Content intelligence

Content intelligence answers “what works, and why?” with evidence you can check: openings that held attention, breakdowns of reference videos, accounts whose posts beat their usual reach, and how your own uploads are doing. Then it turns that evidence into a brief for something original.

Find a reference

GET /v1/hooks searches a bounded recent corpus plus your own saved video analyses. Filter by q, platform, niche, language, hookType, visualHookType and evidence (observed, metadata or all). Page with limit and offset; nextOffset is null at the end of the sample. The sample changes as new content arrives, so treat it as a search, not a stable export. GET /v1/hooks/stats reports counts over the same filtered sample.

curl "https://api.sigmora.org/v1/hooks?q=budget&platform=tiktok&evidence=observed&limit=20" \
  -H "Authorization: Bearer $SIGMORA_API_KEY"

Observed entries carry speech or timestamped visual evidence. Metadata entries are suggestions inferred from titles and descriptions — not verbatim opening lines. Hook classifications are heuristics, not proof of why something performed. Items with an unknown platform, category or language are left out when you filter on that field.

Analyze a video independently

curl -X POST https://api.sigmora.org/v1/video/analyze \
  -H "Authorization: Bearer $SIGMORA_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: reference-breakfast-001" \
  --max-time 600 \
  -d '{"projectId": "prj_9x2a4c", "videoUrl": "https://www.youtube.com/shorts/YOUR_VIDEO_ID"}'

import os
import requests

session = requests.Session()
session.headers["Authorization"] = f"Bearer {os.environ['SIGMORA_API_KEY']}"

analysis = session.post(
    "https://api.sigmora.org/v1/video/analyze",
    headers={"Idempotency-Key": "reference-breakfast-001"},
    json={"projectId": "prj_9x2a4c", "videoUrl": "https://www.youtube.com/shorts/YOUR_VIDEO_ID"},
    timeout=600,
).json()["analysis"]
print(analysis["id"], analysis["hook"])

const headers = {
  Authorization: `Bearer ${process.env.SIGMORA_API_KEY}`,
  "Content-Type": "application/json",
};

const { analysis } = await fetch("https://api.sigmora.org/v1/video/analyze", {
  method: "POST",
  headers: { ...headers, "Idempotency-Key": "reference-breakfast-001" },
  body: JSON.stringify({ projectId: "prj_9x2a4c", videoUrl: "https://www.youtube.com/shorts/YOUR_VIDEO_ID" }),
  signal: AbortSignal.timeout(600_000),
}).then((r) => r.json());
console.log(analysis.id, analysis.hook);

The call is synchronous and can take several minutes. It accepts public videos up to 180 seconds long, checked against the downloaded media before any analysis. It is a paid capability — GET /v1/capabilities has the live price — and failures are not charged. After a connection failure, retry with the same idempotency key. The response contains analysis.id, hook, scenes, transcript, ctas, pacing, lowConfidenceFields, analyzedAt, and the original source URL. Speech that starts in the first six seconds forms the spoken opening, even if a segment runs past six seconds. Scene boundaries are approximate, not frame-perfect. Call-to-action matching recognizes English phrases. Pacing describes the scenes observed, not a count of every cut. GET /v1/video/analyses/{analysisId} reads a saved analysis for free. Analyses are private to you and your workspace; analyzing a URL never adds it to shared research data.

Turn evidence into production

curl -X POST https://api.sigmora.org/v1/research/briefs \
  -H "Authorization: Bearer $SIGMORA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "analysisId": "YOUR_ANALYSIS_ID",
    "topic": "Breakfast on a budget",
    "audience": "Busy parents",
    "targetPlatform": "shorts"
  }'

Brief creation is free and deterministic. It returns suggested original beats, timestamped references, a productionPrompt, and an adaptationRequest. Suggestions are labeled separately from reference observations.

Add your projectId to brief.adaptationRequest and send it to POST /v1/text/adapt with a new idempotency key.
Review the returned script.
Send it as story to POST /v1/video/render with videoType: "social", aspect: "vertical", and your projectId. Use another idempotency key.
Poll GET /v1/jobs/{jobId} and collect artifacts.download only when status is done.

Adaptation and rendering are charged at their normal prices. The dashboard’s Research → Content intelligence tab runs the same flow.

