بنقرة واحدة
token-budget-optimizer
Auto-discoverable wrapper for `.hforge/library/skills/token-budget-optimizer/SKILL.md`.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Auto-discoverable wrapper for `.hforge/library/skills/token-budget-optimizer/SKILL.md`.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | token-budget-optimizer |
| description | Auto-discoverable wrapper for `.hforge/library/skills/token-budget-optimizer/SKILL.md`. |
.hforge/ or other authoritative guidance surfaces.hforge/library/skills/token-budget-optimizer/SKILL.md.hforge/library/skills/token-budget-optimizer/references/.hforge/library/skills/token-budget-optimizer/scripts/inspect_token_surfaces.py.hforge/library/docs/authoring/token-budget-optimizer-port.mdUse the canonical skill under .hforge/library/skills/ for execution. Treat
this wrapper as a discovery entrypoint only, prefer the smallest authoritative
runtime surfaces first, and use the maintainer-facing port note only when the
import rationale or promotion intent matters.
Auto-discoverable wrapper for `.hforge/library/skills/bug-investigation/SKILL.md`.
Auto-discoverable wrapper for `.hforge/library/skills/double-diamond-feature/SKILL.md`.
Use for defects, regressions, outages, flaky tests, and unexpected behavior. Guides the agent through triage, containment, reproduction, hypotheses, root-cause evidence, fix verification, and recurrence prevention. Do not use as the primary workflow for new features; use double-diamond-feature instead.
Use for meaningful feature development before coding. Guides the agent through Discover, Define, Develop, and Deliver with evidence, options, validation, and rollback notes. Do not use for bugs, regressions, outages, or flaky tests; use bug-investigation instead.
Auto-discoverable wrapper for `.hforge/library/skills/complex-task-protocol/SKILL.md`.
Automatic operating protocol for complex agent tasks. Use when work is multi-step, risky, cross-module, orchestration-heavy, or likely to benefit from bounded sidecar help, verification discipline, recovery checkpoints, and durable learning capture.