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npx skills add https://github.com/WILLOSCAR/research-units-pipeline-skills --skill section-logic-polisher
Purpose: close the main “paper feel” gap that remains even when a subsection is long and citation-dense:
This is a local, per-H3 polish step that happens after drafting and before merging.
Note: if the main problem is paragraph-count overgrowth, run
paragraph-curator after this check. It only merges adjacent paragraph
boundaries and preserves all prose; semantic redundancy still belongs to the
owning writer.
Blocking (must fix):
Non-blocking (diagnostic only):
You are the logic editor for one survey subsection.
Your job is to make the subsection read like a single argument:
- paragraph 1 commits to a clear thesis (content claim)
- each paragraph has an explicit logical relation to the previous one
- bridges are content-bearing (contrast/causal/implication), not slide narration
Constraints:
- do not add new citations
- do not change citation keys
- do not invent facts
Editing lens:
- if a paragraph does not advance the argument (claim/contrast/eval/limitation), compress or delete it
- if a transition is empty, rewrite it as a content-bearing bridge
sections/ (expects H3 body files like S<sec>_<sub>.md)outline/subsection_briefs.jsonl (use thesis + paragraph_plan[].connector_phrase as intent)outline/writer_context_packs.jsonl (preferred; has trimmed anchors/comparisons + must_use)output/SECTION_LOGIC_REPORT.md (PASS/FAIL for thesis; connector stats shown for diagnosis)Manual / LLM-first (in place):
sections/ (e.g., sections/S<sec>_<sub>.md) to fix thesis/bridges (no new citations; keep keys stable)1) Run the checker script to surface the exact failing files.
2) For each failing H3 file:
sections/S<sec>_<sub>.mdoutline/subsection_briefs.jsonl as the source of truth for the subsection thesis and paragraph-plan intent.outline/writer_context_packs.jsonl to stay aligned with must_use anchors/constraints (no new cites).This subsection argues/surveys ....1) claim / tension
2) why it matters (protocol/evaluation relevance)
3) how the subsection will resolve it (what contrasts/anchors it will use)
Next, we ..., We now turn to ...).3) Rerun the checker until output/SECTION_LOGIC_REPORT.md is PASS, then
proceed to paragraph-curator, evaluation-anchor-checker, the final
argument-selfloop snapshot, and merge.
Bad (topic setup only):
Tool interfaces vary across agent systems, and many recent works explore different designs.Better (conclusion-first claim):
A central tension in tool interfaces is balancing expressivity with verifiability; as a result, interface contracts often determine which evaluation claims transfer across environments.Bad (meta narration):
This subsection argues that memory is important for agents.Better (content claim):
Memory designs trade off retrieval reliability against write-time contamination, and this trade-off shows up as distinct failure modes under fixed evaluation protocols.Bad (no relation):
X does ... (para 2)Y does ... (para 3)Better (explicit tie):
Whereas X optimizes for <axis>, Y shifts the bottleneck to <axis>; under fixed budgets, this changes whether the reported gains reflect better planning or simply more expensive search.output/SECTION_LOGIC_REPORT.md shows - Status: PASSTODO/…/...) or outline meta markers (Intent:/RQ:/Evidence needs:)uv run python .codex/skills/section-logic-polisher/scripts/run.py --workspace <workspace>Notes:
--workspace <dir>--unit-id <U###>--inputs <semicolon-separated>--outputs <semicolon-separated>--checkpoint <C#>uv run python .codex/skills/section-logic-polisher/scripts/run.py --workspace <workspace>
uv run python .codex/skills/section-logic-polisher/scripts/run.py --workspace <workspace> --outputs output/SECTION_LOGIC_REPORT.md
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take willoscar/section-logic-polisher from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.