geofeed-tuner

github/awesome-copilot · updated Apr 8, 2026

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$npx skills add https://github.com/github/awesome-copilot --skill geofeed-tuner
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summary

This skill helps you create and improve IP geolocation feeds in CSV format by:

skill.md

Geofeed Tuner – Create Better IP Geolocation Feeds

This skill helps you create and improve IP geolocation feeds in CSV format by:

  • Ensuring your CSV is well-formed and consistent
  • Checking alignment with RFC 8805 (the industry standard)
  • Applying opinionated best practices learned from real-world deployments
  • Suggesting improvements for accuracy, completeness, and privacy

When to Use This Skill

  • Use this skill when a user asks for help creating, improving, or publishing an IP geolocation feed file in CSV format.
  • Use it to tune and troubleshoot CSV geolocation feeds — catching errors, suggesting improvements, and ensuring real-world usability beyond RFC compliance.
  • Intended audience:
    • Network operators, administrators, and engineers responsible for publicly routable IP address space
    • Organizations such as ISPs, mobile carriers, cloud providers, hosting and colocation companies, Internet Exchange operators, and satellite internet providers
  • Do not use this skill for private or internal IP address management; it applies only to publicly routable IP addresses.

Prerequisites

  • Python 3 is required.

Directory Structure and File Management

This skill uses a clear separation between distribution files (read-only) and working files (generated at runtime).

Read-Only Directories (Do Not Modify)

The following directories contain static distribution assets. Do not create, modify, or delete files in these directories:

Directory Purpose
assets/ Static data files (ISO codes, examples)
references/ RFC specifications and code snippets for reference
scripts/ Executable code and HTML template files for reports

Working Directories (Generated Content)

All generated, temporary, and output files go in these directories:

Directory Purpose
run/ Working directory for all agent-generated content
run/data/ Downloaded CSV files from remote URLs
run/report/ Generated HTML tuning reports

File Management Rules

  1. Never write to assets/, references/, or scripts/ — these are part of the skill distribution and must remain unchanged.
  2. All downloaded input files (from remote URLs) must be saved to ./run/data/.
  3. All generated HTML reports must be saved to ./run/report/.
  4. All generated Python scripts must be saved to ./run/.
  5. The run/ directory may be cleared between sessions; do not store permanent data there.
  6. Working directory for execution: All generated scripts in ./run/ must be executed with the skill root directory (the directory containing SKILL.md) as the current working directory, so that relative paths like assets/iso3166-1.json and ./run/data/report-data.json resolve correctly. Do not cd into ./run/ before running scripts.

Processing Pipeline: Sequential Phase Execution

All phases must be executed in order, from Phase 1 through Phase 6. Each phase depends on the successful completion of the previous phase. For example, structure checks must complete before quality analysis can run.

The phases are summarized below. The agent must follow the detailed steps outlined further in each phase section.

Phase Name Description
1 Understand the Standard Review the key requirements of RFC 8805 for self-published IP geolocation feeds
2 Gather Input Collect IP subnet data from local files or remote URLs
3 Checks & Suggestions Validate CSV structure, analyze IP prefixes, and check data quality
4 Tuning Data Lookup Use Fastah's MCP tool to retrieve tuning data for improving geolocation accuracy
5 Generate Tuning Report Create an HTML report summarizing the analysis and suggestions
6 Final Review Verify consistency and completeness of the report data

Do not skip phases. Each phase provides critical checks or data transformations required by subsequent stages.

Execution Plan Rules

Before executing each phase, the agent MUST generate a visible TODO checklist.

The plan MUST:

  • Appear at the very start of the phase
  • List every step in order
  • Use a checkbox format
  • Be updated live as steps complete

Phase 1: Understand the Standard

The key requirements from RFC 8805 that this skill enforces are summarized below. Use this summary as your working reference. Only consult the full RFC 8805 text for edge cases, ambiguous situations, or when the user asks a standards question not covered here.

RFC 8805 Key Facts

Purpose: A self-published IP geolocation feed lets network operators publish authoritative location data for their IP address space in a simple CSV format, allowing geolocation providers to incorporate operator-supplied corrections.

