Fluid Desk / API
Get a token

Driving Fluid 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
planTurn a type brief into a scale that holdsbrief, knownSummary, The Sheet, The Numbers, Reasoning, Next Step
auditWhat this scale measures, and where it stops being a scalesheet, worrySummary, Verdict, Findings, Corrected Sheet, