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 build-with-dhiraj/ai-workflow-framework-portability-kit --skill generate-synthetic-datagit clone --depth 1 https://github.com/build-with-dhiraj/ai-workflow-framework-portability-kitWrote 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/build-with-dhiraj/ai-workflow-framework-portability-kit/generate-synthetic-data)<a href="https://agentmods.dev/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/generate-synthetic-data"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/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/build-with-dhiraj/ai-workflow-framework-portability-kit/generate-synthetic-data"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/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 | $0.00074 | $0.01184 |
| Opus 5.5 | $0.00030 | $0.00474 |
| Sonnet 5.5 | $0.00015 | $0.00237 |
| Haiku 4.5 | $0.00007 | $0.00118 |
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 1mo 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.
This is a copy
100% identical to generate-synthetic-data — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Synthetic Data
Generate diverse, realistic test inputs that cover the failure space of an LLM pipeline.
Prerequisites
Before generating synthetic data, identify where the pipeline is likely to fail. Ask the user about known failure-prone areas, review existing user feedback, or form hypotheses from available traces. Dimensions (Step 1) must target anticipated failures, not arbitrary variation.
Core Process
Step 1: Define Dimensions
Dimensions are axes of variation specific to your application. Choose dimensions based on where you expect failures.
Dimension 1: [Name] — [What it captures]
Values: [value_a, value_b, value_c, ...]
Dimension 2: [Name] — [What it captures]
Values: [value_a, value_b, value_c, ...]
Dimension 3: [Name] — [What it captures]
Values: [value_a, value_b, value_c, ...]
Example for a real estate assistant:
Feature: what task the user wants
Values: [property search, scheduling, email drafting]
Client Persona: who the user serves
Values: [first-time buyer, investor, luxury buyer]
Scenario Type: query clarity
Values: [well-specified, ambiguous, out-of-scope]
Start with 3 dimensions. Add more only if initial traces reveal failure patterns along new axes.
Step 2: Draft 20 Tuples with the User
A tuple is one combination of dimension values defining a specific test case. Present 20 draft tuples to the user and iterate until they confirm the tuples reflect realistic scenarios. The user's domain knowledge is essential here — they know which combinations actually occur and which are unrealistic.
(Feature: Property Search, Persona: Investor, Scenario: Ambiguous)
(Feature: Scheduling, Persona: First-time Buyer, Scenario: Well-specified)
(Feature: Email Drafting, Persona: Luxury Buyer, Scenario: Out-of-scope)
Step 3: Generate More Tuples with an LLM
Generate 10 random combinations of ({dim1}, {dim2}, {dim3})
for a {your application description}.
The dimensions are:
{dim1}: {description}. Possible values: {values}
{dim2}: {description}. Possible values: {values}
{dim3}: {description}. Possible values: {values}
Output each tuple in the format: ({dim1}, {dim2}, {dim3})
Avoid duplicates. Vary values across dimensions.
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.
- 1mo ago First seen · 132 lines · 74 tokens per session scan A 37b8e12ceb02
generate-synthetic-data is a skill published in the GitHub repository build-with-dhiraj/ai-workflow-framework-portability-kit (5 stars, last pushed 21d ago), licensed MIT. It adds 74 tokens to every session and 1,184 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to generate-synthetic-data, differing in 0 lines, and is treated as a copy.
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