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 NachoLafuente/5050-gtm --skill linkedin-self-improvement-loopgit clone --depth 1 https://github.com/NachoLafuente/5050-gtmWrote 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/nacholafuente/5050-gtm/linkedin-self-improvement-loop)<a href="https://agentmods.dev/skills/nacholafuente/5050-gtm/linkedin-self-improvement-loop"><img src="https://agentmods.dev/badge/skills/nacholafuente/5050-gtm/linkedin-self-improvement-loop/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/nacholafuente/5050-gtm/linkedin-self-improvement-loop"><img src="https://agentmods.dev/badge/skills/nacholafuente/5050-gtm/linkedin-self-improvement-loop.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 | $0.00136 | $0.01558 |
| Opus 5 | $0.00068 | $0.00779 |
| Sonnet 5 | $0.00027 | $0.00312 |
| Haiku 4.5 | $0.00014 | $0.00156 |
Grade A, and why
linkedin-self-improvement-loop 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 11d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn self-improvement loop
Most "content analytics" is a noun: a report you read once and forget. This is a verb. It runs the build-measure-learn loop on your LinkedIn and keeps state, so every cycle compounds on the last instead of starting from zero.
1. MEASURE -> 2. RECONCILE -> 3. UPDATE BELIEFS
(ingest export) (did last (confidence rises if a
^ cycle's bet pattern held, halves if
| hold up?) it broke)
| |
6. WAIT <- 5. DRAFT BRIEFS <- 4. PROPOSE ONE EXPERIMENT
(re-run next (hand to a (biggest effect on the
export) drafting skill) least-settled belief)
It is advisory: it proposes experiments and emits draft briefs, but a human writes and posts every post. It never touches LinkedIn directly.
State it keeps (in --state, default ./state)
| File | What |
|---|---|
beliefs.json / beliefs.md |
The model: ranked traits (topic/hook/day/length) with a confidence that updates each cycle. .md is git-friendly and readable. |
ledger.jsonl |
One line per cycle: what was reconciled, discovered, proposed. The audit trail. |
snapshots/<date>.json |
Parsed metrics from each export, so trends compute across exports (beats the top-50 survivorship trap over time). |
A belief is just: "posts with this trait beat your average on the chosen metric." It starts at low confidence, climbs ~0.34 of the way to 1.0 each cycle it survives, and halves when a new export contradicts it. Survive enough cycles and it's a law; break and it's archived.
What the user downloads (same two files every cycle)
- Creator analytics (required) -
AggregateAnalytics_<name>_<dates>.xlsx. LinkedIn -> profile -> Analytics -> Export. Impressions, engagements, top-50 posts, followers, demographics. (LinkedIn caps it at the top ~50 posts / 365 days.) - Data archive (optional, recommended) - the
Complete_LinkedInDataExportzip (Settings -> Data Privacy -> Get a copy of your data -> larger archive, email, ~24h). ItsShares_*.csvcarries full post text so the loop can tag topics and hooks.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 123 lines · 0 tokens per session scan A 2da9d1c8f7ea
linkedin-self-improvement-loop is a skill published in the GitHub repository NachoLafuente/5050-gtm (3 stars, last pushed 2mo ago), licensed MIT. It adds 136 tokens to every session and 1,558 once invoked, about $0.0007 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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