Replace raw file paths in docs with clear text

CONFIG.md

@@ -73,7 +73,7 @@ You can pass a TOML table instead of such a prefixed string:
 model = { provider = "openai", model_id = "gpt-5.6" }
 ```
 
-Note that models require specific configuration based on the provider. For details, see the `quantiles.toml` file under the provider of your choice in [`cli/examples/configs`](./cli/examples/configs).
+Note that models require specific configuration based on the provider. For details, see the `quantiles.toml` file under the provider of your choice in the [provider configuration examples](./cli/examples/configs).
 
 ### `custom_code`
 
@@ -177,4 +177,4 @@ input = { greeting = "world" }
 
 ### Custom evaluation with failure simulation
 
-See [`cli/examples/configs/custom_code/quantiles.toml`](./cli/examples/configs/custom_code/quantiles.toml) for a complete, commented example including a sample Python script.
+See the complete [custom-code configuration example](./cli/examples/configs/custom_code/quantiles.toml) for an annotated configuration and sample Python script.

CONTRIBUTING.md

@@ -31,15 +31,15 @@ git clone https://github.com/quantiles-evals/quantiles.git
 cd quantiles
 ```
 
-For the [`./cli`](./cli) directory, ensure you have Rust installed and use the `mise.toml` file in that directory to run common build/lint/check/test commands. For the [`./python`](./python) directory, use the [`uv`](https://docs.astral.sh/uv/) tool for similar commands. The [`typescript/`](./typescript) directory holds the yet-unreleased, unsupported TypeScript SDK.
+For the [CLI directory](./cli), ensure you have Rust installed and use the `mise.toml` file in that directory to run common build/lint/check/test commands. For the [Python SDK directory](./python), use the [`uv`](https://docs.astral.sh/uv/) tool for similar commands. The [TypeScript SDK directory](./typescript) holds the yet-unreleased, unsupported TypeScript SDK.
 
 ## Security
 
-Please do not report security vulnerabilities through public GitHub issues. Follow the reporting guidance in [SECURITY.md](./SECURITY.md).
+Please do not report security vulnerabilities through public GitHub issues. Follow the security reporting guidance in [SECURITY.md](./SECURITY.md).
 
 ## Code of Conduct
 
-By participating in this project, you agree to follow our [CODE_OF_CONDUCT.md](./CODE_OF_CONDUCT.md).
+By participating in this project, you agree to follow our [Code of Conduct](./CODE_OF_CONDUCT.md).
 
 ## License
 

README.md

@@ -26,13 +26,13 @@ Install the CLI:
 curl -fsSL https://cli.quantiles.io/install.sh | bash
 ```
 
-Run the [SimpleQA-verified](https://arxiv.org/abs/2509.07968) built-in benchmark:
+Run the [SimpleQA Verified](https://quantiles.io/benchmark-hub/benchmark/simpleqa-verified) built-in benchmark:
 
