appraising

name	appraising
description	Use when appraising included papers with deep critical analysis. Dispatches 6 analysis agents in parallel, fact-checks, and synthesizes into analysis.md. TRIGGERS: appraise, appraising, critical appraisal, avaliar paper, analise critica, deep analysis

Appraising

Critical appraisal of included papers using 8 specialized agents.

Overview

After extraction (condense + tag), appraise papers with deep critical analysis. Each paper gets 6 parallel analysis agents, a fact-checker, and a synthesizer.

Core principle: 6 agents in parallel -> fact-checker -> paper-appraiser.

Prerequisite: Papers must have fulltext.pdf. Works best after extraction (scimesh:extracting).

Output: {paper_path}/analysis/ directory with 7 markdown files + analysis.md synthesis.

Directory Structure

{review_path}/
└── papers/
    └── {year}/
        └── {paper-slug}/
            ├── index.yaml
            ├── fulltext.pdf
            ├── condensed.md        # From extracting
            └── analysis/           # From appraising
                ├── scope-extractor.md
                ├── methodology-extractor.md
                ├── results-extractor.md
                ├── reproducibility-extractor.md
                ├── contribution-extractor.md
                ├── critical-reader.md
                └── analysis.md     # Final synthesis

Agents

Agent	Model	Responsibility	Input	Output
scope-extractor	opus	Hypothesis, RQs, scope, biases	PDF	analysis/scope-extractor.md
methodology-extractor	opus	Study design, datasets, baselines, method	PDF	analysis/methodology-extractor.md
results-extractor	opus	Quantitative results, ablations, significance	PDF	analysis/results-extractor.md
reproducibility-extractor	opus	Code, data, weights, hyperparams, compute	PDF	analysis/reproducibility-extractor.md
contribution-extractor	opus	Contributions, novelty, citation context	PDF	analysis/contribution-extractor.md
critical-reader	opus	Claims vs evidence, assumptions, generalizability	PDF	analysis/critical-reader.md
fact-checker	opus	Verify all 6 outputs against paper	PDF + 6 outputs	Annotates 6 files inline
paper-appraiser	opus	Synthesize all into final appraisal	6 annotated outputs	analysis/analysis.md

Workflow

digraph appraising {
    rankdir=TB;
    node [shape=box];

    input [label="Paper with fulltext.pdf"];
    mkdir [label="Create analysis/ dir"];

    scope [label="scope-extractor"];
    method [label="methodology-extractor"];
    results [label="results-extractor"];
    repro [label="reproducibility-extractor"];
    contrib [label="contribution-extractor"];
    critical [label="critical-reader"];

    checker [label="fact-checker" shape=hexagon];
    synth [label="paper-appraiser" shape=doubleoctagon];
    output [label="analysis/analysis.md" shape=note];

    input -> mkdir;
    mkdir -> {scope method results repro contrib critical};
    {scope method results repro contrib critical} -> checker;
    input -> checker;
    checker -> synth;
    synth -> output;
}

Launching Agents

Step 1: Create the analysis directory

mkdir -p {paper_path}/analysis

Step 2: Dispatch 6 agents in parallel (single message, 6 Agent calls)

