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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:

Working Directories (Generated Content)

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

File Management Rules

1. Never write toassets/, 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 ) as the current working directory, so that relative paths like and resolve correctly. Do not into 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.

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):

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 with BOM), then .

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 is .

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 > > > .

Validation Rules Reference

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

Populating Messages

When a validation check matches, add a message to the entry's Messages array using the values from the reference table:

entry["Messages"].append({
    "ID": "1201",      # From the table
    "Type": "ERROR",   # From the table
    "Text": "Invalid country code: not a valid ISO 3166-1 alpha-2 value",  # From the table
    "Checked": True    # From the table (True = tunable)
})

After populating all messages for an entry, derive the entry-level flags:

entry["HasError"] = any(m["Type"] == "ERROR" for m in entry["Messages"])
entry["HasWarning"] = any(m["Type"] == "WARNING" for m in entry["Messages"])
entry["HasSuggestion"] = any(m["Type"] == "SUGGESTION" for m in entry["Messages"])
entry["Tunable"] = any(m["Checked"] for m in entry["Messages"])

Accuracy Level Counting Rules

Accuracy levels are mutually exclusive. Assign each valid (non-ERROR, non-invalid) entry to exactly one bucket based on the most granular non-empty geo field:

Do not count entries with HasError: true or entries in InvalidEntries in any accuracy bucket.

The agent MUST NOT:

• Rename fields
• Add or remove fields
• Change data types
• Reorder keys
• Alter nesting
• Wrap the object
• Split into multiple files

If a value is unknown, leave it empty — never invent data.

Structure & Format Check

This phase verifies that your feed is well-formed and parseable. Critical structural errors must be resolved before the tuner can analyze geolocation quality.

CSV Structure

This subsection defines rules for CSV-formatted input files used for IP geolocation feeds. The goal is to ensure the file can be parsed reliably and normalized into a consistent internal representation.

• CSV Structure Checks

  • If pandas is available, use it for CSV parsing.

  • Otherwise, fall back to Python's built-in csv module.

  • Ensure the CSV contains exactly 4 or 5 logical columns.

  • Comment lines are allowed.

  • A header row may or may not be present.

  • If no header row exists, assume the implicit column order:

    ip_prefix, alpha2code, region, city, postal code (deprecated)
    

  • Refer to the example input file: assets/example/01-user-input-rfc8805-feed.csv

• CSV Cleansing and Normalization

IP Prefix Analysis

• Check that the IPPrefix field is present and non-empty for each entry.

• Check for duplicate IPPrefix values across entries.

• If duplicates are found, stop the skill and report to the user with the message: Duplicate IP prefix detected: {ip_prefix_value} appears on lines {line_numbers}

• If no duplicates are found, continue with the analysis.

• Checks

  • Each subnet must parse cleanly as either an IPv4 or IPv6 network using the code snippets in the references/ folder.
  • Subnets must be normalized and displayed in CIDR slash notation.
    • Single-host IPv4 subnets must be represented as /32.
    • Single-host IPv6 subnets must be represented as /128.

Geolocation Quality Check

Analyze the accuracy and consistency of geolocation data:

• Country codes
• Region codes
• City names
• Deprecated fields

This phase runs after structural checks pass.

Country Code Analysis

• Use the locally available data table ISO3166-1 for checking.

  • JSON array of countries and territories with ISO codes
  • Each object includes:
    • alpha_2: two-letter country code
    • name: short country name
    • flag: flag emoji
  • This file represents the superset of validCountryCode values for an RFC 8805 CSV.

• Check the entry's CountryCode (RFC 8805 Section 2.1.1.2, column alpha2code) against the alpha_2 attribute.

Region Code Analysis

• Use the locally available data table ISO3166-2 for checking.

  • JSON array of country subdivisions with ISO-assigned codes
  • Each object includes:
    • code: subdivision code prefixed with country code (e.g., US-CA)
    • name: short subdivision name
  • This file represents the superset of validRegionCode values for an RFC 8805 CSV.

