10-of-my-most-popular-text-to-image-series-prompts-78b0897e
generate a bunch of images, then you curate the results to handpick the best ones
Context window hygiene for long-running LLM agents. Decision rules for when and how to compress, mask, switch, or delegate context — backed by research (JetBrains/NeurIPS 2025, OpenHands, Letta/MemGPT, LLMLingua). Use when an agent runs for extended sessions, accumulates large tool outputs, approaches context limits, or suffers from compaction/overflow. Also use when designing agent architectures that need to manage context over time.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install compass-soul/shedExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Shed what you don't need. Keep what matters.
Named for molting — the process of shedding an outer layer to grow. Your context window is your skin. When it gets too heavy, shed the dead weight.
Tool outputs are 84% of your context growth but the lowest-value tokens you carry. (Lindenbauer et al., NeurIPS 2025 DL4C workshop, measured on SWE-agent). Everything flows from this.
ls -la from 20 turns ago.memory/YYYY-MM-DD.md. Future-you needs a trailhead, not a transcript.Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
# Shed — Context Hygiene for Agents *Shed what you don't need. Keep what matters.* Named for molting — the process of shedding an outer layer to grow. Your context window is your skin. When it gets too heavy, shed the dead weight. ## Core Principle **Tool outputs are 84% of your context growth but the lowest-value tokens you carry.** (Lindenbauer et al., NeurIPS 2025 DL4C workshop, measured on SWE-agent). Everything flows from this. ## The Rules ### After Every Tool Call 1. **Extract, don't accumulate.** When a tool returns large output (file contents, search results, logs, API responses), immediately write the key facts to a file or compress into bullets. The raw output is now disposable. 2. **Ask: "Will I need this verbatim later?"** Almost never. The answer you extracted is what matters, not the 500 lines that contained it. ### When Context Reaches ~70% 3. **Trigger condensation.** Don't wait for the platform to compact you — that's losing control of your own memory. At 70%, actively shed. 4. **Mask old tool outputs first** (free, no LLM calls). Keep your reasoning and action history intact — you need your decision chain, not the raw `ls -la` from 20 turns ago. 5. **Summarize reasoning only as backup.** If masking isn't enough, compress old reasoning turns. But this is lossy and costs an LLM call — use sparingly. 6. **Never re-summarize a summary.** If you've already condensed once and context is growing again, switch context or spawn a sub-agent. Recursive summarization compounds errors. ### When Completing a Task 7. **Write results to file, then switch context immediately.** Stale completed-task context is anti-signal for your next task. Don't carry it. 8. **Leave breadcrumbs.** Before switching: write what you did, what's next, and where the files are to `memory/YYYY-MM-DD.md`. Future-you needs a trailhead, not a transcript. ### When Delegating Work 9. **Spawn fresh-context sub-agents for complex sub-tasks.** Your context is noise for their work....
generate a bunch of images, then you curate the results to handpick the best ones
generate a bunch of images, then you curate the results to handpick the best ones
Productivity system based on Maya calendar with 13 natural tones for project management and personal development
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.