Preview, save, and run discovery

POST /v1/discovery/preview accepts intent, keywords, excludeKeywords, and platforms. It returns a deterministic quality check and a configPatch. It does not run a model, collect content, create a resource, or spend credits. Without explicit keywords it uses phrases from the intent; provide several specific phrases for better coverage. Send the patch to PUT /v1/discovery/configs/{brandId}. Use enabled: false for manual research, then POST /v1/discovery/runs with contentBrandId and kind: "discover". To schedule daily research set enabled: true and discoveryIntervalMinutes: 1440. GET /v1/discovery/configs/{brandId}/results reads the plan’s results, applying its platforms and its exclusions (matched case-insensitively against titles, descriptions and hashtags). Matching is by theme and phrase — it does not guarantee that every result matches your intent, or that every platform is fully covered. Source health and partial runs are reported as in Niche discovery.

Creator breakouts

GET /v1/creators/{creatorId}/outliers?platform=youtube_shorts, where creatorId is the creator’s username or id, returns observed posts compared with the median views of up to 20 earlier observed posts from that same creator and platform. Five baseline posts and a positive median are required; otherwise outlierRatio is null. A ratio of three or more is labeled a breakout. The response includes the baseline sample size, publication window and when the metrics were observed. Collected posts skew toward successful ones and lifetime views are not adjusted for age, so treat this as a research signal, not a full channel audit.

Watched accounts and their proven hits

GET /v1/competitors and GET /v1/competitors/{competitorId} return each watched account with performance: the median views (or likes) of its posts older than 72 hours, how many posts that median covers, and up to 12 hits that beat it, each with its outlierRatio. A hit needs a ratio of two or more over at least eight mature posts; with fewer, status is insufficient-history rather than a guessed ratio. reason says why there are no numbers (no data key for the platform, out of credits). Accounts are measured when added and refreshed weekly. To measure one now:

curl -X POST https://api.sigmora.org/v1/competitors/$COMPETITOR_ID/measure \
  -H "Authorization: Bearer $SIGMORA_API_KEY" \
  -H "Idempotency-Key: measure-acme-001" \
  -H "Content-Type: application/json" \
  -d '{}'

It is charged like Measure now in the dashboard: one collection at the data provider’s measured cost when a paid source is needed for the account (YouTube is free). A measurement under an hour old is returned as is, with measured: false, and is not charged again.

Your channel’s performance, and fixing a live video

GET /v1/performance/channels reads your connected YouTube channels’ measured uploads, each against its own channel’s medians, with a diagnosis:

Diagnosis	Meaning	The fix
`winner`	Well above the usual	Make variations of the format
`packaging`	Low CTR, normal retention	A new title or thumbnail
`hook`	Viewers leave early	Recut the opening
`under` · `steady`	Below or around the usual	Keep iterating
`too-new` · `unmeasured`	Not enough data yet	Wait

It also lists recentEdits — live changes already made, with the numbers from before them. Reading it is free. To change a live video, propose the edit:

curl -X POST https://api.sigmora.org/v1/performance/video-edits \
  -H "Authorization: Bearer $SIGMORA_API_KEY" \
  -H "Idempotency-Key: retitle-dQw4w9WgXcQ-001" \
  -H "Content-Type: application/json" \
  -d '{"videoRef":"dQw4w9WgXcQ","title":"A sharper title","reason":"packaging"}'

Nothing changes yet. The answer is the same approval a Sigma turn proposes: approval.summary says exactly what will change and approval.approval is a signed reference. Send it to POST /v1/sigma/approvals to apply the change through the channel’s YouTube connection. The change is recorded with what it replaced and the video’s numbers from before it, so a later GET /v1/performance/channels shows whether it worked. A free edit can run under the workspace’s approval setting without the extra call; the answer then carries approval.autoApproved. Pass projectId to keep the conversation in a project.

MCP

Use video_analyze, video_analysis_get, hooks_search, hooks_stats, discovery_preview, creator_outliers, and research_brief. Paid calls require projectId and idempotencyKey. Continue with text_adapt, video_render, and jobs_get for production. Watched accounts are competitors_list, competitors_get and competitors_measure; your own channels are performance_channels_get and performance_video_edits_propose, applied with sigma_approve; the niche corpus is discovery_corpus_get.

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Operate

Find a reference

Analyze a video independently

Turn evidence into production

Preview, save, and run discovery

Creator breakouts

Watched accounts and their proven hits

Your channel’s performance, and fixing a live video

MCP

​Find a reference

​Analyze a video independently

​Turn evidence into production

​Preview, save, and run discovery

​Creator breakouts

​Watched accounts and their proven hits

​Your channel’s performance, and fixing a live video

​MCP

Find a reference

Analyze a video independently

Turn evidence into production

Preview, save, and run discovery

Creator breakouts

Watched accounts and their proven hits

Your channel’s performance, and fixing a live video

MCP