CSV Column Order (Sections 2.1.1.1–2.1.1.5):

Column Field Required Notes
1 ip_prefix Yes CIDR notation; IPv4 or IPv6; must be a network address
2 alpha2code No ISO 3166-1 alpha-2 country code; empty or "ZZ" = do-not-geolocate
3 region No ISO 3166-2 subdivision code (e.g., US-CA)
4 city No Free-text city name; no authoritative validation set
5 postal_code No Deprecated — must be left empty or absent

Structural rules:

  • Files may contain comment lines beginning with # (including the header, if present).
  • A header row is optional; if present, it is treated as a comment if it starts with #.
  • Files must be encoded in UTF-8.
  • Subnet host bits must not be set (i.e., 192.168.1.1/24 is invalid; use 192.168.1.0/24).
  • Applies only to globally routable unicast addresses — not private, loopback, link-local, or multicast space.

Do-not-geolocate: An entry with an empty alpha2code or case-insensitive ZZ (irrespective of values of region/city) is an explicit signal that the operator does not want geolocation applied to that prefix.

Postal codes deprecated (Section 2.1.1.5): The fifth column must not contain postal or ZIP codes. They are too fine-grained for IP-range mapping and raise privacy concerns.

Phase 2: Gather Input

  • If the user has not already provided a list of IP subnets or ranges (sometimes referred to as inetnum or inet6num), prompt them to supply it. Accepted input formats:

    • Text pasted into the chat
    • A local CSV file
    • A remote URL pointing to a CSV file
  • If the input is a remote URL:

    • Attempt to download the CSV file to ./run/data/ before processing.
    • On HTTP error (4xx, 5xx, timeout, or redirect loop), stop immediately and report to the user: Feed URL is not reachable: HTTP {status_code}. Please verify the URL is publicly accessible.
    • Do not proceed to Phase 3 with an incomplete or empty download.
  • If the input is a local file, process it directly without downloading.

  • Encoding detection and normalization:

    1. Attempt to read the file as UTF-8 first.
    2. If a UnicodeDecodeError is raised, try utf-8-sig (UTF-8 with BOM), then latin-1.
    3. Once successfully decoded, re-encode and write the working copy as UTF-8.
    4. If no encoding succeeds, stop and report: Unable to decode input file. Please save it as UTF-8 and try again.

Phase 3: Checks & Suggestions

Execution Rules

  • Generate a script for this phase.
  • Do NOT combine this phase with others.
  • Do NOT precompute future-phase data.
  • Store the output as a JSON file at: ./run/data/report-data.json

Schema Definition

The JSON structure below is IMMUTABLE during Phase 3. Phase 4 will later add a TunedEntry object to each object in Entries — this is the only permitted schema extension and happens in a separate phase.

JSON keys map directly to template placeholders like {{.CountryCode}}, {{.HasError}}, etc.

{
  "InputFile": "",
  "Timestamp": 0,

  "TotalEntries": 0,
  "IpV4Entries": 0,
  "IpV6Entries": 0,
  "InvalidEntries": 0,

  "Errors": 0,
  "Warnings": 0,
  "OK": 0,
  "Suggestions": 0,

  "CityLevelAccuracy": 0,
  "RegionLevelAccuracy": 0,
  "CountryLevelAccuracy": 0,
  "DoNotGeolocate": 0,

  "Entries": [
    {
      "Line": 0,
      "IPPrefix": "",
      "CountryCode": "",
      "RegionCode": "",
      "City": "",

      "Status": "",
      "IPVersion": "",

      "Messages": [
        {
          "ID": "",
          "Type": "",
          "Text": "",
          "Checked": false
        }
      ],

      "HasError": false,
      "HasWarning": false,
      "HasSuggestion": false,
      "DoNotGeolocate": false,
      "GeocodingHint": "",
      "Tunable": false
    }
  ]
}

Field definitions:

Top-level metadata:

  • InputFile: The original input source, either a local filename or a remote URL.
  • Timestamp: Milliseconds since Unix epoch when the tuning was performed.
  • TotalEntries: Total number of data rows processed (excluding comment and blank lines).
  • IpV4Entries: Count of entries that are IPv4 subnets.
  • IpV6Entries: Count of entries that are IPv6 subnets.
  • InvalidEntries: Count of entries that failed IP prefix parsing and CSV parsing.
  • Errors: Total entries whose Status is ERROR.
  • Warnings: Total entries whose Status is WARNING.
  • OK: Total entries whose Status is OK.
  • Suggestions: Total entries whose Status is SUGGESTION.
  • CityLevelAccuracy: Count of valid entries where City is non-empty.
  • RegionLevelAccuracy: Count of valid entries where RegionCode is non-empty and City is empty.
  • CountryLevelAccuracy: Count of valid entries where CountryCode is non-empty, RegionCode is empty, and City is empty.
  • DoNotGeolocate (metadata): Count of valid entries where CountryCode, RegionCode, and City are all empty.

