القائمة
Use Hermes Agent as the intake, scheduling, and status layer for Hipson-governed Codex software work.
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 | hipson-codex-orchestrator |
| description | Use Hermes Agent as the intake, scheduling, and status layer for Hipson-governed Codex software work. |
Use this skill when a user asks Hermes to coordinate coding work, repository reviews, CI checks, Codex sessions, task packets, or Hipson workflows.
Hermes is the front desk, scheduler, and durable status layer. Hipson is the workflow authority. Codex is the implementation and verification agent.
Codex remains the user's primary control surface. The user may continue to work from Codex exactly as before; Codex decides when Hermes should be used for status tracking, scheduling, Telegram/gateway dispatch, or async handoff.
Never bypass Hipson for non-trivial repository work. When Hermes receives a repo task directly, start with:
hipson hermes intake --project /home/hipson47/code/<project> --task "<task>"
If the current directory is already the target repo, use:
hipson hermes intake --project . --task "<task>"
/home/hipson47/code/<project>.hipson hermes intake --project <repo> --task "<task>".git diff plus verification output as the source of truth.hipson hermes doctor
hipson hermes intake --project . --task "review failing CI"
hipson hermes events list
hipson route --task "security review of auth"
hipson scan . --include-diff
hipson packet review . --title "Review current delta" --include-diff -o runs/review-packet.md
hipson packet exec . --title "Implement next task" --goal "..." --allowed-edit src,tests -o runs/executor-packet.md
Keep user updates short:
Use when routing an ambiguous Computer Vision task to a bounded local, webapp, model, dataset, demo, and verification skill stack.
Use when starting a Computer Vision web application with Next.js, FastAPI, browser MediaPipe, typed APIs, bounded uploads, and private media handling.
Use when extracting, labeling, splitting, converting, validating, or versioning Computer Vision datasets with provenance, privacy, and leakage controls.
Use when building browser or Python hand, gesture, face, or pose interactions with smoothing, semantic events, and privacy-preserving media handling.
Use when building or reviewing OpenCV image, video, or webcam frame loops with deterministic fallbacks, performance measurement, and reliable cleanup.
Use when packaging an existing Computer Vision pipeline as a reproducible local CLI or lightweight demo with explicit artifacts and verification.