Ai Presenter Video

Make a verified AI presenter video from script + image.

Skill metadata

Source	Optional — install with hermes skills install official/creative/ai-presenter-video
Path	optional-skills/creative/ai-presenter-video
Version	1.0.0
Author	cclank (https://github.com/cclank/lanshu-create-ai-presenter-video), ported by Hermes Agent
License	MIT
Platforms	linux, macos
Tags	video, presenter, avatar, lipsync, tts, captions, creative
Related skills	hyperframes, kanban-video-orchestrator, comfyui

Reference: full SKILL.md

info

The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

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:

   bash "$SKILL_DIR/scripts/finalize_delivery.sh" \
     ~/Videos/my-presenter-video/renders/rendered.mp4 \
     ~/Videos/my-presenter-video/outputs my-video

   The finalizer preserves aspect ratio, runs two-pass loudness normalization (program ≈ −16 LUFS), produces master + share encodes, decode-verifies both, writes a delivery report JSON, and emits a nine-frame contact sheet. Inspect the contact sheet with vision_analyze before claiming completion.

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.