# Postey Analytics

> Read what a post or an account actually did, say what the numbers support, and turn that into the next content decision. Includes the checks that stop a number being read as a result when it is not one.

- Skill: `posteyai/postey-analytics` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add posteyai/postey-analytics`
- Raw SKILL.md: https://api.skillmd.com/api/skills/posteyai/postey-analytics/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: posteyai (https://skillmd.com/u/posteyai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/posteyai/postey-analytics

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# Postey Analytics

Reading numbers is easy. Saying what they support, and refusing to say more, is the job.

## Check the data is real before interpreting it

**Do this first, every time.** Platform analytics arrive partial, delayed, or absent, and a
confident reading of a broken feed is worse than saying nothing.

| Symptom | What it means | What to say |
|---|---|---|
| every metric is `0` across all posts on a platform | the feed is not returning, or the platform never provided it | say the platform reports nothing; do not rank by it |
| impressions present, engagement all `0` | partial fetch | use impressions only, and name the limitation |
| one post has numbers, its siblings do not | the others have not been fetched yet | compare only the ones that have data |
| `fetch_count` is 0 | never collected | not "zero performance" — no data at all |

**Zero is not a result.** On a real account checked during development, every LinkedIn post
reported zero impressions while the same content on X reported between 481 and 2,935. Ranking that
account's LinkedIn posts would have produced a confident ordering of nothing.

## What the numbers can and cannot support

They **can** support: this post got more reach than that one; this format appears more often near
the top; this account's median engagement moved after a change.

They **cannot** support: why. Attribution needs a controlled comparison that social platforms do
not give you. Say "these three of your top five were lists" — not "lists perform better for you",
which is a causal claim from five data points.

Three traps worth naming out loud when they apply:

- **Survivorship.** Top posts are top by definition. Look at the bottom too, or every conclusion
  is "do more of what already worked".
- **Sample size.** Under ~10 posts with real data, report observations, not patterns.
- **Time confounds.** A post published during a spike in following did not earn that reach on
  content alone.

## Turning it into a decision

The output is a recommendation the user can act on this week, with its evidence attached:

```
Observation   4 of your 5 highest-reach posts on X opened with a question.
Evidence      posts 6016, 6028, 6017, 5994 (481–2,935 impressions)
Caveat        5 posts, one platform, one month
Recommendation  Try a question opener on the next three X posts and compare.
```

Never emit a recommendation without its caveat. The caveat is what makes it honest, and it is what
stops the next session treating a hunch as an established fact.

Hand recurring findings to `postey-voice` if it is installed: a consistent structural habit among
top posts is exactly the kind of observation its rules ledger can accumulate evidence for. Do not
write to its ledger from here — say what you found and let the user decide.

## Scope

Per-post numbers: `postey://posts/{post_id}/analytics`. Account roll-up:
`postey://accounts/{account_id}/analytics`. Ranked posts:
`postey://accounts/{account_id}/analytics/posts`.

Whether a post **published at all** is not this skill's question — that is `postey-ops` and
`post.publish_status`. A post that never went out has no performance to explain, and confusing the
two produces a very confident analysis of a post that does not exist.