docs: correct stale agent lists, five broken anchors, and two wrong repo URLs

.claude/skills/benchmark/SKILL.md

@@ -66,7 +66,7 @@ Do these in order. Do not skip the HF-token check; a missing token is the most c
 **2a. HuggingFace token must be available BEFORE starting vLLM.** `vllm-serve.sh` resolves a token from `$HF_TOKEN`, else a `.hf_token` file in the repo root, the `self-hosted/vllm/` dir, or `$HOME`. Check that at least one is present:
 
 ```bash
-cd ~/agentic-coding-harness-benchmarks
+cd ~/sample-agentic-coding-harness-benchmarks
 if [ -n "${HF_TOKEN:-}" ] || [ -s .hf_token ] || [ -s self-hosted/vllm/.hf_token ] || [ -s "$HOME/.hf_token" ]; then
   echo "HF token available"
 else
@@ -195,7 +195,7 @@ Run the end-to-end script from `benchmarks/`. It re-runs every pre-flight check
 
 ```bash
 cd benchmarks
-./scripts/run-e2e-benchmark.sh --provider {provider} --model {model} --dataset {dataset} [--agent claude|pi|omp|codex|kiro] [--yes] [--count N] [--skip-judge]
+./scripts/run-e2e-benchmark.sh --provider {provider} --model {model} --dataset {dataset} [--agent claude|pi|omp|codex|kiro|strands|strands-harness] [--yes] [--count N] [--skip-judge]
 ```
 
 Tell the user, before it runs:

benchmarks/README.md

@@ -100,7 +100,7 @@ Two flags carry most of the method:
 - `--holdout` routes each task from tier means recomputed with **that task excluded**. Without it the evaluation is in-sample: the skill builds `models.json` from these same 21 tasks, so it would score partly on data it has already seen. Leave-one-out gives the honest number. Run it in-sample and you get an upper bound.
 - `--no-allow-list` ignores the organisation's approved-model list, so the result measures routing rather than local policy. Drop it to see what the shipped allow-list permits.
 
-`--floor-sweep 55,65,70,75` replaces the judged floors with fixed ones, which shows how much the whole result depends on where the floor is set. Results and caveats: [Does routing beat picking one model?](../README.md#does-routing-beat-picking-one-model)
+`--floor-sweep 55,65,70,75` replaces the judged floors with fixed ones, which shows how much the whole result depends on where the floor is set. Results and caveats: [Does routing pay for itself?](../docs/swe-router-evaluation.md)
 
 ## Quick start
 

benchmarks/docs/path-open-weight-on-bedrock-litellm.md

@@ -1,6 +1,6 @@
 # Path 2 - open-weight models on Amazon Bedrock via a LiteLLM proxy
 
-Use this path to benchmark **non-Anthropic (open-weight) models hosted on Amazon Bedrock** -- Moonshot AI's Kimi, Qwen, DeepSeek, Mistral, MiniMax, GLM, GPT-OSS, and so on. `claude -p` only speaks the Anthropic Messages API, and Bedrock's native Anthropic route rejects these models (see [Path 1](path-anthropic-on-bedrock.md#anthropic-only)). The fix is a [LiteLLM proxy](https://docs.litellm.ai/docs/simple_proxy) that we run in front of Bedrock: it translates between the Anthropic Messages format Claude Code speaks and the OpenAI Chat Completions format the open-weight Bedrock models speak, so **any open-weight model on Bedrock can be wired into Claude Code** and driven through the harness with `provider: endpoint`.
+Use this path to benchmark **non-Anthropic (open-weight) models hosted on Amazon Bedrock** -- Moonshot AI's Kimi, Qwen, DeepSeek, Mistral, MiniMax, GLM, GPT-OSS, and so on. `claude -p` only speaks the Anthropic Messages API, and Bedrock's native Anthropic route rejects these models (see [Path 1](path-anthropic-on-bedrock.md#supported-models)). The fix is a [LiteLLM proxy](https://docs.litellm.ai/docs/simple_proxy) that we run in front of Bedrock: it translates between the Anthropic Messages format Claude Code speaks and the OpenAI Chat Completions format the open-weight Bedrock models speak, so **any open-weight model on Bedrock can be wired into Claude Code** and driven through the harness with `provider: endpoint`.
 
 The [scripts/bedrock-mantle-proxy.sh](../scripts/bedrock-mantle-proxy.sh) helper starts the proxy for you. For everything common to all paths -- dataset format, runner config, metrics file, and the judge -- see the [harness reference](harness-reference.md).
 

benchmarks/scripts/run-benchmark-batch.sh

@@ -42,7 +42,8 @@
 #   --dataset    dataset YAML, relative to benchmarks/ (required)
 #   --models     comma-separated model ids, run in order (required)
 #   --tasks      comma-separated task ids; default is every task in the dataset
-#   --agent      coding agent: pi (default), claude, omp, kiro
+#   --agent      coding agent: pi (default), claude, omp, kiro, codex, strands,
+#                strands-harness
 #   --skill      swe3 (default) or swe2
 #   --aws-region region for the codex judge (default us-east-1). Needed even on
 #                the vllm path: the model is local but the judge is on Bedrock.

docs/vision.md

