| name | resource-and-baseline-preparation |
| execution_scope | executor_template |
| execution_owner | T5-SPECIALIZE-EXECUTOR-SKILLS |
| description | Prepare and statically validate the datasets, benchmarks, baseline implementations, evaluation code, preprocessing assets, checkpoints, environments, protocols, and references required by the confirmed ResearchOS external-executor scope. Use when `research-execution` dispatches Phase B after non-blocking context alignment, when resource readiness is missing or stale, or when authorized local resource review, public remote acquisition, or baseline reimplementation is needed. Produce a requirement matrix, provenance-rich inventory, candidate and review records, material gaps, propagated risks, and a `ready`, `partial`, or `blocked` readiness gate. Do not run baseline experiments, redesign claims, silently replace required baselines, execute unreviewed third-party code, or broaden network, dataset, license, path, or reimplementation authority. |
Resource and Baseline Preparation
Prepare the minimum experiment loop without confusing resource availability with experimental reproduction. This skill owns Phase B static preparation and review; baseline-reproduction owns executed baseline evidence.
Establish paths and ownership
- Locate the nearest directory containing both
project.yaml and external_executor/; call it <workspace>.
- Treat the directory containing this file as
<skill-dir>.
- Read before any write:
<workspace>/external_executor/AGENTS.md;<workspace>/external_executor/allowed_paths.txt;<workspace>/external_executor/handoff_pack.json;<workspace>/external_executor/result_pack.json#context_alignment;<skill-dir>/references/acquisition-policy.md;<skill-dir>/references/resource-requirement-contract.md;<skill-dir>/references/output-contract.md.
- Stop with
blocked when context alignment is absent or blocking, the confirmed execution scope is missing, the acquisition policy is internally inconsistent, or the writable resource boundary cannot be determined. If the policy is absent in a legacy handoff, use the ResearchOS default policy: public remote resource acquisition, public dataset download, and baseline reimplementation are allowed within allowed_paths.txt, license review, and security review.
Write only:
external_executor/report/phase_B/resource_preflight.json;external_executor/resource_requirement_matrix.json;external_executor/report/phase_B/resource_local_inventory.json;external_executor/report/phase_B/resource_search_records.json;external_executor/report/phase_B/resource_source_report.json;external_executor/report/phase_B/resource_source_report.md;external_executor/report/phase_B/resource_preparation_report.json;external_executor/report/phase_B/static_review.json;external_executor/report/phase_B/validation_report.json;- authorized resource material, remote acquisitions, and baseline reimplementations under
resources/;
- the Phase B result-pack sections listed in
references/output-contract.md, through the narrow apply script.
Do not change external_executor/executor_status.json, external_executor/report/run_manifest.json, budgets, iteration decisions, context alignment, experiment plans, baseline reproduction records, or sibling-owned sections. Return control to research-execution after applying the report.
Run deterministic preflight
Run:
python <skill-dir>/scripts/preflight_resources.py --workspace <workspace> \
--output external_executor/report/phase_B/resource_preflight.json
The preflight must confirm:
context_alignment.status is pass or non-blocking mismatch;confirmed_execution_scope contains required baselines, benchmark/protocol information, minimum loop, claim constraints, and acquisition policy;- acquisition mode, capability flags, and allowed domains agree;
- local output,
resources/, and report paths are writable under policy;
- no unsupported major schema is required.
A preflight warning prompts targeted review. A preflight blocker prevents acquisition and reimplementation. Missing resources/ by-hand inputs, resources/baseline_candidates.jsonl, or literature/baseline_map.json are not blockers; this skill owns discovering/acquiring/reimplementing missing resources under resources/.
Resolve operational settings only when Phase B can prove them within scope
When external_executor/report/executor_selection.json has execution_scope=resource_preparation, read the handoff's execution_readiness.required_decisions. Do not send these back as a generic request for the researcher to fill in. For every decision that can be resolved within the already declared T4.5 scope, record a precise item in resource_preparation_report.json#operational_settings after source review:
setting: the exact pending T5 decision string;setting_type: runtime_environment, model_backbone, execution_scale, declared_benchmark_resource, or seed_policy;selected_value: the exact version, package, model/checkpoint, scale policy, benchmark resource, or seed policy to use;selection_basis and non-empty source_refs;research_boundary_preserved=true.
Examples include a concrete simulator package/version for a declared simulation, a reviewed accessible checkpoint for a declared model family, a fixed benchmark version/split for an already declared benchmark, or a bounded sample/budget policy under project constraints. Mark an item unresolved when the available sources cannot establish it. Never use this mechanism to choose a different research task, central mechanism, required baseline set, benchmark scope, or claim/contribution boundary; those remain explicit scope-change records for the root.