• If a RegionCode value is provided (RFC 8805 Section 2.1.1.3):

  • Check that the format matches {COUNTRY}-{SUBDIVISION} (e.g., , ).

City Name Analysis

• City names are validated using heuristic checks only.

• There is currently no authoritative dataset available for validating city names.

• ERROR

  • Report the following conditions as ERROR :

  • Placeholder or non-meaningful values

    • Condition: Placeholder or non-meaningful values including but not limited to:
      • undefined
      • Please select
      • null
      • N/A
      • TBD
      • unknown

Postal Code Check

• RFC 8805 Section 2.1.1.5 explicitly deprecates postal or ZIP codes.

• Postal codes can represent very small populations and are not considered privacy-safe for mapping IP address ranges, which are statistical in nature.

• ERROR

  • Report the following conditions as ERROR :
  • Postal code present
    • Condition: A non-empty value is present in the postal/ZIP code field.
    • Message ID: 1501

Tuning & Recommendations

This phase applies opinionated recommendations beyond RFC 8805, learned from real-world geofeed deployments, that improve accuracy and usability.

• SUGGESTION

  • Report the following conditions as SUGGESTION :

  • Region or city specified for small territory

    • Condition:
      • Entry is a small territory
      • RegionCode is non-empty OR
      • City is non-empty.
    • Message IDs: 3301 (for region), 3402 (for city)

  • Missing region code when city is specified

    • Condition:
      • City is non-empty
      • RegionCode is empty

Phase 4: Tuning Data Lookup

Objective

Lookup all the Entries using Fastah's rfc8805-row-place-search tool.

Execution Rules

• Generate a new script only for payload generation (read the dataset and write one or more payload JSON files; do not call MCP from this script).
• Server only accepts 1000 entries per request, so if there are more than 1000 entries, split into multiple requests.
• The agent must read the generated payload files, construct the requests from them, and send those requests to the MCP server in batches of at most 1000 entries each.
• On MCP failure: If the MCP server is unreachable, returns an error, or returns no results for any batch, log a warning and continue to Phase 5. Set TunedEntry: {} for all affected entries. Do not block report generation. Notify the user clearly: Tuning data lookup unavailable; the report will show validation results only.
• Suggestions are advisory only — never auto-populate them.

Step 1: Build Lookup Payload with Deduplication

Load the dataset from: ./run/data/report-data.json

• Read the Entries array. Each entry will be used to build the MCP lookup payload.

Reduce server requests by deduplicating identical entries:

• For each entry in Entries, compute a content hash (hash of CountryCode + RegionCode + City).
• Create a deduplication map: { contentHash -> { rowKey, payload, entryIndices: [] } }. rowKey is a UUID that will be sent to the MCP server for matching responses.
• If an entry's hash already exists, append its 0-based array index in Entries to that deduplication entry's entryIndices array.
• If hash is new, generate a UUID (rowKey) and create a new deduplication entry.

Build request batches:

• Extract unique deduplicated entries from the map, keeping them in deduplication order.

• Build request batches of up to 1000 items each.

• For each batch, keep an in-memory structure like [{ rowKey, payload, entryIndices }, ...] to match responses back by rowKey.

• When writing the MCP payload file, include the rowKey field with each payload object:

  [ {"rowKey": "550e8400-e29b-41d4-a716-446655440000", "countryCode":"CA","regionCode":"CA-ON","cityName":"Toronto"}, {"rowKey": "6ba7b810-9dad-11d1-80b4-00c04fd430c8", "countryCode":"IN","regionCode":"IN-KA","cityName":"Bangalore"}, {"rowKey": "6ba7b811-9dad-11d1-80b4-00c04fd430c8", "countryCode":"IN","regionCode":"IN-KA"} ]

• When reading responses, match each response rowKey field to the corresponding deduplication entry to retrieve all associated entryIndices.

Rules:

• Write payload to: ./run/data/mcp-server-payload.json
• Exit the script after writing the payload.

