Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add spinningrachel/career-engine --skill linkedin-post-reviewergit clone --depth 1 https://github.com/spinningrachel/career-engineWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/spinningrachel/career-engine/linkedin-post-reviewer)<a href="https://agentmods.dev/skills/spinningrachel/career-engine/linkedin-post-reviewer"><img src="https://agentmods.dev/badge/skills/spinningrachel/career-engine/linkedin-post-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/spinningrachel/career-engine/linkedin-post-reviewer"><img src="https://agentmods.dev/badge/skills/spinningrachel/career-engine/linkedin-post-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.01447 |
| Opus 5 | $0.00011 | $0.00724 |
| Sonnet 5 | $0.00004 | $0.00289 |
| Haiku 4.5 | $0.00002 | $0.00145 |
Grade A, and why
linkedin-post-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Post Reviewer Skill
Check Sequence
Run checks in this order. Stop at each FAIL and record it before continuing — do not stop the review early.
Check 1 — Word count
Compare word count to format ceiling:
- Format A: ≤950w — PASS; >950w — FAIL: "Word count [N] exceeds Format A ceiling (950w)"
- Format B: ≤600w — PASS; >600w — FAIL
- Format C: ≤500w — PASS; >500w — FAIL
Also flag if the post is far below the floor (Format A <700w, Format B <350w, Format C <200w) — the post may be underdeveloped.
Check 2 — Prohibited punctuation (§1)
Scan for:
- Em dashes (
—or--used as em dash substitute) — FAIL: cite exact sentence - Colons in X:Y structure on LinkedIn [LI] — FAIL: cite exact sentence
- Any other punctuation pattern banned in §1
Check 3 — Banned vocabulary (§2)
Scan for any word from the banned vocabulary lists in §2:
- AI writing patterns: crucial, pivotal, vibrant, showcase, tapestry, underscore (verb), landscape (noun), testament, enduring, foster, garner, interplay, intricate, foundational, transformative, robust, seamless, comprehensive, leverage (verb), synergy, spearhead, paradigm, land (verb in marketing sense)
- Hollow self-description: results-driven, passionate, dynamic, etc.
- LinkedIn-specific bans: "In today's world", "Today's landscape", "Unlock/Unleash/Harness", "Broke the mold", "Actually" for emphasis, "In reality", "Hit home", "How we show up"
Each instance = one violation.
Check 4 — Banned phrases and constructions (§3)
Check for:
- Named construction bans: "that made it land", "behind the [noun]", "at an inflection point", "quietly [verb]ing", "rare" as self-descriptor
- LinkedIn opening/transition bans: "Here's the hard truth", "Here's the thing", "And honestly?", "Real talk:", "Not gonna lie"
- False dichotomies: "It's not about X, it's about Y" structure
- Oppositional rhetoric: "everyone else does X, but I do Y"
- Vague phrases: "something clicked", "game-changer", "needle-mover"
Check 5 — Structural anti-patterns (§4)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 122 lines · 22 tokens per session scan A 133063638fef
linkedin-post-reviewer is a skill published in the GitHub repository spinningrachel/career-engine (4 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 1,447 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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