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/show-memoryWrote 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/show-memory)<a href="https://agentmods.dev/skills/sarveshtalele/linkedin-content-skill/show-memory"><img src="https://agentmods.dev/badge/skills/sarveshtalele/linkedin-content-skill/show-memory.svg" alt="Measured on agentmods" 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 | $0.00015 | $0.00131 |
| Opus 5 | $0.00008 | $0.00066 |
| Sonnet 5 | $0.00003 | $0.00026 |
| Haiku 4.5 | $0.00002 | $0.00013 |
Grade A, and why
show-memory 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 7d 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
Read Memory
python3 scripts/memory_manager.py read
Display the memory contents cleanly. At the top summarise:
- How many feedback entries exist in the log
- What the current primary niche is set to
- What tone/style is configured
Then show the full memory content.
At the bottom remind the user:
💡 Edit
memory.mdin.claude/skills/scripts/to set your niche and tone. Use/feedbackto add new learnings.
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.
- 7d ago First seen · 21 lines · 0 tokens per session scan A 3d9c1dd9533d
show-memory is a skill published in the GitHub repository sarveshtalele/linkedin-content-skill (6 stars, last pushed 3mo ago), licensed MIT. It adds 15 tokens to every session and 131 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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rlm-search
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os-improvement-loop
Pattern 5: Concurrent Event-Driven Multi-Agent Loop. Coordinates multiple Claude sessions as OS threads sharing a common event bus and memory address space. Every loop cycle is a full improvement cycle: execute, eval against benchmark (KEEP/DISCARD), emit friction events, and close with surveys, metrics, memory…
rlm-distill-agent
Distills uncached files into the Recursive Language Model(RLM) Summary cache Ledger. You (the agent) ARE the distillation engine. Read each file deeply, write a high-quality 1-sentence summary, inject it via injectsummary.py. The purpose is if you read the full file once and produce a great summary once it will avoid…
rlm-curator
Knowledge Curator agent skill for the RLM Factory. Auto-invoked when tasks involve distilling code summaries, querying the semantic ledger, auditing cache coverage, or maintaining RLM hygiene. Supports both Ollama-based batch distillation and agent-powered direct summarization. V2 enforces Concurrency Safety…