platform-norms

name	platform-norms
description	Use to research and record how a platform currently behaves — the length band that performs, hashtag counts, where the fold falls, whether links get demoted, what flops. Run it when social_check reports "no norms on file", when norms are flagged stale, or when the operator says results have changed. Triggers: "research the norms", "what works on LinkedIn now", "our reach dropped", "refresh the platform rules", "no norms on file".
tools	["social_platform_spec","social_record_norms"]

Researching platform norms

Nothing soft is built into this plugin. Character caps are compiled in because the platform enforces them; everything else — how long a post should be, how many hashtags read as native, whether an external link costs you reach — is researched, dated, and stored where the operator can see and edit it.

That's deliberate. A hashtag rule hard-coded in 2026 is still being asserted in 2029, with the same confidence and none of the truth. A norm with no date and no source is a guess wearing a number.

When to run this

• social_check said no norms on file for a platform you're about to write for.
• Norms are flagged stale (older than ~180 days).
• The operator says reach or engagement changed and nobody knows why.
• A platform announced a ranking or format change.

1. Look at what's on file

social_platform_spec(platform) — hard limits, whatever norms exist, and when they were last checked. social_platform_spec() with no argument shows freshness across every platform, so you can see what's missing at a glance.

2. Research it properly

Use web_search and fetch_url. What you're after, in rough order of value:

1. The platform's own engineering or creator posts. Primary sources beat commentary.
2. Recent, dated analyses with a sample size — someone who measured a few thousand posts this year beats a listicle with no date.
3. Practitioner reports where they agree with each other.

Weigh sources honestly:

• Prefer recent. A 2024 study of a 2026 ranking model is archaeology.
• Distrust anything selling a scheduling tool. Their "best time to post" data is marketing with a chart attached.
• Watch for folklore. Some norms get repeated for years after they stopped being true — the "external links are punished" claim in particular is asserted far more often than it's measured. If the evidence is thin, record link_penalty as false, or leave it out and put what you actually found in notes. Leaving a field out is a real answer.
• Two independent sources or it's a note, not a norm.

3. Record what you found

social_record_norms(platform="linkedin", norms_yaml="""
sources: ["https://...", "https://..."]
sweet_spot: [900, 1800]
hashtag_norm: [3, 5]
fold: 210
link_penalty: true
link_workaround: drop the link in the first comment and say so in the post
alt_text: recommended
hook: the first two lines are all that show before 'see more'
native_shape: short paragraphs with white space; a story or a number, then the lesson
flops: corporate announcement voice, buzzword stacks, obvious AI cadence
notes: the first-comment link trick still measurably beats an in-body link
""")

sources is required and the write is refused without it. Every other key is optional, and the record merges into what's already there — re-checking one field won't wipe the rest.

Only record what you actually found. If the research didn't settle the hashtag question, omit hashtag_norm. The linter skips checks it has no norm for and says so; that is a much better outcome than a threshold you made up, because the operator can see the gap.

4. Report

Tell the operator what you recorded, what you couldn't establish, and anything that contradicts what they believed. A norm that changed since they last looked is the most useful thing you can hand back — it usually explains a number they've been staring at.

Then offer to re-lint anything already in the queue for that platform: drafts approved under the old norms may read differently under the new ones.