# Sopaper Evidence > Evidence-first research workflow for evidence discovery, source verification, and citation grounding. Use when the task requires searching, verifying, and organizing real papers, datasets, benchmarks, case studies, and project artifacts without fabricating results, citations, or claims. - Skill: `dvcrn/sopaper-evidence` (Agent Skill, multi-file: 44 files) - Install (CLI): `npx skillmds@latest add dvcrn/sopaper-evidence` - Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/sopaper-evidence/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Product & Planning - Author: dvcrn (https://skillmd.com/u/dvcrn) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/dvcrn/sopaper-evidence --- # Sopaper Evidence Sopaper Evidence is an evidence-first research skill. Its job is to build a reliable evidence pack before supporting any downstream paper outline, abstract, related work summary, experiment plan, or draft section. Version: `v0.6.1` ## Upstream source Canonical repository: `https://github.com/sheepxux/SoPaper-Evidence` This published skill bundle includes the helper scripts it references under `scripts/`. The GitHub repository remains the public source of truth for releases, examples, and issue tracking. ## Use this skill when - The user wants to turn a project into a paper without inventing evidence - The task requires finding prior papers, datasets, benchmarks, baselines, or case studies - The task requires mapping claims to verified sources - The task requires identifying evidence gaps before writing - The user wants related work or experiment planning grounded in real sources ## Hard rules - Do not fabricate papers, authors, venues, dates, citations, datasets, benchmarks, experiments, or numerical results - Prefer primary sources over summaries, reposts, or blog interpretations - Separate verified facts from inference and open questions - If evidence is missing, say it is missing and recommend what to collect next - Do not state that the user's method outperforms baselines unless there is explicit evidence - Every writing-oriented output must be traceable to evidence items ## Source priority Use the highest-quality source available for each claim. 1. User-provided project artifacts: experiment logs, tables, code, configs, internal notes 2. Primary external sources: papers, official docs, benchmark leaderboards, dataset pages, project repos 3. Secondary summaries: blogs, news posts, third-party explainers Read [references/source-priority.md](references/source-priority.md) when source quality or conflicts matter. Read [references/input-schemas.md](references/input-schemas.md) when stronger input structure is needed before running the workflow. ## Core workflow ### 1. Scope the task Collect or infer: - Project name - Research topic - Core problem - Method summary - Existing evidence and file paths - Target venue or paper style if known If the project scope is unclear, produce a short working scope and label assumptions. ### 2. Search for evidence Search for: - Prior work - Benchmarks and datasets - Baseline methods - Comparable case studies - Official metrics definitions - Relevant project artifacts in the local repository For each source, capture the title, URL or path, source type, and why it matters. Use [references/prior-work-search-playbook.md](references/prior-work-search-playbook.md) for a repeatable search process. For OpenClaw-specific work, use [references/openclaw-evidence-playbook.md](references/openclaw-evidence-playbook.md). ### 3. Verify and classify For each evidence item, classify it as: - `verified_fact` - `project_evidence` - `inference` - `unverified` Do not merge these labels. If a statement depends on inference, say so explicitly. ### 4. Extract structured evidence Use the schema in [references/evidence-schema.md](references/evidence-schema.md). At minimum, extract: - Claim or observation - Source - Evidence type - Scope and limitations - Relevance to the user's paper ### 5. Build the evidence map Organize findings into: - `related_work` - `datasets_and_benchmarks` - `baselines` - `case_studies` - `project_results` - `claim_to_evidence` - `evidence_gaps` Use [assets/claim-evidence-map-template.md](assets/claim-evidence-map-template.md) when the user needs a reusable deliverable. Use [assets/related-work-matrix-template.md](assets/related-work-matrix-template.md) when comparing papers, baselines, and benchmark coverage. Use [assets/experiment-gap-report-template.md](assets/experiment-gap-report-template.md) when the task requires prioritizing missing experiments before drafting. Use bundled `scripts/build_evidence_ledger.py` when the user already has markdown notes or source lists and needs a first-pass evidence ledger. Use bundled `scripts/generate_search_plan.py` when the user starts only with a topic and needs a first-pass evidence search plan. Use bundled `scripts/generate_topic_claims.py` when the user starts only with a topic and needs a cautious structured claims draft. Use bundled `scripts/search_external_sources.py` when the user needs a first-pass source list from a topic or search plan. Use bundled `scripts/fetch_external_sources.py` when raw URLs should be converted into structured source-note drafts before review. Use bundled `scripts/verify_source_notes.py` when fetched notes should be conservatively upgraded into page-level verified facts before entering the ledger. Use bundled `scripts/run_evidence_pipeline.py` when the user already has source files, claims, and optional result artifacts and wants one end-to-end draft pack. Use bundled `scripts/bootstrap_claim_map.py` when the user already has a claims list and a ledger draft and needs a first-pass claim map. Use bundled `scripts/triage_evidence_gaps.py` when the user needs a first-pass blocker/major/minor gap report from the current claims and evidence ledger. Use bundled `scripts/run_topic_evidence_pipeline.py` when the user wants the full topic-driven workflow from theme to search plan, source list, fetched notes, ledger, claim map, and gap report. Use bundled `scripts/validate_input_bundle.py` when the user has partially structured inputs and needs a quick schema check before running the pipeline. ### 6. Support writing Only after the evidence map is complete, support tasks such as: - contribution candidates - related work summary - abstract support points - experiment plan - paper outline Before writing, run the checks in [references/claim-audit-rules.md](references/claim-audit-rules.md). Use [assets/paper-outline-from-evidence-template.md](assets/paper-outline-from-evidence-template.md) when the user needs a draft-safe paper structure. ## Output requirements Unless the user asks for something else, default to this output shape: 1. `Evidence brief` 2. `Key sources` 3. `Claim-to-evidence map` 4. `Evidence gaps` 5. `Safe writing notes` 6. `Experiment gap report` when blocker gaps exist See the example set in: - [examples/openclaw-input.md](examples/openclaw-input.md) - [examples/openclaw-search-plan.md](examples/openclaw-search-plan.md) - [examples/openclaw-evidence-brief.md](examples/openclaw-evidence-brief.md) - [examples/openclaw-claim-map.md](examples/openclaw-claim-map.md) - [examples/openclaw-gap-report.md](examples/openclaw-gap-report.md) - [examples/openclaw-source-note.md](examples/openclaw-source-note.md) - [examples/openclaw-source-list.md](examples/openclaw-source-list.md) - [examples/openclaw-ledger-draft.md](examples/openclaw-ledger-draft.md) - [examples/openclaw-claims.md](examples/openclaw-claims.md) - [examples/openclaw-claims-structured.md](examples/openclaw-claims-structured.md) - [examples/openclaw-claim-map-draft.md](examples/openclaw-claim-map-draft.md) - [examples/openclaw-gap-report-draft.md](examples/openclaw-gap-report-draft.md) - [examples/openclaw-paper-outline.md](examples/openclaw-paper-outline.md) ## Writing constraints When supporting downstream paper writing: - Tie each major claim to one or more evidence items - Avoid precise quantitative wording unless the number is verified - Mark missing comparisons, missing ablations, and missing real-world validation - Prefer conservative wording over overstated conclusions ## OpenClaw-specific guidance When the user is working on OpenClaw or a similar embodied AI / robotics project, prioritize: - manipulation benchmarks - long-horizon task evidence - policy or planner comparisons - real-world versus simulation evidence - ablations on perception, planning, or control components Do not assume OpenClaw has capabilities, datasets, or benchmark wins unless they are present in project artifacts or verified sources. Use [references/benchmark-baseline-checklist.md](references/benchmark-baseline-checklist.md) before accepting benchmark-fit or baseline coverage claims. Use [references/evidence-gap-triage.md](references/evidence-gap-triage.md) when deciding whether to keep drafting or stop and report blockers.