Entry fields:

  • Entries: Array of objects, one per data row, with the following per-entry fields:
    • Line: 1-based line number in the original CSV (counting all lines including comments and blanks).
    • IPPrefix: The normalized IP prefix in CIDR slash notation.
    • CountryCode: The ISO 3166-1 alpha-2 country code, or empty string.
    • RegionCode: The ISO 3166-2 region code (e.g., US-CA), or empty string.
    • City: The city name, or empty string.
    • Status: Highest severity assigned: ERROR > WARNING > SUGGESTION > OK.
    • IPVersion: "IPv4" or "IPv6" based on the parsed IP prefix.
    • Messages: Array of message objects, each with:
      • ID: String identifier from the Validation Rules Reference table below (e.g., "1101", "3301").
      • Type: The severity type: "ERROR", "WARNING", or "SUGGESTION".
      • Text: The human-readable validation message string.
      • Checked: true if the validation rule is auto-tunable (Tunable: true in the reference table), false otherwise. Controls whether the checkbox in the report is checked or disabled.
    • HasError: true if any message has Type "ERROR".
    • HasWarning: true if any message has Type "WARNING".
    • HasSuggestion: true if any message has Type "SUGGESTION".
    • DoNotGeolocate (entry): true if CountryCode is empty or "ZZ" — the entry is an explicit do-not-geolocate signal.
    • GeocodingHint: Always empty string "" in Phase 3. Reserved for future use.
    • Tunable: true if any message in the entry has Checked: true. Computed as logical OR across all messages' Checked values. This flag drives the "Tune" button visibility in the report.

Validation Rules Reference

When adding messages to an entry, use the ID, Type, Text, and Checked values from this table.

ID Type Text Checked Condition Reference
1101 ERROR IP prefix is empty false IP Prefix Analysis: empty
1102 ERROR Invalid IP prefix: unable to parse as IPv4 or IPv6 network false IP Prefix Analysis: invalid syntax
1103 ERROR Non-public IP range is not allowed in an RFC 8805 feed false IP Prefix Analysis: non-public
3101 SUGGESTION IPv4 prefix is unusually large and may indicate a typo false IP Prefix Analysis: IPv4 < /22
3102 SUGGESTION IPv6 prefix is unusually large and may indicate a typo false IP Prefix Analysis: IPv6 < /64
1201 ERROR Invalid country code: not a valid ISO 3166-1 alpha-2 value true Country Code Analysis: invalid
1301 ERROR Invalid region format; expected COUNTRY-SUBDIVISION (e.g., US-CA) true Region Code Analysis: bad format
1302 ERROR Invalid region code: not a valid ISO 3166-2 subdivision true Region Code Analysis: unknown code
1303 ER
how to use geofeed-tuner

How to use geofeed-tuner on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add geofeed-tuner
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/github/awesome-copilot --skill geofeed-tuner

The skills CLI fetches geofeed-tuner from GitHub repository github/awesome-copilot and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/geofeed-tuner

Reload or restart Cursor to activate geofeed-tuner. Access the skill through slash commands (e.g., /geofeed-tuner) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.573 reviews
  • Shikha Mishra· Dec 16, 2024

    Registry listing for geofeed-tuner matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Camila Farah· Dec 16, 2024

    Keeps context tight: geofeed-tuner is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Charlotte Huang· Dec 12, 2024

    I recommend geofeed-tuner for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Rahul Santra· Nov 7, 2024

    Keeps context tight: geofeed-tuner is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Luis Martin· Nov 7, 2024

    Registry listing for geofeed-tuner matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ava Anderson· Nov 3, 2024

    Useful defaults in geofeed-tuner — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Pratham Ware· Oct 26, 2024

    I recommend geofeed-tuner for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Luis Dixit· Oct 26, 2024

    Useful defaults in geofeed-tuner — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Luis Chawla· Oct 26, 2024

    geofeed-tuner fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Daniel Rahman· Oct 22, 2024

    Registry listing for geofeed-tuner matched our evaluation — installs cleanly and behaves as described in the markdown.

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