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Get Started Free →Get a token-efficient overview of any project using file tree, code structure, and call graph analysis.
.claude/skills/tldr-overview/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 206% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -12% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -43% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -50% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -39% | 0% |
Get a token-efficient overview of any project using the TLDR stack.
/overview or /tldr-overviewbashtldr tree . --ext .py # or .ts, .go, .rs
bashtldr structure src/ --lang python --max 50
Returns: functions, classes, imports per file
bashtldr calls src/
Returns: cross-file relationships, main entry points
For each entry point found:
bashtldr cfg src/main.py main # Get complexity
## Project Overview: {project_name}
### Structure
{tree output - files and directories}
### Key Components
{structure output - functions, classes per file}
### Architecture (Call Graph)
{calls output - how components connect}
### Complexity Hot Spots
{cfg output - functions with high cyclomatic complexity}
---
Token cost: ~{N} tokens (vs ~{M} raw = {savings}% savings)pythonfrom tldr.api import get_file_tree, get_code_structure, build_project_call_graph # 1. Tree tree = get_file_tree("src/", extensions={".py"}) # 2. Structure structure = get_code_structure("src/", language="python", max_results=50) # 3. Call graph calls = build_project_call_graph("src/", language="python") # 4. Complexity for hot functions for edge in calls.edges[:10]: cfg = get_cfg_context("src/" + edge[0], edge[1])
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 20,139 | 7,396 | -63% | 1 | 1 | 0% | 3,321 | 1,435 | -57% | 0 | 0 | — |
case-01 | fail→fail | 4,418 | 6,752 | +53% | 1 | 1 | 0% | 325 | 1,889 | +481% | 0 | 0 | — |
case-03 | fail→pass | 4,704 | 3,532 | -25% | 1 | 1 | 0% | 355 | 1,088 | +206% | 0 | 0 | — |
case-04 | fail→pass | 5,811 | 2,270 | -61% | 1 | 1 | 0% | 974 | 861 | -12% | 0 | 0 | — |
case-05 | fail→pass | 6,540 | 1,924 | -71% | 1 | 1 | 0% | 1,278 | 726 | -43% | 0 | 0 | — |
case-06 | pass→pass | 8,050 | 2,761 | -66% | 1 | 1 | 0% | 1,109 | 745 | -33% | 0 | 0 | — |
case-07 | fail→pass | 9,482 | 2,420 | -74% | 1 | 1 | 0% | 1,583 | 795 | -50% | 0 | 0 | — |
case-08 | fail→pass | 6,865 | 1,852 | -73% | 1 | 1 | 0% | 1,188 | 723 | -39% | 0 | 0 | — |
case-09 | fail→pass | 10,816 | 2,318 | -79% | 1 | 1 | 0% | 2,275 | 881 | -61% | 0 | 0 | — |
case-10 | fail→pass | 9,289 | 3,973 | -57% | 1 | 1 | 0% | 2,014 | 949 | -53% | 0 | 0 | — |
case-11 | fail→pass | 12,992 | 4,092 | -69% | 1 | 1 | 0% | 2,102 | 1,299 | -38% | 0 | 0 | — |
case-12 | fail→fail | 12,967 | 5,560 | -57% | 1 | 1 | 0% | 2,532 | 1,692 | -33% | 0 | 0 | — |
case-13 | fail→pass | 5,263 | 1,834 | -65% | 1 | 1 | 0% | 927 | 826 | -11% | 0 | 0 | — |
case-14 | fail→fail | 8,111 | 2,656 | -67% | 1 | 1 | 0% | 1,562 | 898 | -43% | 0 | 0 | — |
case-15 | fail→pass | 6,907 | 1,742 | -75% | 1 | 1 | 0% | 1,211 | 743 | -39% | 0 | 0 | — |
case-16 | fail→pass | 6,343 | 1,383 | -78% | 1 | 1 | 0% | 1,154 | 697 | -40% | 0 | 0 | — |
case-17 | fail→pass | 15,136 | 8,252 | -45% | 1 | 1 | 0% | 2,479 | 2,227 | -10% | 0 | 0 | — |
case-18 | fail→pass | 12,366 | 3,071 | -75% | 1 | 1 | 0% | 1,893 | 1,043 | -45% | 0 | 0 | — |
case-19 | fail→pass | 8,550 | 1,419 | -83% | 1 | 1 | 0% | 1,519 | 708 | -53% | 0 | 0 | — |
case-20 | pass→pass | 8,078 | 4,288 | -47% | 1 | 1 | 0% | 1,329 | 1,079 | -19% | 0 | 0 | — |
case-21 | pass→pass | 7,146 | 3,132 | -56% | 1 | 1 | 0% | 1,174 | 1,055 | -10% | 0 | 0 | — |
case-22 | fail→pass | 10,870 | 2,641 | -76% | 1 | 1 | 0% | 1,631 | 976 | -40% | 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. The headline lift of +68 percentage points is the difference between those two pass rates over the 22 comparable cases.
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 | 7/29/2026 | +59% |
Other measured skills in the registry, with their headline benchmark lift.