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assafkipassafkip/huntkit/skills/telegram-harvest/SKILL.md

telegram-harvest

Skill74 starsChanged 19 days agoMITView on GitHub

Programmatic, no-LLM Telegram channel harvest: tgspyder scrape -> offline language detection + translation (Argos) -> rule-based field extraction -> XLSX with clue columns, plus listings.jsonl and a capture batch for EV evidence. Built for marketplace / account-sale / fraud-vendor channels. Use when: "harvest these telegram channels", "scrape telegram listings into a spreadsheet", "telegram marketplace sweep", "account procurement sheet". NOT for: private groups the case has no lawful access to, or identifying a person behind a channel (clues only; attribution needs T1/T2 crosslinks).

1 other project uses a version of this.

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  • Other versions1 project uses a version of this.

    Word for word · 1

    1. assafkip/investigators-copilot★ 74100% same

    Edited · 0

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    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository6 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Every row is T3 (automated extraction).

    line 65
  2. Very specific:

    If the workbook itself goes to a client, register it: ingest-client-document.sh <run-dir>/telegram-harvest.xlsx <slug> document …

    line 69

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

telegram-harvest

Port of the founder's assafkip/account-procurement-tools pipeline (see NOTICE.md). Deterministic: keyword dictionaries + regexes, no LLM, no external API at run time.

channels.txt -> tgspyder (scrape + invite links) -> lingua detect -> Argos translate (offline)
             -> extract.py rules (type, price, PIN, verified, qty, handles, links)
             -> geo_clues.py (clues only, never a country verdict)
             -> telegram-harvest.xlsx + listings.jsonl + urls.tsv

Setup (once per machine)

bash skills/telegram-harvest/setup.sh        # venv, deps, tgspyder, Argos models
# tgspyder is not vendored: TGSPYDER_SRC=<checkout> bash .../setup.sh if it is elsewhere
skills/telegram-harvest/.venv/bin/tgspyder @somechannel --chats   # one interactive login

Telegram login uses a real account session: founder decision per case. Sessions are secrets; never commit them.

Run

PY=skills/telegram-harvest/.venv/bin/python
H=skills/telegram-harvest/scripts/harvest.py

# live scrape into the active case (raw-collections/<ts>-telegram-harvest/)
$PY $H --channels <case>/investigation/evidence/telegram-channels.txt --case
# via SOCKS proxy
$PY $H --channels channels.txt --case --proxy socks5://127.0.0.1:9050
# offline: re-process an existing tgspyder messages CSV, no models needed
python3 $H --from-csv messages_x.csv --channel @x --case --no-translate
# a different rule profile (copy config/keywords.json and edit the terms)
$PY $H --channels channels.txt --case --keywords path/to/keywords.json

Bounded: --max-messages per channel (default 5000).

Output columns

The sheet keeps the 23 template columns in order (Date of Finding ... Screenshot 4), then helper columns prefixed »: Confidence (high/medium/low), Needs Review, Clue: Language, Clue: Country Words, Clue: Phone Country, Clue: TG Handles, Source Channel, Message ID, Message Date, Original Text, English Text. Vendor Type, Ecosystem (Geo) and Geo of Acc. are left blank and highlighted: a human makes the country call.

Evidence routing (required before citing)

  • Every row is T3 (automated extraction). The workbook is working material.
  • Capture each message you will cite as an EV item: bash skills/osint/scripts/capture-evidence.sh --batch <run-dir>/urls.tsv --case <case> (trim urls.tsv to the rows you need first; add --social per line for media).
  • If the workbook itself goes to a client, register it: ingest-client-document.sh <run-dir>/telegram-harvest.xlsx <slug> document --provided-by "telegram-harvest run <ts>".
  • Handles, phone prefixes and country words are clues. A shared handle across channels is not the same operator without a crosslink (.claude/rules/q-investigation.md).

Extending the rules

  • config/keywords.json: account types, flags, quantity/payment/transfer/spend/history/age terms, in any language. Short Latin terms match on word boundaries; longer or CJK terms match as substrings.
  • config/countries.json: country names, demonyms and local terms for the clue column.

Tests

python3 -m pytest skills/osint/tests/test_telegram_harvest.py -q (offline: extraction, clues, confidence, workbook columns, CSV mode).

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