Align runner paths with GitHub release repository

README.md

@@ -109,7 +109,7 @@ The repository also includes:
 | `manifests/release_provenance.json` | Authoritative configuration and clean-verification provenance |
 | `selections/` | Reproducible task selections |
 | `tasks/task_NNN/` | Thin Harbor/Pier task bundles |
-| `tools/` | Materialization, provider, batch, and summary tools |
+| `scripts/` | Materialization, provider, batch, and summary tools |
 | `docs/run-batch.md` | Full provider and batch-runner reference |
 
 The environment image contains the baseline source, public fixtures, dependencies, and compilers. The separate verifier image contains held-out tests and the grader. The dataset does not contain reference-answer patches, credentials, agent trajectories, or private verifier tests.
@@ -132,14 +132,14 @@ docker login
 Materialize the default selection:
 
 ~~~bash
-python3 tools/materialize.py \
+python3 scripts/materialize.py \
   --output tasks-selected --force
 ~~~
 
 Materialize one task, a comma-separated list, or inclusive ranges:
 
 ~~~bash
-python3 tools/materialize.py \
+python3 scripts/materialize.py \
   --task-id 002,005-007 \
   --output tasks-selected-small --force
 ~~~
@@ -148,7 +148,7 @@ Materialize the complete 91-task science-knowledge ablation split. It contains
 restricted-license tasks, so the explicit license opt-in is required:
 
 ~~~bash
-python3 tools/materialize.py \
+python3 scripts/materialize.py \
   --task-id 002-082,084,086,090,097-101,111,114 \
   --allow-restricted-licenses \
   --output tasks-science-knowledge-ablation --force
@@ -161,7 +161,7 @@ Every materialization writes `selection.json` with the exact task IDs used for t
 Twenty-three tasks are excluded from the default selection because they contain GPL/LGPL/AGPL-family code, academic non-commercial sources or materials, or restricted third-party data. Include them only after confirming that your use is permitted:
 
 ~~~bash
-python3 tools/materialize.py \
+python3 scripts/materialize.py \
   --allow-restricted-licenses \
   --output tasks-selected-all --force
 ~~~
@@ -198,7 +198,7 @@ pier run -p tasks-selected-small \
 For Codex gateway profiles, use the included wrapper:
 
 ~~~bash
-python3 tools/run_batch.py \
+python3 scripts/run_batch.py \
   --path tasks-selected-small \
   --agent codex \
   --env-file ~/.config/swe-bench-science/codex.env \
@@ -242,7 +242,7 @@ jobs/<job-name>/<task>__<trial>/verifier/test-stdout.txt
 The wrapper additionally writes `jobs/summary.json` and `jobs/summary.csv`. For a direct Pier run, generate the same summaries with:
 
 ~~~bash
-python3 tools/summarize_results.py --jobs-dir jobs
+python3 scripts/summarize_results.py --jobs-dir jobs
 ~~~
 
 Use `pier view jobs` to inspect trajectories.

docs/run-batch.md

@@ -1,6 +1,6 @@
 # Batch Evaluation Reference
 
-`tools/run_batch.py` is the convenience wrapper for running a materialized task
+`scripts/run_batch.py` is the convenience wrapper for running a materialized task
 selection with Pier. It does not build task images. It reads the immutable
 environment and verifier references from each `task.toml`, pulls those images
 for `linux/amd64`, writes a redacted run record, and invokes Pier with
@@ -13,7 +13,7 @@ From a downloaded release directory:
 ~~~bash
 uv tool install --python 3.12 "datacurve-pier==0.3.0"
 docker login
-python3 tools/materialize.py \
+python3 scripts/materialize.py \
   --task-id 002,005-007 \
   --output tasks-selected-small --force
 ~~~
@@ -86,7 +86,7 @@ the selected harness by Pier.
 Run a no-model infrastructure smoke:
 
 ~~~bash
-python3 tools/run_batch.py \
+python3 scripts/run_batch.py \
   --path tasks-selected-small \
   --agent nop \
   --n-concurrent 1 \
@@ -98,7 +98,7 @@ python3 tools/run_batch.py \
 Run Codex through a gateway:
 
 ~~~bash
-python3 tools/run_batch.py \
+python3 scripts/run_batch.py \
   --path tasks-selected-small \
   --agent codex \
   --env-file ~/.config/swe-bench-science/codex.env \
@@ -112,7 +112,7 @@ python3 tools/run_batch.py \
 Run Claude Code or mini-swe-agent:
 
 ~~~bash
-python3 tools/run_batch.py \
+python3 scripts/run_batch.py \
   --path tasks-selected-small \
   --agent claude-code \
   --env-file ~/.config/swe-bench-science/claude.env \
@@ -201,7 +201,7 @@ for trajectories and inspect `result.json`, `reward.json`, and
 Pull nothing and inspect the fully rendered command:
 
 ~~~bash
-python3 tools/run_batch.py \
+python3 scripts/run_batch.py \
   --path tasks-selected-small \
   --agent codex \
   --env-file ~/.config/swe-bench-science/codex.env \
@@ -212,12 +212,12 @@ python3 tools/run_batch.py \
 Run the 91-task science-knowledge ablation selection after materialization:
 
 ~~~bash
-python3 tools/materialize.py \
+python3 scripts/materialize.py \
   --task-id 002-082,084,086,090,097-101,111,114 \
   --allow-restricted-licenses \
   --output tasks-science-knowledge-ablation --force
 
-python3 tools/run_batch.py \
+python3 scripts/run_batch.py \
   --path tasks-science-knowledge-ablation \
   --agent codex \
   --env-file ~/.config/swe-bench-science/codex.env \