LinkdTime
Build a clean timeline of any LinkedIn activity from a single URL or a whole list of links.
URL
https://github.com/Lucksi/LinkdTime no tagged releases; latest commit 2025‑04‑11 (as of January, 2026)
Description
LinkdTime is a small CLI Python tool that derives precise timestamps from LinkedIn activity URLs (posts, comments/replies, profile‑photo/background‑image/company‑logo changes) and can assemble them into a chronological timeline (HTML/TXT). For timestamping, it decodes the numeric ID embedded in LinkedIn URLs rather than scraping page HTML, so the time extraction itself works without network access; optional flags can save/download images for timelines. Outputs are a single timestamp for one URL or an HTML/TXT timeline for a list, with timezone, date, and clock format configurable. (see GitHub).
It accepts either:
• A single LinkedIn URL – e.g. https://www.linkedin.com/feed/update/urn:li:activity:... or a comment permalink – and prints the precise UTC time the action occurred;
• A text file of many URLs – one per line – and builds a chronological HTML or TXT timeline. Use --save to embed images as Base64 inside the HTML or --download to save originals alongside it.
Example use case
An analyst harvesting every post- and comment-URL authored by a suspected astroturf network feeds them to LinkdTime. The generated timeline shows that “independent” accounts replied within 90 seconds of each post – a pattern typical of centrally scripted campaigns. Astroturfing explained
Typical Workflow
The script returns either a single timestamp or a full HTML / TXT timeline (optional embedded images or Base64). Investigators can therefore spot coordination patterns, for example, discovering that replies labelled “organic” landed within minutes of each other. The tool prints either one ISO 8601 timestamp or writes timeline.html / timeline.txt. Investigators can visualise coordination, e.g. five “organic” replies landing < 3 min after a post may indicate astroturfing (see definition above).
Cost
Open-source under the GPL-3.0 licence; no paid tier (GitHub).
Level of difficulty
Requirements
Linux with Python 3 (officially tested; MacOS & Windows may work but are untested, Jul 2025).
Target URLs must be viewable without signing in. LinkdTime does not bypass LinkedIn log-in walls.
Limitations
Untested on Windows or macOS, manual tweaks may be necessary;
Breaks if LinkedIn changes its HTML structure (pure scraping);
Cannot access content behind the login wall or private-visibility posts;
Heavy, rapid queries may trigger LinkedIn's anti-bot defences; use rate-limiting or rotating proxies;
No graphical interface, terminal only.
LinkedIn deploys rate-limits, CAPTCHAs and UA/velocity-based WAF rules against automated scraping. Plan pauses or proxy rotation. See LinkedIn’s own note on anti-scraping defences.
Timestamp extraction depends on LinkedIn URL patterns and the numeric ID encoding scheme (not HTML scraping).
LinkdTime doesn’t log in; it can’t fetch private content. Image download features only work if the media URL is accessible and may trigger anti-bot controls if used at scale.
Ethical Considerations
Scraping LinkedIn may violate its terms of service; check your legal context before large-scale use.
LinkdTime extracts only information already publicly visible; nevertheless, assembling complete timelines can expose behavioural or work-pattern insights that the subject did not expect to be profiled.
Guides and articles
“A LinkedIn Activity Date Finder – LinkdTime” – creator’s announcement on LinkedIn (Mar 2025, LinkedIn).
Featured in OSINT Newsletter 67 – tool showcase with workflow tips (Apr 2025, X (formerly Twitter).
Anti-scraping policy – LinkedIn Safety Series.
Tool provider
• Developer: Luca Garofalo (Lucksi) – GitHub profile and repository. GitHub • License: GPL‑3.0 (see LICENSE in repo). GitHub
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