.cursor/skills/fix-last-task/SKILL.md
Analyze and fix issues in the last completed task — find missed problems, fix errors, perform session review. Use when user reports errors after task was marked as done, when completion report had high confidence but result has issues, or when AI missed obvious problems.
npx skillsauth add dmitryprg-ai/cursor-develop-autorules fix-last-taskInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Principle: ANALYZE FAILURE BEFORE FIXING.
core-master.mdc (Cross-check + 4 questions)Apply 5 Whys from _base-5wh.mdc — dig at least 5 levels to find why the issues were missed.
Create TODO list — one item per missed issue, starting with critical ones.
For each TODO:
[before]% → [after]% — [reason]Apply Cross-check (open every changed file) + Final Challenge (4 questions).
Apply session-review skill fully. Record improvement to .cursor/data/improvements-backlog.md.
Full DONE block with: issues found, fixes applied, root cause, improvement recorded, final confidence.
For detailed analysis and review templates, see templates.md.
development
Scan codebase for technical debt and fix safely with TDD. Use to find oversized files, duplicated code, code smells, and refactor safely. Workflow - SCAN, TEST CASES, REFACTOR, VERIFY. Keywords - techdebt, tech debt, duplicates, code quality audit.
development
Test-Driven Development workflow with strict Red-Green-Refactor cycle. Use when developing features with TDD, writing tests before code, or when test-driven approach is needed. MANDATORY order - test cases table BEFORE code, failing tests BEFORE implementation.
testing
Review work session quality and capture improvements. Use at end of session, after large tasks, after series of errors, or when user asks for session review, retrospective, lessons learned. Records improvements to backlog.
data-ai
Analyze data, investigate datasets, work with CSV/parquet/pandas/dataframes. Use when analyzing data, exploring datasets, running experiments, or when user mentions data, analysis, parquet, csv, pandas, dataframe, statistics, investigation.