Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/clear-memory)<a href="https://agentmods.dev/skills/sarveshtalele/linkedin-content-skill/clear-memory"><img src="https://agentmods.dev/badge/skills/sarveshtalele/linkedin-content-skill/clear-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 | $0.00127 |
| Opus 5 | $0.00008 | $0.00063 |
| Sonnet 5 | $0.00003 | $0.00025 |
| Haiku 4.5 | $0.00002 | $0.00013 |
Grade A, and why
clear-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 5d 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
Step 1 — Confirm
Ask the user to confirm first:
⚠️ Are you sure? This will erase all your saved feedback from memory. Type "yes" to confirm.
Wait for confirmation before running anything.
Step 2 — Clear (only after "yes")
python3 scripts/memory_manager.py clear
Step 3 — Confirm
✅ Memory cleared. Reset to defaults. Use /feedback to start building 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.
- 5d ago First seen · 20 lines · 0 tokens per session scan A c572cfc4af7a
clear-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 127 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.
Other skills, from other repositories
os-memory-manager
Trigger with "remember this", "update memory", "what should we record from this session", "capture learnings", "write a session log", or when closing a session. Guides agents on managing memory hygiene across sessions, deciding what to write to dated memory logs, what to promote to long-term memory.md, and when to…
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-cleanup-agent
Removes stale and orphaned entries from the RLM Summary Ledger. Use after files are deleted, renamed, or moved to keep the ledger in sync with the filesystem. user: "Clean up the RLM cache after I renamed some files" assistant: "I'll use rlm-cleanup-agent to remove stale entries from the ledger." user: "The RLM ledger…
hf-upload
Upload primitives for HuggingFace Soul persistence - file, folder, snapshot, JSONL append, and dataset card management with exponential backoff. Use when persisting agent learnings, snapshots, or semantic caches to HuggingFace.
rlm-audit
Audit RLM cache coverage - compare manifest against filesystem.
notebook
Project notes system to prevent AI context loss and reasoning loops. Init notes, save mid-flow, recover context, migrate messy notes, resolve lessons. Subcommands: /notebook, /notebook save, /notebook recover, /notebook migrate.