Assess Paper

Assess Paper

The single command that tells an author where they stand and explains why, so a non-expert isn't left staring at five separate tool outputs. It does not re-implement any check; it runs the specialist skills and turns their results into one readable Paper Health Report that leads with strengths, then risks, then a ranked to-do list.

Pairs with orchestrate-paper (which drives the full lifecycle) — assess-paper is the read-only "where am I right now" snapshot you can run at any point. Copilot, not pilot: it reports and explains, it never edits the paper or submits anything.

When to use

• The author wants one overview instead of running five skills and stitching the results together.
• Early ("how far off am I?") or late ("am I ready to submit?").
• A co-author or advisor wants a quick, honest state-of-the-paper.

When NOT to use it (say this plainly)

• You want one specific check — run that skill directly (e.g. just citations → verify-citations).
• You want fixes applied — this skill only diagnoses; hand each fix to the skill that owns it.
• You want an acceptance probability or award forecast — no tool can give one honestly; this skill refuses to.

Inputs

• The draft: a .tex source tree, a compiled PDF, or a readable draft. Processed transiently — never copied into this repo.
• The target venue and track (what counts as "ready" differs by venue). Ask if unstated; run against the nearest family default and say so if no profile exists.
• Optional: .paper-memory/profile.yml (positioning) and lessons.md (recurring weaknesses), if present.

What it consolidates

Process

1. Confirm the target (venue + track) and read .paper-memory/profile.yml if present, so the assessment matches the paper's positioning — a theory paper and an applied paper are judged differently.
2. Run the checks that apply to the draft's stage. Compliance and citations/originality are integrity gates; benchmark and mock-review are quality reads. Skip and say so if an input is missing (e.g. no .bib yet) — never fabricate a result to fill a gap.
3. Synthesize the Paper Health Report (do not dump five raw outputs):
   • Overall read — one honest paragraph: roughly where the paper stands and the single most important thing to do next. Never an acceptance probability.
   • Top strengths (lead with these) — 3–5 concrete things the paper does well, drawn from the mock review and benchmark, each tied to a section, so the author knows what to protect while editing.
   • Biggest risks — ranked by impact × ease of fix, each linking to the skill that fixes it.
   • Scorecard + gates — the venue-fit index, mock-review decision-risk band, and a clear pass / flag on each integrity gate (citations, originality, desk-reject).
   • Do next — a short ordered checklist.
4. Write the report to paper-workspace/review/paper-health-report.md and present the summary in chat.

Any venue fact that flows through from the sub-skills (a page limit, a deadline, a required section) carries its source and confidence label exactly as the sub-skill reported it — do not restate a venue rule the underlying skill could not verify.

Worked mini-example

Input: a draft paper.tex + refs.bib, targeting sigspatial-2026 (full track). The synthesized report opens like this:

markdownCopy code
# Paper Health Report — paper.tex → SIGSPATIAL 2026 (full track) · 2026-06-21

## Overall read
Structurally this reads like a SIGSPATIAL full paper and the core method is
clearly framed. The one thing holding it back is the evaluation: a single
dataset with no significance test is the weakness every mock reviewer flagged.
Fix that before anything else.

## Top strengths (protect these)
- Crisp contribution list, 3 claims each mapped to a section (§1) — mock panel
  praised the framing.
- Strong, reproducible artifact: code + seeds released (§6) — benchmark scored
  reproducibility 9/10, above the exemplar median.
- Clear problem motivation tied to a real spatial workload (§2).

## Biggest risks (ranked by impact × ease)
1. Single-dataset evaluation, no significance test — add a second dataset +
   variance. → simulate-reviewers (soundness), benchmark-paper (evaluation)
2. 2 references unresolvable on Crossref/DBLP (likely wrong year). → verify-citations
3. Page count at 9.3 / 9 — over the limit. → preflight-check, fit-page-limit

## Scorecard + gates
- Venue-fit index: 7.1/10 — "structurally in line; evaluation is the gap"
- Mock decision-risk: borderline-reject (no champion)
- Gates: citations FLAG (2 unresolvable) · originality PASS · desk-reject FLAG (over page limit)

## Do next
1. Resolve the 2 citations (verify-citations) — quick, removes an integrity flag.
2. Trim to the page limit (fit-page-limit).
3. Add the second dataset + significance test (the real lever).

Output

A single paper-health-report.md: overall read → strengths → ranked risks → scorecard & gates → do-next checklist. Each finding names the underlying skill so the author can drill in. Plain language throughout — the goal is the author understanding their paper, not a wall of metrics.

Guardrails

• Strengths are mandatory and come first. An assessment that is all problems misleads the author about what to preserve — surface what genuinely works, but never invent strengths to pad.
• Honest readiness read only — never a probability of acceptance or an award (see benchmark-paper, simulate-reviewers). Refuse if asked, and explain why.
• Integrity gates (fabricated citations, plagiarism, desk-reject blockers) are reported as blockers, not averaged away by good scores elsewhere.
• It reports and explains; it never edits the paper or submits anything. Hand fixes to the specific skills.
• Process the paper transiently; never copy paper text into this repo.

Memory

Uses the shared .paper-memory/ convention described by paper-memory-convention.md.

• At start: read profile.yml (positioning) and lessons.md (recurring weaknesses) so the report is personalized and tracks progress across drafts — lead with any recurring weakness.
• At end: append durable takeaways in the shared format - [YYYY-MM-DD] (assess-paper | <scope>) finding -> recommendation via reflect-and-improve's reflect_log.py append (it dedupes and dates). A weakness that persists across drafts is recurring; a one-time gap is this-paper. Log lasting takeaways, not the full report or per-run scores.
• Create .paper-memory/ on demand; it is local-only and never uploaded.