Build the resource requirement matrix
Run:
python <skill-dir>/scripts/build_requirement_matrix.py --workspace <workspace> \
--output external_executor/resource_requirement_matrix.json
Then complete or refine the matrix using references/resource-requirement-contract.md. Create one requirement per independently verifiable resource, covering as applicable:
- benchmark definition and official protocol;
- dataset, version, legal access, split, labels, and checksums;
- required and optional baseline implementations;
- metric and evaluation implementation, direction, and aggregation;
- preprocessing/tokenization/feature construction;
- pretrained checkpoints or initialization assets;
- environment/runtime constraints;
- protocol/reference material needed to resolve implementation details.
Every requirement must state whether it is required, what minimum-loop dependency it serves, accepted source classes, exact compatibility expectations, acceptance criteria, replacement authority, and whether its absence blocks execution. Distinguish missing, incomplete, incompatible, not_runnable, protocol_nonequivalent, restricted, and untrusted_source.
Do not silently weaken a requirement because a convenient candidate exists.
Use the three-step resource strategy
For each unsatisfied requirement, proceed in this exact order:
- verify local resources under the project
resources/ area;
- search and acquire authorized public remote resources under
resources/;
- reimplement an unavailable required baseline under
resources/ when policy permits.
After each step, review candidates against the requirement matrix and check whether all minimum-loop requirements are now satisfied. If they are satisfied, stop the strategy for those requirements and do not run later fallback steps. If all three steps finish and a minimum-loop required resource is still unsatisfied, mark the requirement blocked, record the blocker, and return to research-execution for human supplementation or scope review.
Inventory local resource material first
Run:
python <skill-dir>/scripts/inventory_local_resources.py --workspace <workspace> \
--output external_executor/report/phase_B/resource_local_inventory.json
Inspect in this order:
resources/ # by-hand local project resources
literature/resource_catalog.jsonl # paper-associated discovery leads, if present
The literature catalog is not a resource root and does not make its entries usable. Use it to trace candidate identity back to a paper, prioritize official sources, and identify stated code/data/benchmark/supplement links. Before accepting any candidate, still verify public accessibility, license, fixed revision, security, task/dataset/split/metric compatibility, and protocol equivalence under this Phase B contract.
Use references/resource-review-checklist.md to map candidates to requirements. Inspect provenance, fixed version, license, README, configuration, entry points, dependency manifests, dataset split, preprocessing, metric implementation, benchmark protocol, checkpoints, symlinks, submodules, and minimum-loop coverage. Do not execute third-party setup, download, shell, notebook, training, or evaluation code during inventory.
Stage accepted by-hand material under resources/byhand/<candidate-id>/.
When a local candidate is accepted for controlled work, copy it without mutating the source:
python <skill-dir>/scripts/stage_local_resource.py --workspace <workspace> \
--source <workspace-relative-source> --candidate-id <candidate-id>
Reject escaping or unresolved symlinks. Preserve source and staged checksums in provenance.
Initialize the durable report envelope after preflight, matrix creation, and local inventory:
python <skill-dir>/scripts/initialize_resource_report.py --workspace <workspace> \
--output external_executor/report/phase_B/resource_preparation_report.json
The initializer preserves already reviewed child-owned sections by default. Use --force only when the root has invalidated the entire Phase B checkpoint.
Search and acquire remote resources only when local resources are insufficient
Read references/source-ranking-and-search.md before remote search.
Use remote search only for requirements still unsatisfied after local review and only when all of these are true:
- acquisition mode is
github_allowed or github_and_reimplementation;
network_allowed=true;- the domain is explicitly allowed;
- dataset download is separately authorized when the resource contains data;
- the query does not expose private manuscript text, unpublished results, secrets, or private paths.
For ResearchOS T5 external execution, public remote search/acquisition and public dataset download are allowed by default unless a narrower handoff policy explicitly forbids them.
Search these platform groups first, then broaden only if they cannot satisfy the requirement:
- Baseline: Hugging Face, OpenReview, GitLab, Bitbucket, ModelScope, Zenodo.
- Dataset: Hugging Face, OpenML, Kaggle, UCI, Zenodo, Dataverse, DataCite.
- Benchmark: OpenML, Hugging Face Leaderboards, Codabench, EvalAI, HELM, and relevant domain-specific platforms such as OGB, MTEB, or OpenCompass.
Record every public source class checked, selected candidate, rejection, and remaining uncertainty in external_executor/report/phase_B/resource_search_records.json.
Prefer, in order:
- official author or project repository linked by the paper/project page;
- official benchmark or dataset organization;
- author-recognized implementation;
- high-confidence third-party reproduction with explicit protocol mapping;
- other candidates only as documented alternatives, not silent equivalents.