 ```bash
 qt run simpleqa-verified
 ```
 
-> Important note: this `qt run` command will run the [`simpleqa-verified`](https://arxiv.org/abs/2509.07968) benchmark against a "model" that simply generates random text. This functionality is intended to quickly show you how to run evals with the `qt` tool, without requiring you to set up API keys or spend money on tokens. Do not expect to draw conclusions from the results returned from this command.
+> Important note: this `qt run` command will run the [`simpleqa-verified`](https://quantiles.io/benchmark-hub/benchmark/simpleqa-verified) benchmark against a "model" that simply generates random text. This functionality is intended to quickly show you how to run evals with the `qt` tool, without requiring you to set up API keys or spend money on tokens. Do not expect to draw conclusions from the results returned from this command.
 
 Inspect the recorded run:
 
@@ -67,7 +67,7 @@ qt compare <run_id_a> <run_id_b>
 
 > Note: you can pass the `--json` flag to any of the above commands, to output machine and agent-friendly JSON instead of human-formatted output.
 
-To learn more about what you can do with the CLI, see [quantiles.io/documentation/reference/cli](https://quantiles.io/documentation/reference/cli).
+See the [CLI reference](https://quantiles.io/documentation/reference/cli) for available commands, options, and usage details.
 
 ### Configuration file and customization
 
@@ -98,8 +98,8 @@ The CLI will execute the command with `QUANTILES_RUN_ID`, `QUANTILES_WORKFLOW_NA
 
 See the following resources for more details:
 
-- [`CONFIG.md`](./CONFIG.md) - in-depth guides to configuration and reference
-- [`./cli/examples/configs`](./cli/examples/configs) - complete examples, including a [custom code benchmark example](./cli/examples/configs/custom_code/quantiles.toml)
+- [Configuration guide](./CONFIG.md) - Detailed configuration instructions and reference documentation.
+- [Complete configuration examples](./cli/examples/configs) - Complete examples, including a [custom-code benchmark configuration](./cli/examples/configs/custom_code/quantiles.toml)
 
 ## Local-First and Offline by Default
 
@@ -124,7 +124,7 @@ Built-in benchmarks are ready-to-run evaluations with predefined datasets, scori
 | `qt run <benchmark>`                                   | Run a built-in benchmark against the demo model to inspect sample-level inputs and outputs, scoring behavior, workflow steps, and aggregate metrics |
 | `qt run <benchmark> --input '{"model":"<model_name>}'` | Run a built-in benchmark against your model                                                                                                         |
 
-Quantiles also provides a [benchmark hub](https://quantiles.io/benchmark-hub) for discovering built-in benchmarks, understanding their evaluation setup, and reviewing common metrics used across AI evaluation workflows.
+Quantiles also provides a [Benchmark Hub](https://quantiles.io/benchmark-hub) for discovering built-in benchmarks, understanding their evaluation setup, and reviewing common metrics used across AI evaluation workflows.
 
 ### Add a built-in benchmark
 
@@ -134,7 +134,7 @@ Helpful requests include the benchmark name, source dataset or repository, licen
 
 ## Custom Evaluations
 
-A custom evaluation is a [Python](https://quantiles.io/documentation/reference/python-sdk) program that is run by the `qt` CLI and uses its [local storage](https://quantiles.io/documentation/local-first-offline) and [durable workflow engine](https://quantiles.io/documentation/workflows-and-steps) to run efficiently and reliably. Your code owns the evaluation logic like loading data, calling a model or agent, scoring outputs, computing metrics, and returning a summary. Quantiles manages [durable steps, step caching, and step resume](https://quantiles.io/documentation/workflows-and-steps), metrics, inputs, outputs, and comparisons.
+A custom evaluation is a [Python](https://quantiles.io/documentation/reference/python-sdk) program that is run by the `qt` CLI and uses its [local storage](https://quantiles.io/documentation/local-first-offline) and [durable workflow engine](https://quantiles.io/documentation/workflows-and-steps) to run efficiently and reliably. Your code owns the evaluation logic like loading data, calling a model or agent, scoring outputs, computing metrics, and returning a summary. Quantiles manages durable steps, step caching, and step resume, metrics, inputs, outputs, and comparisons.
 
 Custom evaluations are configured in `quantiles.toml` with `type = "custom_code"`:
 
@@ -151,21 +151,21 @@ Run the evaluation with `qt run my-eval`. If it fails, resume it later with `qt
 
 Use custom evaluations when you need to measure behavior that is specific to your product, workflow, prompt, dataset, rubric, or release process.
 
-Read more about how to build and run custom evaluations at [quantiles.io/documentation/custom-evaluations](https://quantiles.io/documentation/custom-evaluations).
+See the [custom evaluations guide](https://quantiles.io/documentation/custom-evaluations) for instructions on building and running custom evaluations.
 
 ### Python SDK
 
 Use the official Quantiles Python SDK to build your custom evaluations with primitives like durable steps, structured inputs/outputs, and metrics emission, using patterns and practices native to Python. The SDK integrates tightly with the `qt` CLI’s local API for running, recording, and analyzing benchmarks.
 
-The code for the Python SDK is located in this repository at [`./python/`](./python). Read more about it at [quantiles.io/documentation/reference/python-sdk](https://quantiles.io/documentation/reference/python-sdk).
+The [Python SDK source code](./python) is available in this repository. See the [Python SDK reference](https://quantiles.io/documentation/reference/python-sdk) for usage and API documentation.
 
 ## Coding Agents
 
-Quantiles is designed to work well with coding agents such as Codex, Claude Code, Cursor, GitHub Copilot, Gemini CLI, OpenCode, and other agentic development tools. For a concise, public, LLM-readable overview of Quantiles with links to agent guides and related documentation, see [quantiles.io/llms.txt](https://quantiles.io/llms.txt).
+Quantiles is designed for use with coding agents such as Codex, Claude Code, Cursor, GitHub Copilot, Gemini CLI, and OpenCode. The [Quantiles `llms.txt` file](https://quantiles.io/llms.txt) provides a concise, public, LLM-readable overview with links to agent guides and related documentation.
 
 ### `SKILL.md`
 
-The [github.com/quantiles-evals/skill](https://github.com/quantiles-evals/skill) repository includes a [`SKILL.md`](https://github.com/quantiles-evals/skill/blob/main/SKILL.md) file that gives agents complete instructions for running evaluations, inspecting results, comparing runs, and summarizing regressions. To use the skill with your agent, install it with the following prompt:
+The [Quantiles agent skill repository](https://github.com/quantiles-evals/skill) provides a [`SKILL.md`](https://github.com/quantiles-evals/skill/blob/main/SKILL.md) instruction file that guides coding agents through creating, running, analyzing, and comparing evaluations. Use the following agent prompt to install it:
 
