RRepoGEO

REPOGEO REPORT · LITE

m0n0x41d/haft

Default branch main · commit 5abf6fe7 · scanned 6/23/2026, 8:21:51 AM

GitHub: 1,350 stars · 102 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)

2 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 m0n0x41d/haft, 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 README opening to explicitly state core purpose and what it is not

    Why:

    CURRENT
    *formerly quint-codeFPF governance substrate for AI-assisted software delivery.** Your agents (Claude Code, Codex) write code fast. Most repositories are not ready for serious harness engineering: the target system is underspecified, the enabling system is implicit, term maps are missing, and runtime evidence is detached from the spec. Haft makes the project harnessable before it scales execution.
    COPY-PASTE FIX
    Haft is an **engineering decisions engine** and **governance substrate** for AI-assisted software delivery, not an HTTP proxy, static file server, or a coding agent. It helps you frame, compare, and decide with evidence decay and parity enforcement, making your repositories harnessable for agents like Claude Code, Cursor, and Codex.
  • mediumreadme#2
    Add a 'How Haft Differs' section to the README

    Why:

    COPY-PASTE FIX
    ## How Haft Differs from Related Tools

While tools like MLflow, OpenAI Evals, Great Expectations, Pydantic, and pytest are essential for MLOps, evaluation, data validation, and testing, Haft operates at a higher layer. Haft is a *governance substrate* and *engineering decisions engine* that ensures your AI-assisted projects are built on auditable artifacts and explicit decisions, with evidence decay and parity enforcement. It complements these tools by providing the framework for *why* and *how* decisions are made, rather than just *what* was run or *if* it passed a test.
  • lowreadme#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    ## License
Haft is distributed under a custom license. Please refer to the `LICENSE` file in the repository for full details on its terms and conditions.

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 m0n0x41d/haft
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MLflow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MLflow · recommended 1×
  2. openai/evals · recommended 1×
  3. great-expectations/great_expectations · recommended 1×
  4. pydantic/pydantic · recommended 1×
  5. pytest-dev/pytest · recommended 1×
  • CATEGORY QUERY
    How can I manage and audit engineering decisions in AI-driven software projects?
    you: not recommended
    AI recommended (in order):
    1. MLflow

    AI recommended 1 alternative but never named m0n0x41d/haft. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help enforce specifications and track evidence in AI-generated codebases?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Evals (openai/evals)
    2. Great Expectations (great-expectations/great_expectations)
    3. Pydantic (pydantic/pydantic)
    4. pytest (pytest-dev/pytest)
    5. DeepSource
    6. SonarQube
    7. MLflow (mlflow/mlflow)
    8. DVC (iterative/dvc)

    AI recommended 8 alternatives but never named m0n0x41d/haft. 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 m0n0x41d/haft?
    pass
    AI named m0n0x41d/haft explicitly

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

  • If a team adopts m0n0x41d/haft in production, what risks or prerequisites should they evaluate first?
    pass
    AI named m0n0x41d/haft 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 m0n0x41d/haft solve, and who is the primary audience?
    pass
    AI named m0n0x41d/haft explicitly

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

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m0n0x41d/haft — 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