For each candidate, record source class, repository URL, paper/project identity match, immutable commit or release, license, task/dataset/split/metric/protocol compatibility, dependencies, compute assumptions, maintenance state, security findings, and selection or rejection reason.
Acquire only an immutable revision and do not initialize submodules or execute repository content:
python <skill-dir>/scripts/acquire_github_resource.py --workspace <workspace> \
--repo-url https://github.com/<owner>/<repo>.git \
--revision <commit-or-tag> \
--candidate-id <candidate-id>
Immediately perform static review:
python <skill-dir>/scripts/static_review_repository.py --workspace <workspace> \
--path <workspace>/resources/Remote_acquisition/<candidate-id> \
--output external_executor/report/phase_B/static_review.json
Treat static review as risk discovery, not proof of safety. Never run a fetched install script merely because the scan passed.
After remote acquisition and review, recompute whether the affected requirement is satisfied. If a required baseline, dataset, benchmark, metric, or checkpoint now has a passing candidate with suitable approval, do not proceed to reimplementation for that requirement.
Reimplement a baseline only as the final authorized path
Read references/baseline-reimplementation.md before proceeding.
Reimplementation is permitted only when:
- mode is
github_and_reimplementation;
baseline_reimplementation_allowed=true;- local resource review and authorized public remote searches are exhausted and recorded;
- core algorithm, objective, dataset/split, metric, and benchmark protocol are recoverable;
- license or access restrictions do not prohibit the work;
- the requirement does not demand an official implementation specifically.
For ResearchOS T5 external execution, baseline reimplementation is allowed by default after local and authorized remote searches are exhausted and recorded.
Create a provenance-first package:
python <skill-dir>/scripts/scaffold_reimplementation.py --workspace <workspace> \
--requirement-id <requirement-id> \
--baseline-name <baseline-name> \
--source <paper-or-supplement-reference> \
--source <benchmark-protocol-reference>
Before writing algorithm code, complete the generated specification, assumptions, paper-to-code map, fidelity risks, and validation plan. Keep engineering repairs separate from algorithmic choices. Provide independent configuration, unified evaluation integration points, sanity tests, and explicit uncertainty.
A reimplementation must be labeled executor_reimplementation or approximate_reproduction; never official, author_implementation, or protocol_equivalent without evidence.
Validate the package before considering it a candidate:
python <skill-dir>/scripts/validate_reimplementation_package.py --workspace <workspace> \
--path <reimplementation-package> --mode candidate \
--output external_executor/report/phase_B/validation_report.json
If the central mechanism or protocol cannot be recovered, mark the baseline unavailable rather than inventing missing details.
After reimplementation validation and independent review, recompute readiness. If the reimplemented candidate still cannot satisfy the minimum-loop requirement, mark Phase B blocked and request human-provided resource material or an approved scope/baseline decision from the root.
Place reimplementation products under resources/reproduction/<baseline>/.
Perform an independent resource review
Read references/resource-review-checklist.md and references/repository-static-review.md.
Review each candidate against its requirement, not against convenience. When independent workers are supported, use a reviewer that reads the requirement, source material, repository/data metadata, static review, adapters, and provenance directly. Otherwise perform a separate sequential review after acquisition work is complete.
For each reviewed candidate record:
verdict: pass | needs_fix | blocked;- identity/mechanism fidelity;
- task, dataset, split, preprocessing, metric, and protocol fidelity;
- metric direction and aggregation;
- fairness risks, including extra data, pretraining, tuning, or compute advantage;
- data leakage and evaluation contamination risks;
- source version, checksum, license, access, and redistribution status;
- security and dependency risks;
- adapter/patch effects;
- reimplementation assumptions and approximation level;
- evidence references;
approved_for, using only the vocabulary in the output contract.
A candidate may be approved for static_inspection or smoke_preparation while still being rejected for baseline_reproduction or formal_comparison.
A baseline may be marked executable, or approved for baseline_reproduction / formal_comparison, only when all seven conditions are explicitly satisfied: accessible code or model exists; an immutable revision can be locked; license is clear; environment or dependency information exists; dataset version and split are determined; metric implementation is determined; and at least one traceable result record exists.
Compute the readiness gate
Assemble external_executor/report/phase_B/resource_preparation_report.json using references/output-contract.md, then run:
python <skill-dir>/scripts/compute_resource_readiness.py --workspace <workspace> \
--report <workspace>/external_executor/report/phase_B/resource_preparation_report.json \
--write-back
Before final validation, build the source report for resource products under resources/:
python <skill-dir>/scripts/build_resource_source_report.py --workspace <workspace> \
--output external_executor/report/phase_B/resource_source_report.json \
--markdown-output external_executor/report/phase_B/resource_source_report.md \
--write-back
The report must classify products as byhand, Remote_acquisition, or reproduction.