 ```text
 Install the Quantiles eval skill at github.com/quantiles-evals/skill
@@ -183,7 +183,7 @@ The embedded [`AGENTS.md` file](./AGENTS.md) gives agents repository-specific in
 
 ## Documentation
 
-Full documentation is available at [quantiles.io/documentation](https://quantiles.io/documentation/).
+See the [Quantiles documentation](https://quantiles.io/documentation/) for comprehensive guides and reference documentation.
 
 Start here:
 
@@ -199,7 +199,7 @@ Please read our [contributing guide](./CONTRIBUTING.md) to get started.
 
 ## Security
 
-Please do not report security vulnerabilities through public GitHub issues. Follow the reporting guidance in [SECURITY.md](./SECURITY.md).
+Please do not report security vulnerabilities through public GitHub issues. Follow the security reporting guidance in [SECURITY.md](./SECURITY.md).
 
 ## License
 

cli/README.md

@@ -28,7 +28,7 @@ qt list
 qt show 1
 ```
 
-> See [quantiles.io/documentation/reference/cli](https://quantiles.io/documentation/reference/cli) for a detailed list of `qt` commands.
+> See the [CLI reference](https://quantiles.io/documentation/reference/cli) for a detailed list of `qt` commands.
 
 ### Custom evaluations
 
@@ -49,14 +49,14 @@ qt run my-eval
 qt resume <run_id>
 ```
 
-See [`examples/configs/custom_code/quantiles.toml`](./examples/configs/custom_code/quantiles.toml) for a complete working example.
+See the [custom-code configuration example](./examples/configs/custom_code/quantiles.toml) for a complete working configuration.
 
 ## Configuration files and customization
 
 You can customize how the CLI executes built-in benchmarks and custom evaluations using a `quantiles.toml` or `.quantiles.toml` configuration file. See the following resources for information and examples:
 
-- [`../CONFIG.md`](../CONFIG.md) for a guide and reference.
-- [`./examples/configs`](./examples/configs) for complete working examples.
+- [Configuration reference](../CONFIG.md) for configuration guidance and supported options.
+- [Configuration examples](./examples/configs) for complete working configurations.
 
 ### Built-in benchmarks
 

python/README.md

@@ -10,7 +10,7 @@ uv add quantiles
 
 ## Usage
 
-To build a custom eval with Python, use the following code. To ensure this eval is runnable with `qt run`, set up a `quantiles.toml` configuration file. See [`../CONFIG.md`](../CONFIG.md) for details.
+Use the following code to build a custom evaluation with Python. To run it with `qt run`, configure it in a `quantiles.toml` file as described in the [configuration guide](../CONFIG.md).
 
 ```python
 import asyncio

typescript/README.md

@@ -12,14 +12,14 @@ bun install quantiles
 
 ## Usage
 
-To build a custom eval with TypeScript, use the following code. To ensure this eval is runnable with `qt run`, set up a `quantiles.toml` configuration file. See [`../CONFIG.md`](../CONFIG.md) for details.
+Use the following code to build a custom evaluation with TypeScript. To run it with `qt run`, configure it in a `quantiles.toml` file as described in the [configuration guide](../CONFIG.md).`
 
 ```ts
 import { QuantilesClient } from "@quantiles/sdk";
 
 const client = new QuantilesClient();
 const run = await client.createRun("eval-smoke-test", {
-  model: "gpt-4.1-mini",
+  model: "gpt-5.6",
 });
 
 const output = await run.step("call-model", { prompt: "hello" }, async () => {