Caption Desk / API
Get a token

Driving Caption Desk from code

Everything the web app does is available over HTTP. The base URL is https://api.skillsafe.ai/v1/app-api, every request carries Authorization: Bearer <token>, and every response is the same envelope.

The task field comes first

This app has five lanes behind one endpoint. Every run body must carry a task field naming the lane - it is what the system prompt routes on. Send the wrong one and you get a valid package of the wrong kind; omit it and the model picks the closest lane and tells you which it chose.

One more shape trap: the run body is the input object. Do not wrap it in an {"input": ...} envelope - that returns 200 while hiding task from the model, which is the most confusing way this API can fail.

taskLaneFieldsSections returned
planChoose the channel before the file is writtenbrief, knownSummary, The Header, The Numbers, Reasoning, Next Step
checkWhether this file can be delivered, and whether it readssheet, worrySummary, Verdict, Findings, Corrected File, Next Step
wireWhat each caption costs and when it has to startsheetSummary, The Budget, Cue By Cue, What Each Mode Costs, Next Step
grid32 columns, 15 rows, and no thirty-third columnsheetSummary, The Grid, Row By Row, What Positioning Costs, Next Step
deliverDecide what changes: the edit, the channel, or nothingsheet, fixedSummary, An Edit Fixes, Only The Channel Fixes, Nothing Fixes, Next Step

Only task and the lane's own required fields are mandatory: sheet on check, wire, grid and deliver; brief on plan. Every field is a string - there are no number fields on this app. sheet is the caption file itself: SRT or WebVTT, pasted unmodified, optionally with a few KEY: value lines above the cues.

The header takes PROGRAMME, FPS, MODE, SERVICES, ROWS, CPS and DURATION. None is required and all of them change a number: FPS most of all, because the byte budget is two bytes times the frame rate and is therefore directly proportional to it. 29.97 is read as the exact 30000/1001 rather than the decimal.

MODE is pop-on, roll-up or paint-on. They differ only in their control codes - a pop-on cycle pays RCL, a PAC per row, EOC and EDM; a roll-up sets its base row once and pays a carriage return per row after the first; a paint-on writes straight to the displayed memory. Every control code is a two-byte pair transmitted TWICE, so each one costs four bytes, and on a busy channel that difference is what decides whether captions arrive.

SERVICES is how many caption services share the field. CC1 and CC2 both ride field 1 and share its two bytes a frame, so SERVICES: 2 halves the rate for both: 29.97 B/s each instead of 59.94, and every caption takes twice as long to send. A second language is not extra capacity.

Timecode works, but only with a rate. 00:00:04:12 is hours, minutes, seconds and FRAMES, and a frame is only a duration once FPS says how long one is - so a timecoded file with no rate returns an error rather than an assumption. A semicolon (00:00:04;12) means drop-frame, which skips LABELS and never a frame of picture, so it does not change the byte budget at all.

Markup is counted, not ignored. <i>, </i>, <font> and {\i1} are each a mid-row control code, so each is another four bytes on the wire; an italic word costs two of them. Characters outside the basic set - accents, curly quotes, dashes - are two-byte codes rather than one byte. WebVTT cue settings such as line: and align: are read and reported, because the 32 by 15 grid has rows and columns and nothing else.

A BYTE COUNT is reported in bytes, a transmission in frames and in seconds, a share as a percentage to one place, and a reading speed in characters a second. The wire is frame-quantised, so frames are the smallest unit anything here has.

Add $model to any body to choose the model for that run: gpt-5.6-luna, gpt-5.6-terra (the default) or gpt-5.6-sol. Luna caps output at 4,096 tokens and will fail the check, wire and grid lanes rather than shorten them - a findings table, a corrected file, or a row per cue, is several thousand characters before the reasoning starts.

The response envelope

Success and failure have the same outer shape, so one check covers both.

{
  "ok": true,
  "data": {
    "...": "the result"
  }
}
{
  "ok": false,
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "seconds should be number, got string",
    "details": {}
  }
}
HTTPerror.codeWhat it means
400VALIDATION_ERRORThe body was not a JSON object, or a declared field had the wrong type. A number field sent as a string is the usual cause.
401UNAUTHORIZEDNo token, or a token that has expired or been revoked. Mint a new one.
402INSUFFICIENT_CREDITSThe balance is below the run's minimum. Call /estimate first and compare hold_credits against /me.
404NOT_FOUNDWrong path, or a job id that does not belong to this token.
409CONFLICTAn Idempotency-Key replay whose body differs from the original request.
429RATE_LIMITEDToo many requests. Back off; do not tight-loop.
503UPSTREAM_UNAVAILABLEThe model provider is unavailable. Retry with backoff.