Use these outcomes:
ready: every minimum-loop required resource has a passing review and suitable approval; no unresolved blocking license, security, access, identity, or protocol issue remains.partial: the minimum loop is feasible, but an approximate baseline, optional gap, constrained dataset, moderate fidelity risk, or claim-limiting condition remains.blocked: a required dataset, baseline identity, core protocol, license, security condition, permission, or access constraint prevents a valid minimum loop.
Never mark ready merely because files exist. Never delete, replace, or downgrade a required baseline. If replacement is not already authorized, record a replacement request or scope-change blocker and return to the root.
Validate and apply narrowly
Run:
python <skill-dir>/scripts/validate_resource_report.py --workspace <workspace> \
--report external_executor/report/phase_B/resource_preparation_report.json \
--output external_executor/report/phase_B/validation_report.json
python <skill-dir>/scripts/apply_resource_report.py --workspace <workspace> \
--report external_executor/report/phase_B/resource_preparation_report.json
The apply script updates only:
result_pack.resource_requirement_matrix
result_pack.resources
result_pack.baseline_candidates
result_pack.dataset_inventory
result_pack.material_gaps
result_pack.resource_risks
result_pack.resource_readiness
If validation fails, fix the report. Do not bypass the validator or edit sibling sections.
The final validation_report.json is the overall Phase B receipt (not merely a subpackage check): it must have schema_version=resource_preparation_validation.v1 and valid=true before returning to ResearchOS.
Return to the root
Return a compact child result:
child_skill=resource-and-baseline-preparation
status=complete|partial|blocked|failed
resource_readiness=ready|partial|blocked
report=external_executor/report/phase_B/resource_preparation_report.json
matrix=external_executor/resource_requirement_matrix.json
approved_requirement_ids=<ids>
constrained_requirement_ids=<ids>
blocking_requirement_ids=<ids>
claim_constraints=<ids-or-summary>
recommended_next_action=continue_to_experiment_design|continue_with_constraints|human_review|stop_and_report
The recommendation is advisory. research-execution owns manifest registration, executor status, checkpointing, scope-change gates, and the next dispatch.
Evidence, safety, and boundary rules
- Files, immutable revisions, metadata, and checksums are facts; model summaries are navigation aids.
- Separate source discovery, acquisition, static review, protocol review, and executed reproduction.
- Never execute unreviewed third-party code, install hooks, notebooks, containers, or download scripts.
- Never expose secrets in URLs, logs, search queries, manifests, or subprocess environments.
- Never fabricate a dataset, split, checkpoint, metric, paper result, license, repository identity, or official status.
- A different split, metric, preprocessing, task, or simplified algorithm is not an equivalent required baseline.
- Preserve rejected candidates, search failures, approximations, and material gaps as evidence.
- Keep original material read-only; use staged copies, wrappers, adapters, or patches under authorized paths.
- Do not write experiment results or claim support. Phase B produces readiness evidence only.
Resource map
references/acquisition-policy.md: authorization modes, privacy, network, dataset, license, and immutable acquisition rules.references/resource-requirement-contract.md: requirement taxonomy, fields, deficiency states, and blocking semantics.references/source-ranking-and-search.md: source tiers, query construction, candidate ranking, and search records.references/baseline-reimplementation.md: reimplementation preconditions, package contract, fidelity labels, and stop rules.references/resource-review-checklist.md: identity, protocol, fairness, license, access, and approval review.references/repository-static-review.md: static risk categories and non-execution review policy.references/output-contract.md: Phase B report, result-pack mapping, gate consistency, and child return contract.scripts/preflight_resources.py: validate Phase B prerequisites, authority, capability, and write boundaries.scripts/build_requirement_matrix.py: create a deterministic requirement scaffold from confirmed scope.scripts/inventory_local_resources.py: inventory local candidates without executing them.scripts/initialize_resource_report.py: create or refresh the durable Phase B report envelope without overwriting reviewed sections.scripts/stage_local_resource.py: copy accepted local material into resources/byhand/ with provenance.scripts/acquire_github_resource.py: fetch one immutable public Git revision without submodules or code execution.scripts/build_resource_source_report.py: classify products under resources/ by source category.scripts/static_review_repository.py: inspect repository metadata and risky patterns statically.scripts/scaffold_reimplementation.py: create a provenance-first baseline reimplementation package.scripts/validate_reimplementation_package.py: validate reimplementation completeness and labels.scripts/compute_resource_readiness.py: derive the Phase B gate from requirements and reviews.scripts/validate_resource_report.py: enforce the report and readiness contract.scripts/apply_resource_report.py: atomically update only Phase B result-pack sections.
