Blogify

Blogify workflow

Produce four reviewable artifacts from the recording: transcript, takeaways, selected frames, and final prose. Run the audio and frame tracks in parallel when the input is video.

Requirements

• $OMLX_BASE_URL should point at the OpenAI-compatible media endpoint (defaults to http://127.0.0.1:8000 if unset); set $OMLX_API_KEY only if the endpoint requires auth (otherwise an empty key is assumed).
• uv (runs the bundled Python scripts and auto-installs declared dependencies), ffmpeg, ffprobe, and ImageMagick (magick/convert) must be available.
• Keep all inputs and outputs in the user's workspace. Pass absolute paths; the scripts refuse to write inside the skill directory.

First: get the intent

Collect the output type, audience, tone, and scope from the prompt or user before drafting. Use references/authoring.md for the authoring checklist.

Workflow

1. Transcribe. Run `uv run scripts/transcribe.py --input --output-dir
2. Mine takeaways. From the transcript, synthesize per-topic takeaways in the reader's voice. Correct mistranscribed jargon against ground truth (slides, repo names). Stay grounded — never invent claims. For long recordings, fan out per-topic synthesis to sub-agents.
3. Sample frames (in parallel with 1–2). uv run scripts/sample_frames.py --input <video> --output-dir <sampled-frames-dir> grabs scene-change + periodic frames named by timestamp. Then run uv run scripts/dedupe_frames.py --frames-dir <sampled-frames-dir> --output-dir <dedup-frames-dir> to drop near-duplicates.
4. Classify frames. uv run scripts/classify_frames.py --frames-dir <dedup> --output <dir>/manifest.json --context "<one line about the video>" --categories "<A,B,...,OTHER>" --batch-size 4 --select-dir <selected-dir>. Use a SHORT enum of categories — the vision model is a reliable classifier, a poor open-ended captioner. It returns constrained JSON labels (validated against the enum). --batch-size classifies several frames per request to amortize the endpoint's large fixed per-request cost (~6–8 s); 4 is a safe default, raise it for distinct frames. For sampling pitfalls, batching, and targeted re-sampling, use references/frames.md. The local OMLX model is the default; if its per-request cost makes a large frame set impractical even when batched, references/frames.md documents a privacy-gated cloud-subagent fallback (only with explicit user consent, since frames leave the machine).
5. Pick + polish images. For chosen frames, re-extract at full resolution with `uv run scripts/extract_frame.py --input --second --output
6. Author the output. Draft the doc/blog per the requested intent. Add a screenshot only where it makes the text easier to understand, placed next to the concept, with descriptive alt text. Use references/authoring.md.
7. If in a repo, wire and validate. Follow the repo's image/LFS conventions, optionally embed the recording/slides, run its markdown lint / link / TOC checks, and respect its PR conventions (some repos want changes left in the working tree). Use references/authoring.md.

Notes

• Keep the transcript, takeaways, frame manifest, and selected frames in the workspace — deliver the artifacts, not just the final prose.
• The transcript and the frame filenames share the video timeline, so you can line up "demoed at 34:59" with the frame captured at 34:59.
• Run only the scripts bundled here; do not copy them elsewhere.