L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Productivité · SKILL.md

common-context-optimization

common-context-optimization

Manage context and prompt-cache costs without losing task state. Use for context management, orchestration cost, token budgets, large tool outputs, cache misses, or long-running conversations.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: common-context-optimization description: Manage context and prompt-cache costs without losing task state. Use for context management, orchestration cost, token budgets, large tool outputs, cache misses, or long-running conversations. metadata: triggers: files: - "*.log" - "chat-history.json" keywords: - reduce tokens - optimize context - summarize history - clear output - context management - prompt-cache - prompt caching - orchestration cost

Priority: P1 (HIGH)

Workflow

  1. Inspect host capabilities; do not assume agents can rewrite history, prompts, or memory.
  2. Project outputs after consuming them: retain decisions, evidence, errors, and required values; point to complete logs/artifacts by stable reference.
  3. Bound context at host-supported boundaries. Carry goal, active slice, authority, decisions, blockers, evidence links, and next action; preserve a stable append-only prefix where supported.
  4. Compact only when useful and supported. Keep required details retrievable; never discard source evidence or rely on a fixed turn/token threshold.
  5. Measure cache reads, replayed input, and total actor cost across the whole task. Separate observed usage from estimates; no invoice, savings, or efficacy claims without evidence.

Anti-Patterns

  • No history rewrite assumption: Project outputs or create artifact references; use only host-supported controls.
  • No fixed threshold: Compact from observed context pressure and task needs, not a universal turn/token count.
  • No evidence deletion: Keep stable references to source outputs, decisions, and verification.
  • No partial cost claim: Include cache reads, replay, and total actor cost; label estimates.

References

Skills enregistrées