Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sarveshtalele/linkedin-content-skillnpx agentmods add skills/sarveshtalele/linkedin-content-skill/feedbackWrote 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/sarveshtalele/linkedin-content-skill/feedback)<a href="https://agentmods.dev/skills/sarveshtalele/linkedin-content-skill/feedback"><img src="https://agentmods.dev/badge/skills/sarveshtalele/linkedin-content-skill/feedback/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/sarveshtalele/linkedin-content-skill/feedback"><img src="https://agentmods.dev/badge/skills/sarveshtalele/linkedin-content-skill/feedback.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 | $0.00021 | $0.00228 |
| Opus 5.5 | $0.00008 | $0.00091 |
| Sonnet 5.5 | $0.00004 | $0.00046 |
| Haiku 4.5 | $0.00002 | $0.00023 |
Grade A, and why
feedback 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 1mo 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.
What it actually says
You are saving successful content patterns to the LinkedIn skill's reinforcement learning memory.
Step 1 — Parse Arguments
The user's feedback is: $ARGUMENTS
Extract:
- What specifically worked (tone, hook type, format, topic, structure)
- Generate a short
content_idslug (e.g. "contrarian-ai-hook", "storytelling-carousel") - Identify relevant tags (e.g. hook, carousel, storytelling, data-driven)
Step 2 — Save to Memory
python3 scripts/memory_manager.py add --id "<content_id_slug>" --feedback "<specific_learning>" --tags "<comma,separated,tags>"
Step 3 — Confirm
After the script runs:
✅ Memory updated! Saved: ""
Future posts, carousels, and calendars will now reflect this preference automatically.
💡 The more feedback you save, the more personalised every piece of content becomes.
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.
- 1mo ago First seen · 30 lines · 0 tokens per session scan A 740c7325a910
feedback is a skill published in the GitHub repository sarveshtalele/linkedin-content-skill (9 stars, last pushed 4mo ago), licensed MIT. It adds 21 tokens to every session and 228 once invoked, about $0.0001 per session on Opus 5.5. 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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