Step 2: Invoke Fastah MCP Tool

• An example mcp.json style configuration of Fastah MCP server is as follows:

  "fastah-ip-geofeed": {
    "type": "http",
    "url": "https://mcp.fastah.ai/mcp"
  }
  

• Server: https://mcp.fastah.ai/mcp

• Tool and its Schema: before the first tools/call, the agent MUST send a tools/list request to read the input and output schema for rfc8805-row-place-search. Use the discovered schema as the authoritative source for field names, types, and constraints.

• The following is an illustrative example only; always defer to the schema returned by tools/list:

  [
      {"rowKey": "550e8400-...", "countryCode":"CA", ...},
      {"rowKey": "690e9301-...", "countryCode":"ZZ", ...}
  ]
  

Step 3: Attach Tuned Data to Entries

• Generate a new script for attaching tuned data.
• Load both ./run/data/report-data.json and the deduplication map (held in memory from Step 1, or re-derived from the payload file).
• For each response from the MCP server:
  • Extract the rowKey from the response.
  • Look up the entryIndices array associated with that rowKey from the deduplication map.
  • For each index in entryIndices, attach the best match to Entries[index].
• Use the first (best) match from the response when available.

Create the field on each affected entry if it does not exist. Remap the MCP API response keys to Go struct field names:

"TunedEntry": {
  "Name": "",
  "CountryCode": "",
  "RegionCode": "",
  "PlaceType": "",
  "H3Cells": [],
  "BoundingBox": []
}

The TunedEntry field is a single object (not an array). It holds the best match from the MCP server.

MCP response key → JSON key mapping :

Entries with no UUID match (i.e. the MCP server returned no response for their UUID) must receive an empty TunedEntry: {} object — never leave the field absent.

• Write the dataset back to: ./run/data/report-data.json
• Rules:
  • Maintain all existing validation flags.
  • Do NOT create additional intermediate files.

Phase 5: Generate Tuning Report

Generate a self-contained HTML report by rendering the template at ./scripts/templates/index.html with data from ./run/data/report-data.json and ./run/data/comments.json.

Write the completed report to ./run/report/geofeed-report.html. After generating, attempt to open it in the system's default browser (e.g., webbrowser.open()). If running in a headless environment, CI pipeline, or remote container where no browser is available, skip the browser step and instead present the file path to the user so they can open or download it.

The template uses Gohtml/template syntax ({{.Field}}, {{range}}, {{if eq}}, etc.). Write a Python script that reads the template, builds a rendering context from the JSON data files, and processes the template placeholders to produce final HTML. Do not modify the template file itself — all processing happens in the Python script at render time.

Step 1: Replace Metadata Placeholders

Replace each {{.Metadata.X}} placeholder in the template with the corresponding value from report-data.json. Since JSON keys match the template placeholder, the mapping is direct — {{.Metadata.InputFile}} maps to the InputFile JSON key, etc.

Note on{{.Metadata.Timestamp}}: This placeholder appears inside a JavaScript new Date(...) call. Replace it with the raw integer value (no HTML escaping needed for a numeric literal inside <script>). All other metadata values should be HTML-escaped since they appear inside HTML element text.

Step 2: Replace the Comment Map Placeholder

Locate this pattern in the template:

const commentMap = {{.Comments}};

Replace {{.Comments}} with the serialized JSON object from ./run/data/comments.json. The JSON is embedded directly as a JavaScript object literal (not inside a string), so no extra escaping is needed:

comments_json = json.dumps(comments)
template = template.replace("{{.Comments}}", comments_json)

Step 3: Expand the Entries Range Block

The template contains a {{range .Entries}}...{{end}} block inside <tbody id="entriesTableBody">. Process it as follows:

