Video Content Analysis

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.

$env:PATH = "$env:LOCALAPPDATA\Microsoft\WinGet\Packages\Gyan.FFmpeg_Microsoft.Winget.Source_8wekyb3d8bbwe\ffmpeg-8.1.1-full_build\bin;$env:PATH"
& 'C:/Users/AGasser/OneDrive/GitHub/personal-skills/skills/video-content-analysis/venv/Scripts/python.exe' -c @'
from pathlib import Path
from ane_package.video.orchestrator import analyze_video
from ane_package.video.types import ConsentMetadata, ConsentStatus, PrivacyTier
result = analyze_video(
    Path(r"<VIDEO PATH>"),
    privacy_tier=PrivacyTier.<TIER>,
    consent=ConsentMetadata(
        status=ConsentStatus.<STATUS>,
        documented_in=r"<PATH OR NOTE>",
        documented_date="<ISO DATE OR EMPTY>",
        responsible_person="<NAME OR EMPTY>",
    ),
    language=<"ro" OR None>,
    diarize=<True OR False>,
    brand_summary=<True OR False>,
    output_dir=<PATH OR None>,
    m365_caption_path=<PATH OR None>,
)
print("MANIFEST:", result.manifest_path)
print("SUMMARY:", result.summary_path)
print("BRAND_SUMMARY:", result.brand_summary_path)
'@

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:

& 'C:/Users/AGasser/OneDrive/GitHub/personal-skills/skills/video-content-analysis/venv/Scripts/python.exe' -c @'
from ane_package.video.feedback import prompt_verdict
v, n = prompt_verdict()
print(f"VERDICT={v.value}")
print(f"NOTE={n or ''}")
'@

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

/analyze-video --retrospective

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.