RRepoGEO

REPOGEO REPORT · LITE

wuba/WBBlades

Default branch master · commit 6b860be0 · scanned 6/24/2026, 6:36:56 PM

GitHub: 1,612 stars · 233 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface wuba/WBBlades, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening statement to clearly define WBBlades as a post-build analysis toolkit

    Why:

    CURRENT
    WBBlades is a tool set based on `Mach-O` file parsing, including one-click detection for app (supports `OC` and `Swift`), package size analysis (supports a single static library/dynamic library), Point-to-point crash analysis (based on system logs, supports Huawei and Bugly platform crash stack analysis, supports signed and unsigned states), Class automatic extraction and Hook capability based on Mach-O file.
    COPY-PASTE FIX
    WBBlades is a comprehensive **post-build analysis and optimization toolkit for iOS applications**, leveraging `Mach-O` file parsing to detect unused Objective-C/Swift code, analyze package size, and provide point-to-point crash resolution. It offers a powerful suite of tools for static analysis and app optimization.
  • hightopics#2
    Add more specific topics to improve category visibility

    Why:

    CURRENT
    classdump, crash-analysis, dsym, gpt, gpt-35-turbo, ios, mach-o, macho, symbolication
    COPY-PASTE FIX
    classdump, crash-analysis, dsym, gpt, gpt-35-turbo, ios, mach-o, macho, symbolication, unused-code-detection, app-size-optimization, static-analysis, code-optimization, ios-development-tools
  • mediumcomparison#3
    Add a dedicated section to the README clarifying WBBlades' unique differentiators

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, '## Why WBBlades? (Comparison with Alternatives)', to clearly articulate its unique value proposition and differentiate it from common alternatives in static analysis and iOS optimization.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface wuba/WBBlades
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AppCode
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AppCode · recommended 2×
  2. Xcode's Static Analyzer · recommended 1×
  3. Periphery · recommended 1×
  4. Faux Pas · recommended 1×
  5. `nm` · recommended 1×
  • CATEGORY QUERY
    How can I detect and remove unused Objective-C and Swift code from my iOS app?
    you: not recommended
    AI recommended (in order):
    1. Xcode's Static Analyzer
    2. Periphery
    3. Faux Pas
    4. AppCode
    5. `nm`
    6. `objdump`
    7. `find`
    8. `grep`

    AI recommended 8 alternatives but never named wuba/WBBlades. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help analyze Mach-O files for iOS app crash symbolication and size optimization?
    you: not recommended
    AI recommended (in order):
    1. Xcode
    2. atos
    3. nm
    4. size
    5. otool
    6. dwarfdump
    7. Archives Organizer
    8. Hopper Disassembler
    9. MachOView (gdbinit/MachOView)
    10. AppCode
    11. Firebase Crashlytics
    12. Sentry (getsentry/sentry)
    13. Instabug
    14. strip
    15. Binary Ninja

    AI recommended 15 alternatives but never named wuba/WBBlades. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of wuba/WBBlades?
    pass
    AI named wuba/WBBlades explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts wuba/WBBlades in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wuba/WBBlades explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo wuba/WBBlades solve, and who is the primary audience?
    pass
    AI named wuba/WBBlades explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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wuba/WBBlades — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite