Why My LinkedIn Auto Posts Failed Without Any Error Message

Why My LinkedIn Auto Posts Failed Without Any Error Message

ByUS Stacked

1. ChatGPT prompts that sound good but return garbage text

The first iteration sounded great in preview. “Use this prompt to draft a high-engagement LinkedIn post from your blog post summary.” I pasted the prompt into ChatGPT 4, linked it via Zapier’s ChatGPT action, and mapped the blog description pulled from Webflow CMS into the input. Looked perfect. Ran the Zap. Posted nonsense.

The GPT response seemed normal on the surface — about 800 characters, paragraphs spaced right, used hashtags like a LinkedIn post — but the substance was mush. It would reference “insights from the article” that weren’t actually there. Whole sections about interviews that didn’t exist. I realized later it was hallucinating “context” based on the URL itself — not the content I scraped.

I forgot: if you just paste a page summary or API-pulled body text, GPT will still attempt to use prior training on the domain. You have to wrap your content input in quotes and state clearly: “Only reference the following input as the full blog post.” That fixed part of it. But it kept writing slightly unhinged last lines like “Follow for more.” even when the prompt told it to avoid generic CTA phrasing.

Eventually rebuilt the prompt like this, which held up better under Zapier’s trigger limits:

{
  "role": "system",
  "content": "You are a professional B2B copywriter creating a LinkedIn teaser for a blog post. Write a concise post under 1300 characters that pulls insights only from the provided content. Do NOT invent sections, do NOT say 'click below'."
},
{
  "role": "user",
  "content": "Here is the article you are summarizing: \"{{CMS Content}}\"
}

2. Zapier cutoff limits that silently truncate your text block

This one took me forever to catch — Zapier will let you pass a text blob to OpenAI’s action, then truncate it mid-thought without warning if it exceeds token limits. The UI doesn’t flag anything. The Zap tests fine. GPT just sees half a blog post and goes, “Cool, the key insight must be frogs.”

Turns out, depending on formatting and token density, you’re only safe up to maybe 3000 characters of full article body — and even that’s if your prompt is lightweight. A long-form post from Notion or WordPress will break the context window clean in half unless you preprocess.

What finally worked:

  • Split body into chunks via a formatter step
  • Pipe only the intro and most actionable middle section
  • Log exact payload into Airtable first to cross-check what GPT actually sees
  • Use a max_tokens cap and temperature below 0.6 to avoid compound hallucinations

I caught one version where the GPT summary ended mid-sentence, at character 1127, with no punctuation. Looked real enough unless you cross-checked. That one went live to LinkedIn before someone in sales DMed me something like “…are you okay?”

3. LinkedIn business pages require a different Zapier action path

There are two ways to post to LinkedIn via Zapier: one for personal accounts, one for organization pages. And if you wire it up using your personal connection token, it’ll look fine in the editor, even let you preview posts — but then nothing appears publicly, ever. No errors, no failures.

I originally authenticated using my personal LinkedIn in the Zapier setup step and selected my LinkedIn Page from the dropdown. It populated. It saved. It lied. When the Zap ran, the post never actually went live. Dug through history. Zapier’s run log: 100% success. LinkedIn: radio silence.

Eventually found a thread buried on zapier.com where someone mentioned that you must reauthenticate using the LinkedIn Pages connector separately. It’s essentially a different OAuth scope. No UI guidance tells you that. Once I reconnected with the correct account type, the test post fired immediately.

Helpful workaround: run a dummy Zap that posts something like “TEST POST IGNORE” as an initial test, then manually check your LinkedIn Page in incognito to confirm visibility. Notifications lied half the time.

4. Using Notion content as a live source breaks unpredictably

One week, I had a clean setup: team drops blog drafts in Notion, Zap runs daily, filters for new-in-published status, parses the body with line breaks preserved, feeds it to GPT to compress for LinkedIn.

Wednesday morning, I get two DMs from coworkers asking why the post says “Table block not found.” Checked the source. Notion changed its API behavior — now returns a different block type, and line breaks wrap after each block. The paragraph splitter I used (split on \n\n) completely misfires. Headers merge into bullets, bullets become flat paragraphs.

Learned to instead use the Notion API’s plain text export via Make.com, which flattens structure more predictably — or use their new markdown output. Zapier’s Notion-to-text is… unpredictable. It adds phantom line breaks between inline blocks like page links or multi-colored text.

You can mitigate this by logging raw Notion values into a step and checking for rogue "type":"unsupported" fields. Once you see that, the API call is likely failing silently, returning a placeholder or skipping the content entirely.

5. Auto hashtags misfire when tone detection relies on training bias

I wanted to make the captions feel more “LinkedIn native.” So I added a prompt modifier like: “Add 2-3 niche hashtags based on the content themes.” That worked fine for SaaS analytics content, but when the blog topic was anything tangential — like internal culture or API governance — it threw stuff like #synergy or #hustle in the output.

Hadn’t realized how much the default GPT personality “imagines” your audience unless you nail its voice early. GPT tends to slide toward stereotypical LinkedIn influencer language unless corrected. Swapped in a voice preload like:

{
  "role": "system",
  "content": "You write dry, technically sound B2B posts. You hate slang. Use lowercase hashtags. Avoid sales-y phrasing."
}

The model relented. I started getting #audittrail instead of #winning. Testing with Claude produced even flatter results, which in this case was perfect — it leaned towards documentation-aligned summaries and very literal hashtags.

Still, if GPT thinks the blog is “inspirational,” even a technical breakdown, it tries to help by inserting motivational hashtags unless explicitly constrained. One way to reduce this is to end the user prompt with “Do not use vague generalities or language that could fit any topic.”

6. Zap throttling kicked in mid-week after a quiet launch

Everything ran fine Monday and Tuesday. Scheduled Zap checks each morning at 6am, pulls new CMS posts, filters down, routes to GPT, publishes to LinkedIn. On Wednesday, nothing. No post. No error. The Zap ran, passed all steps — then quietly missed the GPT call.

Turns out Zapier throttles OpenAI requests when usage spikes mid-month. But it doesn’t stop the Zap, it just queues the request. If it doesn’t finish in time, the step completes with null and proceeds. Zapier’s log will say “completed” unless you really dig in to the GPT input/output.

I confirmed it by splitting out the GPT step into its own Zap triggered by a webhook, then timing the response. Normal days: 2-4 seconds. That Wednesday: 37 seconds, followed by nothing returned at all.

The fix: reduced daily GPT usage by pre-generating content for the week on Mondays instead of daily pulls. I also added an Airtable sync step to track GPT draft text before it reaches LinkedIn.

Helpful to include a fallback logic in your Zap: if GPT returns null or a short response (e.g. character count under 300), send a Slack DM instead of continuing to auto-post. Caught two failures early that way.

7. Inline link formatting varies depending on GPT output structure

This one’s more cosmetic, but still broke formatting. LinkedIn supports inline links only in personal posts, and even then it strips markdown syntax like [title](url).

When passing blog links through GPT, I wanted it to surface one key link at the end — usually “Read more at [domain]” with a link in the text. GPT would alternate between rendering that correctly and dropping only the raw URL, depending on if it returned HTML, plaintext, or markdown.

The ironic part: when I used the GPT Zapier plugin to preview responses directly in the UI, they were styled fine. But the actual payload from the Zap output was plaintext. Lost all formatting, line breaks doubled, and links became embedded URLs without anchor text.

Small fix that consistently worked:

Tell GPT: “End the post with the following exact sentence, unmodified: Read more at DOMAIN.com.” Then in the next Zapier step, replace DOMAIN.com with your actual article link via a text replace step. Feels dumb but works reliably across markdown inconsistencies.

8. Fallback publishing logic using Airtable delays and checkmarks

Eventually I gave up trusting GPT to always return in real time. Rebuilt the flow around Airtable as the core hub. New content is logged with “Ready = false” and “Review Req = yes.”

Then a second Zap checks for posts with Ready = true every few hours and pushes them to LinkedIn. That way I can manually review flagged posts, check character count, tweak hashtags, or delete any that just say “Insights are powerful” (which happened twice on Thursday).

The Airtable base tracks:

  • Article title
  • LinkedIn draft text
  • Character count field using LEN formula
  • Status flags like Ready, Needs Edit, and Error
  • Timestamp fields edited by each review pass

I still want it all auto. But building in safety steps like this let the prompt architecture stay aggressive while leaving room to catch weirdness.