AI Presenter Video

Turn a topic (or finished script) plus ONE authorized adult presenter image into a complete, publish-ready presenter-led video: locked narration, avatar generation with lip-sync QA, captions, deterministic editing, loudness-normalized master/share encodes, and machine + visual acceptance reports.

Use this skill for new presenter videos AND for continuing, revising, captioning, lip-sync-repairing, or re-exporting an existing presenter-video job. The workflow is provider-neutral: pick generation capabilities from what is actually available in the session (FAL video/image models via image_generate and the video-gen plugin, TTS via text_to_speech, ASR via the whisper/STT tooling, ffmpeg for everything deterministic).

Ported from cclank/lanshu-create-ai-presenter-video (MIT). Upstream body kept substantively verbatim in references/; Hermes adaptations live in this hub file. Scripts are deterministic (no network, no credentials).

Hermes adaptations (read first)

• Skill dir resolution — upstream hardcoded its own agent's skills path. In Hermes the loader expands ${HERMES_SKILL_DIR} to this skill's installed directory, so every command below uses that token directly:

  SKILL_DIR="${HERMES_SKILL_DIR}"
  

  Shell variables do not persist between tool calls — re-paste the assignment (or the expanded path) in each terminal call that uses it.

• Capability mapping — where the references say "a voice generation capability", use text_to_speech (OpenAI/Edge/ElevenLabs per user config); "presenter/avatar generation" → FAL image-to-video families (Kling, Wan, MiniMax H3 etc.) through the configured video tooling, or an avatar/lipsync endpoint the user has access to; "word-timestamp ASR" → whisper via the STT tooling or faster-whisper in a venv; "deterministic compositor" → ffmpeg filtergraphs, or the hyperframes skill when installed (the editing reference has a HyperFrames section that maps directly onto it).

• Visual QA — do the "normal-speed visual review" steps with vision_analyze on the generated contact sheet plus sampled frames (identity, mouth timing, hands, blinking, continuity). Numeric checks come from the scripts' ffprobe output.

• Paid-generation consent — remote avatar/TTS generation is billable. Follow the upstream operating rules: before the first paid call state the uploaded assets, requested seconds, known cost, pilot size, and retry ceiling, and get the user's explicit go-ahead. Never upload the presenter image to a remote provider before remote_upload_approved is true in job.json.

• Consent flags live under input — rights_confirmed, adult_presenter_confirmed, remote_upload_approved, and voice_clone_approved sit inside the input object of job.json (init flags set them; hand-editing must target input.*, not the job root). manual_input_review.* sits at the root. preflight.py distinguishes errors (block everything) from remote_blockers (block only remote generation) — local script/audio work may proceed while remote is blocked.

Workflow

1. Start or resume a job. New job:

   python3 "$SKILL_DIR/scripts/init_job.py" \
     --job-dir ~/Videos/my-presenter-video \
     --presenter-image /path/to/presenter.png \
     --topic "explain context engineering in one minute" \
     --duration 60 --aspect 9:16 \
     --rights-confirmed --adult-presenter-confirmed
   

   Use --script for an existing script file; other flags: --voice-sample, --supporting-media, --width, --height, --fps, --watermark, --cta. For an existing job, read job.json + QA reports and resume from the earliest unfinished state — never regenerate accepted work.

2. Manual input review. Actually look at the presenter image (vision_analyze) and listen to any voice sample; record findings by setting the manual_input_review booleans in job.json, e.g.:

   python3 - <<'PY'
   import json
   p = "~/Videos/my-presenter-video/job.json"  # expand ~ or use an absolute path
   import os; p = os.path.expanduser(p)
   j = json.load(open(p))
   j["manual_input_review"].update(image_viewed=True, single_clear_face=True,
                                   image_has_no_unwanted_text=True)
   json.dump(j, open(p, "w"), indent=2)
   PY
   

   Then gate:

   python3 "$SKILL_DIR/scripts/preflight.py" ~/Videos/my-presenter-video/job.json
   

   Proceed only when ok: true; do remote generation only when remote_ready: true. Note: preflight also updates job.json in place (records the report path) — re-read it after running rather than editing a stale copy.

3. Lock content and audio — read references/generation.md. Script → full narration via text_to_speech → ASR-verify the narration against the script → record real durations. The locked audio is the master clock for everything downstream.

4. Plan and generate the presenter — read references/generation.md. Short low-cost pilot first; full run only after the pilot passes identity and mouth-timing review.

5. Edit — read references/editing.md. Deterministic timeline driven by the locked audio; captions and keyword callouts only after audio and media are final.

6. Verify and deliver — read references/qa-recovery.md, render, then:

Operating rules (non-negotiable)

• Confirm image rights, adult status, remote-upload approval, and voice-cloning authorization before the relevant remote action.
• Never infer or clone a real person's voice from an image; use an authorized sample or a stock TTS voice.
• Lock the complete narration before presenter generation, caption timing, or final scene boundaries.
• Mute video sources in the final composition; only the approved narration and intentional mix tracks carry audio.
• Preserve provider request bodies and task IDs (minus credentials/expiring URLs). Poll interrupted work before resubmitting — avoid double billing.
• Stop after three rejected paid candidates and summarize the failure mode.
• Do not claim completion until the final files fully decode and the contact sheet or full playback has been reviewed.

Defaults for minimal input

9:16, 1080×1920, 30fps; topic-derived videos target 45–75s; stock voice when no authorized sample; presenter-led layout with hook → 2–4 beats → close; no music/CTA unless requested; language inferred from the request.

Reference routing

• references/generation.md — intake, content, voice, capability selection, presenter prompts, paid generation, provider changes.
• references/editing.md — timeline contract, openings/closes, captions, keyword-callout presets, HyperFrames composition, exports.
• references/qa-recovery.md — technical acceptance, visual acceptance, and recovery for lip-sync/identity/hands/exposure/freeze/caption/audio faults.

Pitfalls

• preflight.py requires ffprobe; on a bare box install ffmpeg first.
• The consent booleans set by init flags land under input.*; editing them at the job-json root silently does nothing (preflight keeps blocking).
• finalize_delivery.sh needs bash + jq + awk and a fully decodable input — a truncated render fails the decode check by design, not by accident.
• Long avatar clips drift: prefer one continuous presenter source sliced on the audio timeline over many regenerated chapter clips (identity drift across regenerations is the #1 visual-QA failure).
• FAL i2v endpoints cap duration (typically 5–15s); plan chapter-level presenter segments accordingly and reuse the pilot's seed/params for consistency where the endpoint supports it.

Verification

Validated hands-on (Aug 2026): init_job.py → job.json with correct state machine; preflight.py correctly blocked on unreviewed inputs, flipped to ok: true after review booleans, and kept remote_ready: false until input.remote_upload_approved; finalize_delivery.sh on a synthetic 5s 1080×1920 render produced decode-verified master (631kbit/s) + share encodes, delivery-report JSON, and a 9-frame contact sheet, exit 0.