by itself; prefer live MCP docs over local sibling files.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 16,834 | 17,419 | +3% | 1 | 1 | 0% | 254 | 1,483 | +484% | 0 | 0 | — |
case-02 | pass→fail | 29,716 | 7,924 | -73% | 1 | 1 | 0% | 3,981 | 1,287 | -68% | 0 | 0 | — |
case-03 | fail→fail | 10,006 | 19,834 | +98% | 1 | 1 | 0% | 237 | 2,010 | +748% | 0 | 0 | — |
case-04 | fail→pass | 25,360 | 10,678 | -58% | 1 | 1 | 0% | 3,142 | 1,894 | -40% | 0 | 0 | — |
case-05 | fail→pass | 9,931 | 11,379 | +15% | 1 | 1 | 0% | 733 | 1,963 | +168% | 0 | 0 | — |
case-06 | fail→pass | 13,226 | 12,073 | -9% | 1 | 1 | 0% | 1,589 | 1,975 | +24% | 0 | 0 | — |
case-07 | fail→pass | 15,662 | 11,757 | -25% | 1 | 1 | 0% | 1,180 | 1,548 | +31% | 0 | 0 | — |
case-08 | pass→pass | 15,481 | 8,692 | -44% | 1 | 1 | 0% | 1,527 | 1,597 | +5% | 0 | 0 | — |
case-09 | fail→fail | 9,655 | 3,464 | -64% | 1 | 1 | 0% | 1,461 | 1,417 | -3% | 0 | 0 | — |
case-10 | fail→fail | 17,631 | 8,077 | -54% | 1 | 1 | 0% | 1,787 | 1,499 | -16% | 0 | 0 | — |
case-11 | pass→fail | 49,687 | 17,246 | -65% | 1 | 1 | 0% | 1,693 | 1,768 | +4% | 0 | 0 | — |
case-12 | fail→fail | 12,350 | 3,393 | -73% | 1 | 1 | 0% | 1,844 | 1,437 | -22% | 0 | 0 | — |
case-13 | fail→fail | 10,143 | 18,155 | +79% | 1 | 1 | 0% | 1,597 | 1,547 | -3% | 0 | 0 | — |
case-14 | fail→fail | 11,466 | 3,425 | -70% | 1 | 1 | 0% | 1,778 | 1,438 | -19% | 0 | 0 | — |
case-15 | fail→fail | 10,280 | 5,415 | -47% | 1 | 1 | 0% | 1,600 | 1,582 | -1% | 0 | 0 | — |
case-16 | fail→pass | 13,985 | 2,146 | -85% | 1 | 1 | 0% | 1,405 | 1,210 | -14% | 0 | 0 | — |
case-17 | fail→pass | 22,005 | 7,438 | -66% | 1 | 1 | 0% | 2,612 | 2,114 | -19% | 0 | 0 | — |
case-18 | fail→fail | 13,978 | 14,873 | +6% | 1 | 1 | 0% | 1,565 | 1,685 | +8% | 0 | 0 | — |
case-19 | fail→fail | 19,963 | 6,679 | -67% | 1 | 1 | 0% | 1,747 | 1,983 | +14% | 0 | 0 | — |
case-20 | fail→fail | 20,462 | 4,096 | -80% | 1 | 1 | 0% | 1,531 | 1,506 | -2% | 0 | 0 | — |
case-21 | fail→fail | 17,530 | 8,877 | -49% | 1 | 1 | 0% | 1,949 | 1,457 | -25% | 0 | 0 | — |
case-22 | pass→pass | 18,175 | 8,190 | -55% | 1 | 1 | 0% | 1,972 | 1,364 | -31% | 0 | 0 | — |
case-23 | fail→fail | 16,228 | 7,350 | -55% | 1 | 1 | 0% | 1,657 | 1,270 | -23% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 19 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +17 percentage points is the difference between those two pass rates over the 19 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.