# Paper Discourse Graph > Audit LaTeX papers as discourse graphs when paragraph flow, story continuity, readability, reader-state breadcrumbs, payoff chains, or figure/equation placement matter. Use when the user asks whether a section feels abrupt, wasteful, machine-generated, monotonous, hard to parse, or wants to zoom in/out across paragraphs before editing. - Skill: `shubham0704/paper-discourse-graph` (Agent Skill, multi-file: 16 files) - Install (CLI): `npx skillmds@latest add shubham0704/paper-discourse-graph` - Raw SKILL.md: https://api.skillmd.com/api/skills/shubham0704/paper-discourse-graph/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: shubham0704 (https://skillmd.com/u/shubham0704) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/shubham0704/paper-discourse-graph --- # Paper Discourse Graph Use this skill to review a paper section as a reader-state graph, not only as a linear outline. The goal is to see what each paragraph plants, what it pays off, where notation appears before intuition, and where a justification detour interrupts the main story. ## 1. Workflow ### 1.1 Establish scope Identify the source `.tex` file or section to audit. If the user wants discussion before editing, produce findings and proposed interventions only. Look for project-local guidance before running a generic pass: - `AGENT_REVIEW_BRIEF.md` - `docs/*review*brief*.md` - `tools/discourse_graph/profiles/*.json` - paper-specific profiles or terminology docs ### 1.2 Choose a profile Use the most specific available profile: 1. user-provided `--profile` 2. project-local profile, if present 3. bundled `references/profiles/cphast.json` for C-PHAST drafts 4. bundled `references/profiles/default_scientific_paper.json` Profiles define the central reader question, audience needs, domain terms, and topic checks. Keep project taste in profiles, not in the engine. For methods, theory, or appendix sections where paragraphs hand off to notation and equations, also read `references/formal_block_flow.md`. When authoring or revising source files and the user wants durable machine-readable breadcrumbs for later passes, read `references/source_semantic_comments.md`. For late-stage paragraph-by-paragraph refinement, task breakdowns for fresh agents, or requests to zoom into each paragraph with a fresh mind, read `references/paragraph_refinement_tasks.md`. ### 1.3 Run the CLI From this skill directory: ```bash python scripts/discourse_graph_audit.py \ --section \ --profile references/profiles/default_scientific_paper.json \ --out \ --json-out ``` For C-PHAST: ```bash python scripts/discourse_graph_audit.py paper/sec/3_method_arxiv.tex \ --section 3 \ --profile references/profiles/cphast.json \ --out paper/docs/plans/discourse_graph_sec3.md \ --json-out paper/docs/plans/discourse_graph_sec3.json ``` Optional LLM chunks: ```bash python scripts/discourse_graph_audit.py \ --section \ --out \ --llm-jsonl ``` ### 1.4 Interpret the report Treat the graph as triage, not truth. The labels are prompts for manual review. Important node roles: - `scene`: visible example or running situation - `question`: planted reader question - `claim`: assertion or section-level promise - `mechanism`: method component or construction - `definition` / `notation`: formal object introduction - `justification`: reason, proof sketch, or defensive support - `evidence`: figure, table, theorem, algorithm, or equation block - `boundary`: scope limit or condition - `payoff`: answer to earlier setup - `handoff`: transition to the next object - `semantic_comment`: source-only `% DG:` breadcrumb used to record author intent without rendering in the PDF Important risk labels: - `abrupt`: weak continuity into the current block - `detour`: useful material that may interrupt the active story - `unpaid_question`: a planted question without nearby payoff - `premature_notation`: dense formal terms before visible grounding - `weak_parent_link`: paragraph is weakly tied to its heading - `bridge_candidate`: possible bridge that may rescue a detour - `unconnected_evidence`: figure/table/algorithm not clearly recalled - `context_debt`: named setting or experiment appears before enough local setup - `appendix_claim_leak`: appendix-only evidence is doing main-text argumentative work - `symbol_alias_confusion`: profile-tracked symbols gain a new subscript or variant without explaining the relationship - `coarse_algorithm_reference`: algorithm behavior is described without line, stage, equation, or step anchors - `missing_formal_setup`: equation-like block appears before prose creates the need for it - `missing_formal_payoff`: equation-like block is not followed by a nearby interpretation - `symbol_scope_debt`: formal block introduces several new symbols without nearby scope, type, or dependency prose - `role_mismatch`: prose promises intuition or readability but hands off to dense notation without enough setup - `equation_overload`: formal block introduces many symbolic objects at once - `dangling_reference`: paragraph starts from this/these/such object without a clear local antecedent ## 2. Review Taste For each paragraph or block, ask: - What does the reader know right now? - What question did the previous block plant? - Does this block answer, sharpen, or defer that question? - Is the payoff close enough, or is the reader carrying too much load? - Is this block story-bearing, justification-bearing, evidence-bearing, implementation-bearing, or boundary-bearing? - If this became a task for a fresh agent, what local context, invariants, and claim boundaries would that task need? Prefer a chain where understanding progressively accumulates: visible scene -> reader question -> mechanism -> formal object -> evidence -> payoff -> next question. Use visible language before formal terms. A dense equation should feel inevitable because the previous paragraph made the need for that object obvious. For formal blocks, check the local loop: paragraph motivates need -> notation names object -> equation commits to definition/update/loss/constraint/readout -> paragraph interprets what changed for the reader. ## 3. Output Format Report findings with: - file and line range - live reader question - risk label - why the current placement breaks or helps the story - smallest proposed intervention Use labels: - **Payoff**: answers a live reader question - **Plant**: intentionally creates the next question - **Bridge**: connects intuition to a formal object - **Detour**: useful but not needed for the current story step - **Stray**: does not support the current claim chain When the user asks to discuss, do not edit. Rank the highest-value changes and ask which ones to land. ## 4. Bundled Resources - `scripts/discourse_graph_audit.py`: command-line entry point - `scripts/discourse_graph/`: dependency-free LaTeX segmentation, heuristics, schema, and rendering code - `references/formal_block_flow.md`: detailed rubric for paragraphs, notation, equations, interpretation, and consistency - `references/paragraph_refinement_tasks.md`: late-stage per-paragraph task protocol for fresh review passes - `references/source_semantic_comments.md`: optional `% DG:` comment convention for source-level discourse breadcrumbs - `references/profiles/default_scientific_paper.json`: general profile - `references/profiles/cphast.json`: C-PHAST profile with typed factors, charts, ports, PHASTCore, and action-card checks ## 5. Pitfalls - Do not treat lexical overlap as a correctness judgment. - Do not force every paragraph to use explicit transition phrases. - Do not remove rigorous caveats merely because they are detours; decide whether they belong later, in an appendix, or in a tighter boundary sentence. - Do not let profile-specific vocabulary leak into unrelated papers. - Keep paper-specific notation families in the profile, for example `risk_rules.confusable_symbol_bases`, not in the reusable engine.