RRepoGEO

REPOGEO REPORT · LITE

chopratejas/headroom

Default branch main · commit 90734b69 · scanned 6/24/2026, 11:36:15 PM

GitHub: 49,860 stars · 3,496 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 chopratejas/headroom, 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
    Add a clear disambiguation statement to the README

    Why:

    CURRENT
    The README starts with an ASCII logo and "The context compression layer for AI agents".
    COPY-PASTE FIX
    Add a clear, explicit statement at the very beginning of the README, right after the main title/logo, such as: "This is `headroom.ai`, a context compression layer for AI agents and LLMs, *not* the `headroom.js` UI library for web headers."
  • mediumabout#2
    Enhance the 'About' description to emphasize AI/LLM focus

    Why:

    CURRENT
    Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.
    COPY-PASTE FIX
    An AI/LLM context compression tool: Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.
  • lowreadme#3
    Explicitly state active development status in README

    Why:

    COPY-PASTE FIX
    Add a small section or line in the README, perhaps near the top or in a 'Status' section, like: "Status: Actively developed and maintained for modern AI/LLM applications."

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 chopratejas/headroom
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Anthropic Claude
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Anthropic Claude · recommended 1×
  2. OpenAI GPT-4 · recommended 1×
  3. Google Gemini 1.5 Pro · recommended 1×
  4. run-llama/llama_index · recommended 1×
  5. langchain-ai/langchain · recommended 1×
  • CATEGORY QUERY
    How can I reduce token usage and cost when sending large inputs to my LLM?
    you: not recommended
    AI recommended (in order):
    1. Anthropic Claude
    2. OpenAI GPT-4
    3. Google Gemini 1.5 Pro
    4. LlamaIndex (run-llama/llama_index)
    5. LangChain (langchain-ai/langchain)
    6. Hugging Face Transformers (huggingface/transformers)
    7. Pinecone
    8. Weaviate (weaviate/weaviate)
    9. Qdrant (qdrant/qdrant)
    10. OpenAI Fine-tuning API
    11. Hugging Face TRL (huggingface/trl)

    AI recommended 11 alternatives but never named chopratejas/headroom. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best methods to compress RAG context for more efficient LLM processing?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Anthropic's Claude 3
    4. Google's Gemini 1.5 Pro
    5. OpenAI's GPT-4 Turbo
    6. OpenAI's GPT-3.5 Turbo
    7. OpenAI's GPT-4

    AI recommended 7 alternatives but never named chopratejas/headroom. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 chopratejas/headroom?
    pass
    AI named chopratejas/headroom explicitly

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

  • If a team adopts chopratejas/headroom in production, what risks or prerequisites should they evaluate first?
    pass
    AI named chopratejas/headroom 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 chopratejas/headroom solve, and who is the primary audience?
    pass
    AI named chopratejas/headroom explicitly

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

Embed your GEO score

Drop this badge into the README of chopratejas/headroom. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/chopratejas/headroom.svg)](https://repogeo.com/en/r/chopratejas/headroom)
HTML
<a href="https://repogeo.com/en/r/chopratejas/headroom"><img src="https://repogeo.com/badge/chopratejas/headroom.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

chopratejas/headroom — 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