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 Goodeye-Labs/truesight-mcp-skills --skill generate-synthetic-datagit clone --depth 1 https://github.com/Goodeye-Labs/truesight-mcp-skillsWrote 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/goodeye-labs/truesight-mcp-skills/generate-synthetic-data)<a href="https://agentmods.dev/skills/goodeye-labs/truesight-mcp-skills/generate-synthetic-data"><img src="https://agentmods.dev/badge/skills/goodeye-labs/truesight-mcp-skills/generate-synthetic-data/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/goodeye-labs/truesight-mcp-skills/generate-synthetic-data"><img src="https://agentmods.dev/badge/skills/goodeye-labs/truesight-mcp-skills/generate-synthetic-data.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 | $0.00043 | $0.01575 |
| Opus 5 | $0.00022 | $0.00788 |
| Sonnet 5 | $0.00009 | $0.00315 |
| Haiku 4.5 | $0.00004 | $0.00158 |
Grade A, and why
generate-synthetic-data 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 12d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Synthetic Data
Generate realistic synthetic traces for LLM evaluation datasets using dimension-based variation.
When to use
- User needs test data for an evaluation but has no production traces.
create-evaluationdelegates here when real traces are unavailable.- User wants to augment sparse real data with targeted synthetic examples.
Scoping protocol
Ask these five questions. Skip any already answered in the conversation (e.g., if create-evaluation already established the system type, do not re-ask).
- System type. What kind of AI system produces the traces?
- Simple RAG, tool-calling agent, multi-turn chat, support bot, classification pipeline, other
- Trace structure. What columns does each trace contain?
- Offer common patterns based on system type:
- Simple RAG:
user_query,retrieved_context,response - Tool-calling agent:
user_request,tool_calls,final_answer - Multi-turn chat:
conversation_history,assistant_response - Support bot:
customer_message,kb_lookup,agent_reply
- Simple RAG:
- Let the user rename, add, or remove columns
- Offer common patterns based on system type:
- Dimensions of variation. What axes should drive diversity?
- Propose 3-5 starter dimensions based on system type and known failure modes
- Each dimension needs 3-6 discrete values
- Example for RAG: query complexity (simple factual, multi-hop, ambiguous, comparative), domain coverage (billing, technical support, account management), context quality (perfect match, partial match, irrelevant, missing)
- Dataset size. How many final traces?
- Default recommendation: 50-100 for initial eval scoping, 200+ for statistical significance
- Output destination. Where should the data go?
- Default: MCP upload when called from
create-evaluation, file when standalone - Options: Truesight dataset (via MCP), JSONL file, CSV file, both
- Default: MCP upload when called from
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.
- 12d ago First seen · 146 lines · 43 tokens per session scan A 618a2bfe9b0c
generate-synthetic-data is a skill published in the GitHub repository Goodeye-Labs/truesight-mcp-skills (7 stars, last pushed 5mo ago), licensed MIT. It adds 43 tokens to every session and 1,575 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.
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