1. Extract the range block body using regex. Critical: The block contains nested {{end}} tags (from {{if eq .Status ...}}, {{if .Checked}}, and {{range .Messages}}). A naive non-greedy match like \{\{range \.Entries\}\}(.*?)\{\{end\}\} will match the first inner {{end}}, truncating the block. Instead, anchor the outer {{end}} to the </tbody> that follows it:

   m = re.search(
       r'\{\{range \.Entries\}\}(.*?)\{\{end\}\}\s*</tbody>',
       template,
       re.DOTALL,
   )
   entry_body = m.group(1)  # template text for one entry iteration
   

This ensures you capture the full block body including all three <tr> rows and the nested {{range .Messages}}...{{end}}. 2. Iterate over each entry in report-data.json's Entries array. 3. Expand the block body for each entry using the processing order below. 4. Replace the entire match (from {{range .Entries}} through </tbody>) with the concatenated expanded HTML followed by </tbody>.

Processing order for each entry (innermost constructs first to avoid {{end}} confusion):

1. Evaluate {{if eq .Status ...}}...{{end}} conditionals (status badge class and icon).
2. Evaluate {{if .Checked}}...{{end}} conditional (message checkbox).
3. Expand {{range .Messages}}...{{end}} inner range.
4. Replace simple {{.Field}} placeholders.

Entry Field Mapping

Within the range block body, replace these placeholders for each entry. Since JSON keys match the template placeholder, the template placeholder {{.X}} maps directly to JSON key X:

data-h3-cells and data-bounding-box format: These are NOT JSON arrays. They are bracket-wrapped, space-separated values. Do not use JSON serialization (no quotes around string elements, no commas between numbers). Examples:

• [836752fffffffff 836755fffffffff] — correct
• ["836752fffffffff","836755fffffffff"] — WRONG , quotes will break parsing
• [-71.70 10.73 -71.52 10.55] — correct
• [] — correct for empty

Evaluating Status Conditionals

Process these BEFORE replacing simple{{.Field}} placeholders — otherwise the {{end}} markers get consumed and the regex won't match.

The template uses {{if eq .Status "..."}} conditionals for the status badge CSS class and icon. Evaluate these by checking the entry's status value and keeping only the matching branch text.

The status badge line contains two {{if eq .Status ...}}...{{end}} blocks on a single line — one for the CSS class, one for the icon. Use re.sub with a callback to resolve all occurrences:

STATUS_CSS = {"ERROR": "error", "WARNING": "warning", "SUGGESTION": "suggestion", "OK": "ok"}
STATUS_ICON = {
    "ERROR": "bi-x-circle-fill",
    "WARNING": "bi-exclamation-triangle-fill",
    "SUGGESTION": "bi-lightbulb-fill",
    "OK": "bi-check-circle-fill",
}

def resolve_status_if(match_obj, status):
    """Pick the branch matching `status` from a {{if eq .Status ...}}...{{end}} block."""
    block = match_obj.group(0)
    # Try each branch: {{if eq .Status "X"}}val{{else if ...}}val{{else}}val{{end}}
    for st, val in [("ERROR",), ("WARNING",), ("SUGGESTION",)]:
        # not needed to parse generically — just map from the known patterns
    ...

A simpler approach: since there are exactly two known patterns, replace them as literal strings:

css_class = STATUS_CSS.get(status, "ok")
icon_class = STATUS_ICON.get(status, "bi-check-circle-fill")
body = body.replace(
    '{{if eq .Status "ERROR"}}error{{else if eq .Status "WARNING"}}warning{{else if eq .Status "SUGGESTION"}}suggestion{{else}}ok{{end}}',
    css_class,
)
body = body.replace(
    '{{if eq .Status "ERROR"}}bi-x-circle-fill{{else if eq .Status "WARNING"}}bi-exclamation-triangle-fill{{else if eq .Status "SUGGESTION"}}bi-lightbulb-fill{{else}}bi-check-circle-fill{{end}}',
    icon_class,
)

This avoids regex entirely and is safe because these exact strings appear verbatim in the template.