# ALL 6 in a single message for maximum parallelism
Agent(
    subagent_type="scimesh:scope-extractor",
    prompt=f"Analyze the paper at: {paper_path}/fulltext.pdf\nSave output to: {paper_path}/analysis/",
    description=f"Scope: {paper_slug}"
)
Agent(
    subagent_type="scimesh:methodology-extractor",
    prompt=f"Analyze the paper at: {paper_path}/fulltext.pdf\nSave output to: {paper_path}/analysis/",
    description=f"Method: {paper_slug}"
)
Agent(
    subagent_type="scimesh:results-extractor",
    prompt=f"Analyze the paper at: {paper_path}/fulltext.pdf\nSave output to: {paper_path}/analysis/",
    description=f"Results: {paper_slug}"
)
Agent(
    subagent_type="scimesh:reproducibility-extractor",
    prompt=f"Analyze the paper at: {paper_path}/fulltext.pdf\nSave output to: {paper_path}/analysis/",
    description=f"Repro: {paper_slug}"
)
Agent(
    subagent_type="scimesh:contribution-extractor",
    prompt=f"Analyze the paper at: {paper_path}/fulltext.pdf\nSave output to: {paper_path}/analysis/",
    description=f"Contrib: {paper_slug}"
)
Agent(
    subagent_type="scimesh:critical-reader",
    prompt=f"Analyze the paper at: {paper_path}/fulltext.pdf\nSave output to: {paper_path}/analysis/",
    description=f"Critical: {paper_slug}"
)

Step 3: Wait for all 6 to complete

Step 4: Dispatch fact-checker

Agent(
    subagent_type="scimesh:fact-checker",
    prompt=f"""Fact-check the 6 agent outputs against the paper.
Paper path: {paper_path}/fulltext.pdf
Agent outputs directory: {paper_path}/analysis/""",
    description=f"Fact-check: {paper_slug}"
)

Step 5: Wait for fact-checker to complete

Step 6: Dispatch paper-appraiser

Agent(
    subagent_type="scimesh:paper-appraiser",
    prompt=f"""Read all markdown files from: {paper_path}/analysis/
Write the final synthesis to: {paper_path}/analysis/analysis.md""",
    description=f"Appraise: {paper_slug}"
)

Step 7: Confirm to user with path to analysis.md

Batch Processing

Process multiple papers -- but each paper's pipeline is sequential (6 parallel -> checker -> appraiser). Multiple papers can run their pipelines concurrently:

Paper A: [6 agents] -> [fact-checker] -> [appraiser]
Paper B: [6 agents] -> [fact-checker] -> [appraiser]
Paper C: [6 agents] -> [fact-checker] -> [appraiser]

Recommended batch size: 2-3 papers at a time (each paper dispatches 6 opus agents).

Pre-Appraisal Check

Ask user before starting:

{
    "question": f"Found {with_pdf_count} included papers with PDFs. Appraise {with_pdf_count} papers with deep critical analysis? (8 opus agents per paper)",
    "header": "Appraise?",
    "options": [
        {"label": f"Yes, appraise all {with_pdf_count} (Rec)", "description": "Deep analysis of all included papers"},
        {"label": "Select specific papers", "description": "Choose which papers to appraise"},
        {"label": "Skip", "description": "Continue without appraisal"}
    ],
    "multiSelect": False
}

Progress Tracking

Create Tasks for appraisal visibility:

for paper in papers_to_appraise:
    TaskCreate(
        subject=f"Appraise: {paper_title[:40]}...",
        description=f"6 extractors -> fact-checker -> appraiser for {paper_slug}",
        activeForm=f"Appraising {paper_slug}..."
    )

Handling Already-Appraised Papers

Check for existing analysis/analysis.md before dispatching:

Glob(pattern="{review_path}/papers/**/analysis/analysis.md")

Skip papers that already have analysis.md unless user explicitly requests re-appraisal.

Common Mistakes

Mistake	Fix
Running 6 agents sequentially	ALL 6 must be in a single parallel dispatch
Passing content in prompt to appraiser	Agents save to files -- appraiser reads from analysis/
Forgetting to create analysis/ dir	mkdir -p before dispatching agents
Reading the paper yourself	The agents read it -- you just orchestrate
Dispatching too many papers at once	2-3 papers max (each = 6 opus agents)
Skipping fact-checker	NEVER skip -- it catches hallucinated numbers and cross-agent inconsistencies

Next Step

After appraisal, the analysis.md files enrich scimesh:synthesizing -- the narrative synthesis can reference epistemic honesty scores, fragility verdicts, and critical analysis from appraisals.