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Get Started Free →TLDR CLI komut referansi. tree, structure, search, extract, context, cfg, dfg, slice, calls, impact, dead, arch, imports, importers, diagnostics, change-impact komutlari.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -9% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 114% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 58% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -56% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -58% | 0% |
You have tldr available on PATH for token-efficient code analysis.
bash# Core analysis tldr tree [path] # File tree tldr structure [path] --lang <lang> # Code structure (codemaps) tldr search <pattern> [path] # Search files tldr extract <file> # Full file info tldr context <entry> --project . # LLM-ready context # Flow analysis tldr cfg <file> <function> # Control flow graph tldr dfg <file> <function> # Data flow graph tldr slice <file> <func> <line> # Program slice tldr calls [path] # Cross-file call graph # Codebase analysis tldr impact <func> [path] # Who calls this function? (reverse call graph) tldr dead [path] # Find unreachable/dead code tldr arch [path] # Detect architectural layers # Import analysis tldr imports <file> # Parse imports from a file tldr importers <module> [path] # Find all files that import a module # Quality & testing (NEW) tldr diagnostics <file|path> # Type check + lint (pyright/ruff) tldr change-impact [files...] # Find tests affected by changes
tldr structure . to see what existstldr search "pattern" instead of grep for structured resultstldr cfg for complexity, tldr dfg for data flowtldr slice file.py func 42 to find what affects line 42tldr context entry_point to get relevant codetldr impact func_name before refactoring to see what would breaktldr dead src/ to find unused functions for cleanuptldr arch src/ to understand layer structuretldr imports file.py to see what a file importstldr importers module_name src/ to find who imports a moduletldr diagnostics . to catch type errors before running teststldr change-impact to run only affected testsSupports: python, typescript, go, rust
bash# 1. See project structure tldr tree src/ --ext .py # 2. Find relevant code tldr search "process_data" src/ # 3. Get context for a function tldr context process_data --project src/ --depth 2 # 4. Understand control flow tldr cfg src/processor.py process_data # 5. Before refactoring - check impact tldr impact process_data src/ --depth 3 # 6. Find dead code to clean up tldr dead src/ --entry main cli
bashtldr impact <function> [path] --depth N --file <filter>
Shows reverse call graph - all functions that call the target. Useful before refactoring.
bashtldr dead [path] --entry <patterns>
Finds functions never called (excluding entry points like main, test_, etc.)
bashtldr arch [path]
Analyzes call patterns to detect:
bashtldr imports <file> [--lang python]
Parses all import statements from a file. Returns JSON with module names, imported names, aliases.
bashtldr importers <module> [path] [--lang python]
Finds all files that import a given module. Complements tldr impact which tracks function calls - this tracks imports.
bashtldr diagnostics <file> # Single file tldr diagnostics . --project # Whole project tldr diagnostics src/ --format text # Human-readable output tldr diagnostics src/ --no-lint # Type check only
Runs pyright (types) + ruff (lint) and returns structured errors. Use before tests to catch type errors early.
bashtldr change-impact # Auto-detect (session/git) tldr change-impact src/foo.py # Explicit files tldr change-impact --session # Session-modified files tldr change-impact --git # Git diff files tldr change-impact --run # Actually run affected tests
Finds which tests to run based on changed code. Uses call graph + import analysis.
All commands output JSON (except context which outputs LLM-ready text, diagnostics --format text for human output).
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 10,046 | 5,603 | -44% | 1 | 1 | 0% | 1,461 | 1,681 | +15% | 0 | 0 | — |
case-02 | fail→fail | 7,456 | 4,092 | -45% | 1 | 1 | 0% | 1,266 | 1,362 | +8% | 0 | 0 | — |
case-03 | fail→fail | 5,085 | 15,880 | +212% | 1 | 1 | 0% | 304 | 1,372 | +351% | 0 | 0 | — |
case-04 | fail→pass | 9,491 | 2,336 | -75% | 1 | 1 | 0% | 1,740 | 1,591 | -9% | 0 | 0 | — |
case-05 | fail→pass | 3,920 | 2,118 | -46% | 1 | 1 | 0% | 691 | 1,480 | +114% | 0 | 0 | — |
case-06 | fail→pass | 6,415 | 2,073 | -68% | 1 | 1 | 0% | 979 | 1,551 | +58% | 0 | 0 | — |
case-07 | fail→pass | 16,025 | 1,356 | -92% | 1 | 1 | 0% | 3,103 | 1,352 | -56% | 0 | 0 | — |
case-08 | fail→pass | 18,275 | 1,346 | -93% | 1 | 1 | 0% | 3,328 | 1,390 | -58% | 0 | 0 | — |
case-09 | fail→pass | 6,894 | 1,531 | -78% | 1 | 1 | 0% | 1,225 | 1,393 | +14% | 0 | 0 | — |
case-10 | fail→pass | 13,044 | 1,671 | -87% | 1 | 1 | 0% | 2,081 | 1,450 | -30% | 0 | 0 | — |
case-11 | fail→pass | 8,210 | 2,499 | -70% | 1 | 1 | 0% | 1,491 | 1,402 | -6% | 0 | 0 | — |
case-12 | fail→pass | 6,128 | 1,922 | -69% | 1 | 1 | 0% | 1,102 | 1,475 | +34% | 0 | 0 | — |
case-13 | fail→pass | 17,708 | 1,422 | -92% | 1 | 1 | 0% | 3,234 | 1,423 | -56% | 0 | 0 | — |
case-14 | fail→pass | 8,737 | 1,827 | -79% | 1 | 1 | 0% | 1,528 | 1,478 | -3% | 0 | 0 | — |
case-15 | fail→pass | 6,632 | 1,466 | -78% | 1 | 1 | 0% | 1,137 | 1,417 | +25% | 0 | 0 | — |
case-16 | fail→pass | 8,737 | 1,512 | -83% | 1 | 1 | 0% | 1,660 | 1,407 | -15% | 0 | 0 | — |
case-17 | fail→pass | 6,497 | 1,468 | -77% | 1 | 1 | 0% | 1,174 | 1,342 | +14% | 0 | 0 | — |
case-18 | fail→pass | 9,201 | 2,161 | -77% | 1 | 1 | 0% | 1,712 | 1,480 | -14% | 0 | 0 | — |
case-19 | fail→pass | 10,781 | 2,235 | -79% | 1 | 1 | 0% | 1,742 | 1,530 | -12% | 0 | 0 | — |
case-20 | pass→fail | 6,049 | 3,703 | -39% | 1 | 1 | 0% | 1,113 | 1,329 | +19% | 0 | 0 | — |
case-21 | pass→pass | 8,830 | 6,639 | -25% | 1 | 1 | 0% | 1,570 | 1,742 | +11% | 0 | 0 | — |
case-22 | pass→fail | 9,976 | 4,059 | -59% | 1 | 1 | 0% | 1,723 | 1,399 | -19% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 17 counted toward the lift figure. The other 5 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +64 percentage points is the difference between those two pass rates over the 17 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 7/31/2026 | — |
Other measured skills in the registry, with their headline benchmark lift.