Step 4: Expand the Nested Messages Range

The {{range .Messages}}...{{end}} block contains a nested {{if .Checked}} checked{{else}} disabled{{end}} conditional, so its inner {{end}} would cause a simple non-greedy regex to match too early. Anchor the regex to </td> (the tag immediately after the messages range closing {{end}}) to capture the full block body:

msg_match = re.search(
    r'\{\{range \.Messages\}\}(.*?)\{\{end\}\}\s*(?=</td>)',
    body, re.DOTALL
)

The lookahead (?=</td>) ensures the regex skips past the checkbox conditional's {{end}} (which is followed by >, not </td>) and matches only the range-closing {{end}} (which is followed by whitespace then </td>).

For each message in the entry's Messages array, clone the captured block body and expand it:

1. Resolve the checkbox conditional per message (must happen before simple placeholder replacement to remove the nested {{end}}):

   if msg.get("Checked"):
       msg_body = msg_body.replace(
           '{{if .Checked}} checked{{else}} disabled{{end}}', ' checked'
       )
   else:
       msg_body = msg_body.replace(
           '{{if .Checked}} checked{{else}} disabled{{end}}', ' disabled'
       )
   

2. Replace message field placeholders :

1. Concatenate all expanded message blocks and replace the original {{range .Messages}}...{{end}} match (msg_match.group(0)) with the result:

   body = body[:msg_match.start()] + "".join(expanded_msgs) + body[msg_match.end():]
   

If Messages is empty, replace the entire matched region with an empty string (no message divs — only the issues header remains).

Output Guarantees

• The report must be readable in any modern browser without extra network dependencies beyond the CDN links already in the template (leaflet, h3-js, bootstrap-icons, Raleway font).
• All values embedded in HTML must be HTML-escaped (<, >, &, ") to prevent rendering issues.
• commentMap is embedded as a direct JavaScript object literal (not inside a string), so no JS string escaping is needed — just emit valid JSON.
• All values must be derived only from analysis output , not recomputed heuristically.

Phase 6: Final Review

Perform a final verification pass using concrete, checkable assertions before presenting results to the user.

Check 1 — Entry count integrity

• Count non-comment, non-blank data rows in the original input CSV.
• Assert: len(entries) in report-data.json == data_row_count
• On failure: Row count mismatch: input has {N} data rows but report contains {M} entries.

Check 2 — Summary counter integrity

• These counters use mutual exclusion based on the boolean flags, which mirrors the highest-severity Status field. An entry with both HasError: true and HasWarning: true is counted only in Errors, never in Warnings. This is equivalent to counting by the entry's Status field.
• Assert all of the following; correct any that fail before generating the report:
  • Errors == sum(1 for e in Entries if e['HasError'])
  • Warnings == sum(1 for e in Entries if e['HasWarning'] and not e['HasError'])
  • Suggestions == sum(1 for e in Entries if e['HasSuggestion'] and not e['HasError'] and not e['HasWarning'])

Check 3 — Accuracy bucket integrity

• Assert: CityLevelAccuracy + RegionLevelAccuracy + CountryLevelAccuracy + DoNotGeolocate == TotalEntries - InvalidEntries
• Note: The accuracy buckets defined in Phase 3 say "Do not count entries with HasError: true", but the Check 3 formula above uses TotalEntries - InvalidEntries (which still includes ERROR entries). This means ERROR entries (those that parsed as valid IPs but failed validation) are counted in accuracy buckets by their geo-field presence. Only InvalidEntries (unparsable IP prefixes) are excluded. Follow the Check 3 formula as the authoritative rule.
• On failure, trace and fix the bucketing logic before proceeding.

Check 4 — No duplicate line numbers

• Assert: all Line values in Entries are unique.
• On failure, report the duplicated line numbers to the user.

Check 5 — TunedEntry completeness

• Assert: every object in Entries has a TunedEntry key (even if its value is {}).
• On failure, add "TunedEntry": {} to any entry missing the key, then re-save report-data.json.

Check 6 — Report file is present and non-empty

• Confirm ./run/report/geofeed-report.html was written and has a file size greater than zero bytes.
• On failure, regenerate the report before presenting to the user.

Weekly Installs

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Repository

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