@@ -1,6 +1,6 @@
 # The vision: a cost-aware coding harness that routes across the frontier
 
-This repository measures a [cost/quality Pareto frontier](../README.md#results-a-worked-example) -- the set of models where nothing else is both better and cheaper. Building that frontier is not the end goal. It is the **lookup table** for the thing we actually want to build: a coding harness (or agent) that, given a task, **intelligently picks the right model from the frontier for that task** -- and switches models mid-task when the situation changes -- so a developer gets frontier-level results at workhorse-level cost without ever having to think about model selection.
+This repository measures a [cost/quality Pareto frontier](../README.md#step-1--benchmark-and-get-a-frontier) -- the set of models where nothing else is both better and cheaper. Building that frontier is not the end goal. It is the **lookup table** for the thing we actually want to build: a coding harness (or agent) that, given a task, **intelligently picks the right model from the frontier for that task** -- and switches models mid-task when the situation changes -- so a developer gets frontier-level results at workhorse-level cost without ever having to think about model selection.
 
 ## The problem this solves
 
@@ -23,7 +23,7 @@ The result: the developer states a task and a budget posture ("cheap", "balanced
 
 ## The first concrete step: `/swe-auto`
 
-The first slice of this vision is **`/swe-auto`** -- a router skill that runs on **either Claude Code or pi**. The developer does not choose the model. Given a repo + ref + problem, a configurable **router model** triages the task read-only, classifies it as **frontier / workhorse / budget**, consults the measured [cost/quality Pareto frontier](../README.md#results-a-worked-example) to pick the cheapest non-dominated model that clears that tier's quality band, then shells out to the existing headless runner to run the **`/swe3`** skill with the selected model and harness -- producing the six artifacts (and, optionally, an `eval.json`). If the first pick fails to complete or scores below its band, it escalates one tier and re-runs, bounded by `max_escalations`.
+The first slice of this vision is **`/swe-auto`** -- a router skill that runs on **either Claude Code or pi**. The developer does not choose the model. Given a repo + ref + problem, a configurable **router model** triages the task read-only, classifies it as **frontier / workhorse / budget**, consults the measured [cost/quality Pareto frontier](../README.md#step-1--benchmark-and-get-a-frontier) to pick the cheapest non-dominated model that clears that tier's quality band, then shells out to the existing headless runner to run the **`/swe3`** skill with the selected model and harness -- producing the six artifacts (and, optionally, an `eval.json`). If the first pick fails to complete or scores below its band, it escalates one tier and re-runs, bounded by `max_escalations`.
 
 The key design decision: the skill does the triage and frontier lookup **inline** (cheap), then **executes via `run-swe-headless.py`** rather than spawning a subagent -- so the same executor path works whether the router runs under Claude Code or pi (pi cannot fan out). Note the two independent harness choices: which agent runs the *router skill*, and which agent the *executor* drives `/swe3` under (`--agent`); they can match or differ.
 
@@ -71,7 +71,7 @@ sequenceDiagram
 
 You cannot route intelligently without knowing, per model:
 
-1. **How good it is** -- the quality benchmark (judge scores, and the [per-dimension breakdown](../README.md#quality-by-dimension-where-models-are-strong-or-weak) that tells you *which kinds* of work each model is reliable at).
+1. **How good it is** -- the quality benchmark (judge scores, and the [per-dimension breakdown](cost-per-task-methodology.md) that tells you *which kinds* of work each model is reliable at).
 2. **What it costs** -- the throughput benchmark, turned into a hardware-derived [cost per task](cost-per-task-methodology.md).
 3. **Where it sits relative to every other model** -- the Pareto frontier that combines the two.
 

self-hosted/vllm/README.md

@@ -106,7 +106,7 @@ It runs the steps below, reports each one, and stops for you at the "inference w
 ### Option B — run the scripts by hand
 
 ```bash
-git clone https://github.com/aws-samples/sample-claude-code-multi-model ~/repo
+git clone https://github.com/aws-samples/sample-agentic-coding-harness-benchmarks ~/repo
 cd ~/repo/self-hosted/vllm/scripts
 
 ./vllm-install.sh      # driver check → apt deps → uv → venv → vLLM → monitoring → GPU check