1. Get a token

Open /tokens.html in a browser and copy the token this app already holds - no developer console needed. A guest token is minted automatically and is enough for /me and /estimate; writing a package is metered and needs a personal token, which comes from signing in on that page.

Keep it in an environment variable rather than in source:

export SKILLSAFE_TOKEN="YOUR_TOKEN"

2. Check the session and the balance

GET /me is free. It returns only three fields: subject_type, subject_id and credits. Signed-in means subject_type == "user" - there is no username or email to test.

curl -sS -X GET "https://api.skillsafe.ai/v1/app-api/me" \
  -H "Authorization: Bearer $SKILLSAFE_TOKEN"

3. Price the run before making it

POST /estimate costs nothing, creates no job, and returns the worst-case cost. Compare hold_credits against the balance from step 2 before you submit: a 402 after the fact is avoidable. hold_credits is a reservation priced at the full output cap - the actual charge is usually far lower.

It also echoes model, model_alias and markup_bps, which is the authoritative check that a run is bound to the model you think it is. Estimate each lane separately: their prompts and caps differ, so their holds do.

curl -sS -X POST "https://api.skillsafe.ai/v1/app-api/estimate" \
  -H "Authorization: Bearer $SKILLSAFE_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "task": "check",
  "sheet": "<an SRT or WebVTT caption file; the grammar is in /llms.txt>",
  "worry": "it graded fine last time and this one will not come clean",
  "rules": "<the working rules for this lane, sent by the app>"
}'

4. Write a package

POST /run submits the job. Always send an Idempotency-Key: a network blip that replays the same request must not bill twice. A replay with the same key returns the stored result and is not charged again; a replay with the same key but a different body is a 409.

The response carries output.output (the Markdown package), charged_credits and truncated. If truncated is true the balance sat between min_credits and hold_credits and the output was cut short - render what arrived and say so rather than presenting it as complete.

curl -sS -X POST "https://api.skillsafe.ai/v1/app-api/run" \
  -H "Authorization: Bearer $SKILLSAFE_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "task": "check",
  "sheet": "<an SRT or WebVTT caption file; the grammar is in /llms.txt>",
  "worry": "it graded fine last time and this one will not come clean",
  "rules": "<the working rules for this lane, sent by the app>"
}'

5. Stream a run

POST /run-stream is the same call with a text/event-stream response. Worth knowing before you build on it: from a server or from cURL you get event: delta frames carrying the output token by token; from a browser you get event: tick heartbeats and then one event: done with the whole output. Handle both, and treat ticks as liveness rather than progress.

Frame types are job (the job id), delta ({"text": "..."}), tick ({"t": seconds}), done, and error. An idempotent replay returns plain JSON with no stream at all, so check the content type before you start reading frames.

curl -sS -N -X POST "https://api.skillsafe.ai/v1/app-api/run-stream" \
  -H "Authorization: Bearer $SKILLSAFE_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" \
  -H "Idempotency-Key: cbd-$(date +%s)" \
  -d '{
  "task": "check",
  "sheet": "<an SRT or WebVTT caption file; the grammar is in /llms.txt>",
  "worry": "it graded fine last time and this one will not come clean",
  "rules": "<the working rules for this lane, sent by the app>"
}'

6. Read the result

output.output is Markdown in the envelope this app's system prompt guarantees: every section is a level-two heading spelled exactly as listed in the lane table above, in that order; tables are GitHub pipe tables with the declared columns; prompts are in fenced blocks opened with three backticks and the word text; checklists are - [x] lines.

So parsing is a split on /^## / - but do it fence-aware, because a prompt block can legitimately contain a line starting with ##. Count the sections you got against the ones the lane declares: a short list means the run was truncated, not that the contract changed.

def sections(md):
    out, name, buf, fence = {}, None, [], False
    for line in md.split("\n"):
        if line.lstrip().startswith("```"):
            fence = not fence
        if not fence and line.startswith("## "):
            if name:
                out[name] = "\n".join(buf).strip()
            name, buf = line[3:].strip(), []
            continue
        if name:
            buf.append(line)
    if name:
        out[name] = "\n".join(buf).strip()
    return out

The artifact most callers want is the fenced text block inside ## The Sheet or ## Corrected Sheet - that is a complete sheet in the grammar above, so it can be fed straight back into another lane with nothing carried alongside it. Every other section is prose and tables meant to be read.

Rate limits and good manners