Convert FGD/webinar/SBCC video files into a structured artefact pack (transcript, frames, manifest, Tier 1 BLUF summary) the existing MEL specialists can consume. Use when Ane asks to "analyse a video", "transcribe a focus group", "process a webinar recording", "summarise a meeting recording", or ru

video content analysis /analyze video <path produces a manifest led artefact pack from one video file. Downstream specialists (qualitative coding specialist, intersectionality analyst, gender transformative assessor, sbcc campaign mel specialist) consume the manifest directly. When to use Trigger for any request that names a video, recording, FGD, webinar, training session, or SBCC clip and asks for transcript, summary, speaker analysis, or downstream coding. Trigger when Ane types /analyze video <path directly. Do not trigger for live audio capture (file based only in v1) or non video media (audio only files are out of v1 scope). Required inputs Ask in one batch. The first three are required. 1. Video path (required). Local file. Supported containers: mp4, mkv, mov, webm. 2. Privacy tier (required). One of sensitive , internal , public . Default sensitive . Sensitive content (FGDs, interviews, anything with informed consent constraints) stays on the local machine. Internal/public content can use Microsoft 365 Stream captions when available. 3. Consent status (required). One of not applicable , consent internal use only , consent research anonymised , consent research attributed , consent publication anonymised , consent publication attributed , consent unclear . consent unclear blocks downstream analysis. 4. Language hint (optional). Two letter code for Whisper (e.g. ro , en , fr ). Omit to auto detect. 5. Run diarization? (optional, default no). diarize runs pyannote.audio after transcription. Required for whose voices were heard analysis. Adds ~50% to runtime. 6. Brand template Word summary? (optional, default no). brand summary writes summary.docx in IPPF Visual Identity 2025 alongside the plain summary.md . 7. Output directory (optional). Default <video parent /<video stem .video analysis/ . 8. Microsoft 365 caption file (optional, internal/public only). Local .vtt path the user has already fetched via the Microsoft 365 MCP server. Skips Whisper. Method Step 1 — gather inputs Ask Ane for required inputs 1–3 in one message. If diarize , brand summary , or a caption path were passed in the invocation, do not re ask. Honour the explicit values. Step 2 — capture consent metadata when missing If consent metadata is incomplete (status set but no documented in / documented date / responsible person ), ask Ane in a second focused batch. Persist the captured values into the orchestrator call so they land in the manifest. Step 3 — invoke the orchestrator Use Bash to run the venv Python with a one line analyze video(...) call. Force the ffmpeg PATH extension before the run. Pass the captured kwargs. Step 4 — print the Tier 1 BLUF summary inline Read summary.md from the orchestrator's return value and print it in the conversation. Add the manifest path on a final line so Ane can hand it to /ann or to a specialist. Step 5 — per run feedback prompt Run the feedback prompt before returning. Use the venv Python: If the verdict is partial or failed , append the verdict and note to ~/.claude/skills/video content analysis/telemetry.jsonl so the next retrospective sees them. The orchestrator already wrote a telemetry line for the run; this second write is a verdict update keyed by source hash. (Stage 6 collapses these into a single in orchestrator call when the prompt timing is reworked.) Saving a regression fixture (verdict: partial / failed) When a run finishes with partial or failed , the skill offers to save it as a regression fixture under tests/video/fixtures/auto/<source hash / . Tier gated: privacy: public AND consent: not applicable consent publication → full save (source + transcript + frames + manifest) any other eligible verdict → metadata only save (redacted manifest with consent + speaker labels + transcript text + source path stripped) consent unclear → blocked entirely (regardless of verdict) The auto save directory is gitignored — auto saved fixtures are local only until you review and selectively commit. Step 6 — return Return the manifest path, the summary path, and (if any) the brand summary path to Ane. Suggest the next move: "Run /ann analyse the focus group findings in <manifest " — for in depth coding. "Run /ann compute speaker time share by gender across these 3 manifests " — for batch cross cuts. "Open summary.docx for the slide deck" — when brand summary was passed. Running the retrospective protocol Reads ~/.claude/skills/video content analysis/telemetry.jsonl , computes performance against the seven anchors at mel wiki/wiki/calibration/video content analysis.md , identifies recurring failures, and writes ~/.claude/skills/video content analysis/retrospectives/retrospective YYYY MM DD.md . Recommendations only. The retrospective never edits the skill, the spec, or any code. Ane reviews and approves before any change ships. The retrospective also fires automatically when should run retrospective(state) returns True at the end of any successful /analyze video run — i.e., after 10 successful runs OR 4 weeks since the last retrospective, whichever is first. Output manifest.json — single source of truth for downstream specialists. summary.md — Tier 1 BLUF summary, plain markdown. summary.docx — IPPF Visual Identity 2025 brand template (when brand summary ). transcript.json / transcript.txt / transcript.vtt — populated by the primitives. frames/ — sequentially numbered PNG frames. network.log — one line audit trail. Calibration anchors Operational quality benchmarks for this skill live at mel wiki/wiki/calibration/video content analysis.md in the work folder. Seven anchors: Schema validity (100% of manifests pass manifest v1.schema.json ) Transcription quality — Romanian (WER < 15% on the synthetic Romanian FGD fixture) Privacy enforcement (zero network egress != none under sensitive tier) Consent enforcement (zero deliverables published from consent unclear material) Data gap detection (every audio quality flags[] flag maps to a data gaps[] line; confidence based extension gated on Stage 4.5) Performance (60 min FGD with diarization < 90 min wall clock on this hardware) User satisfaction (≥ 80% of last 10 runs verdict ∈ {useful, partial}) The retrospective protocol consumes these anchors. See the section above. Evidence base: Stage 5 implementation plan at docs/superpowers/plans/2026 05 09 stage 5 skill orchestrator.md ; design spec at docs/superpowers/specs/2026 05 08 video content analysis design.md Sections 5–8 + 10; IPPF Visual Identity 2025 brand template at ane package.reporting.brand.IPPF FORMAT TEMPLATE .

video-content-analysis