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Build review-training activities in plain language. Learners spot issues in contracts, charts, code snippets, ad copy, or clinical notes. AI grades against your rubric and credits learners for the issues they find and the false positives they avoid.
A training activity where the learner reads a document, contract, chart, code snippet, ad copy, lab note, and marks the issues. Both true findings and false-positive flags are graded, so the learner is trained to be precise as well as thorough.
From a plain-English description of what good and bad look like, to a SCORM-ready review exercise. No PDF annotation tool integration required.
Paste a contract, code snippet, chart, ad copy, or clinical note. The tool renders it in a review-friendly UI with highlight and annotation tools.
Name what counts as an issue, missing indemnification clause, off-by-one error, vague claim, missing CTA. The AI seeds them at the density and difficulty you set.
"Add a subtle indemnification gap." "Throw in two false-flag items that look risky but aren't." Iterate by talking with your coding agent, not by editing markup.
One command produces a SCORM 1.2 zip. Upload to any SCORM LMS. Precision, recall, and total score post back to the gradebook automatically.
The same toolchain powers contract-review training, code-review onboarding, ad-copy QA, clinical-note auditing, and editorial fact-checking drills.
Practice spotting missing indemnification clauses, ambiguous IP terms, hidden auto-renewals. Trained on contracts that look like the ones associates actually see.
New engineers practice spotting bugs, security issues, and style violations on realistic diffs before reviewing real PRs. Calibrate the issue density to the team's standards.
Train reviewers to catch unsupported claims, regulatory red flags, and tone problems in ad copy and product pages. Especially useful in regulated industries.
Practice spotting documentation errors, coding mismatches, and clinical-quality issues in chart notes, the kind of review training payers and quality teams need.
Train editorial staff to flag unverified claims, missing sourcing, and quote-accuracy issues. Realistic articles with seeded errors at the density you choose.
Practice spotting classification errors, suspicious year-over-year movements, and disclosure gaps in realistic statements.
When the review ends, the AI scores recall (issues caught) and precision (issues correctly identified vs falsely flagged) separately. Total score weights both, so learners can't game it by flagging everything.
Update the rubric, issue density, or precision-vs-recall weighting any time. Re-export the SCORM package and the next attempt grades against the new rules.
Every issue caught counts toward recall. Every false-positive flag counts against precision. Total score weights both, so learners can't game it by flagging everything.
The AI inserts issues into your source document based on the rubric and difficulty you set. You review the seeded issues before publishing.
Start with obvious issues and progressively introduce subtler ones. The learner sees how their precision degrades as issues get sneakier.
Add issue types, change density, raise the difficulty, or adjust the precision-vs-recall weight, just describe the change in your own words. Your coding agent reseeds the document and re-exports the SCORM package without you ever editing markup.
When you're happy, export as a SCORM 1.2 package for your LMS, or use the same HTML bundle standalone.
0.6, recall 0.4. Total grader recompiled.Paste any prompt into your coding agent to get a complete document-review activity as a single self-contained HTML file. Adjust source doc, issue rubric, or weights in plain language.
An associate-level review activity on a 6-page SaaS MSA with 8 seeded issues and 2 false-flag clauses. Score weights precision over recall.
Using /document-review, build a SaaS MSA review activity with 8 issues and 2 false flags. Score with precision-weighted rubric.
A 200-line backend PR with 6 seeded issues (one security, two correctness, three style) and 3 false-flag passages that look risky but aren't.
Using /document-review, build a backend PR review activity with 6 issues across security, correctness, and style. Score precision and recall separately.
An ad-copy review for a regulated pharma product with 7 seeded issues (unsupported claim, off-label hint, fair-balance gap) and 2 false flags.
Using /document-review, build a pharma ad-copy review activity with 7 issues and 2 false flags. Score on fair-balance, on-label, and substantiation.
A 1-page inpatient progress note with 5 seeded documentation/coding issues and 2 false flags. Grader weights recall on coding errors.
Using /document-review, build an inpatient note audit with 5 issues across documentation and coding. Score recall-weighted on coding errors.
A 600-word news article with 6 seeded fact issues (unsourced claim, quote attribution error, date inconsistency) and 2 false flags.
Using /document-review, build a 600-word fact-check activity with 6 issues across sourcing and accuracy. Score on attribution and verification.
A 2-page excerpt from a 10-K with 5 seeded issues (classification, YoY anomaly, disclosure gap) and 3 false flags. Aimed at audit-staff training.
Using /document-review, build a 10-K disclosure review with 5 issues and 3 false flags. Score on classification, YoY anomaly, and disclosure completeness.
Every document-review activity exports as a standards-compliant SCORM 1.2 package. Upload the zip, assign it like any other course activity, and precision, recall, and total scores flow back to the gradebook automatically.
cmi.core.score.raw and cmi.core.lesson_statusAuthoring, precision and recall, LMS compatibility, document formats, model choice, answered directly.
A document-review training activity is an interactive exercise where the learner reads a document and marks the issues. Both correctly caught issues (true positives) and incorrectly flagged passages (false positives) are graded, training the learner to be precise as well as thorough.
Anything text-based or structured, contracts, code snippets, charts, ad copy, clinical notes, financial statements, lab reports, editorial articles. The activity renders the document in a review-friendly UI and adapts to the document type.
The AI inserts issues into your source document based on the rubric and difficulty you specify. You review and approve the seeded issues before publishing the activity.
Every issue caught contributes to recall. Every passage incorrectly flagged contributes to a precision penalty. Total score is a weighted combination you control, so learners are trained to be accurate, not just thorough.
Yes. Every activity exports as a SCORM 1.2 package, which works in Cornerstone, Moodle, Canvas, TalentLMS, Docebo, Brightspace, and every other SCORM-compliant LMS. Precision, recall, and total score post back automatically.
Yes. Paste your own contracts, code, or copy and let the AI seed practice issues into them. This is how teams produce review training that looks exactly like the documents reviewers will see at work.
The bundles run on whichever frontier model you configure, Claude, GPT, Gemini, or a self-hosted model. The model is swappable; the rubric, seeded issues, and grading logic are stored in plain text and are model-agnostic.
Document Review is in active development. It will install via your coding agent with a single command, the same way the existing role-play skill works today. Add your email below and we'll let you know the day it ships.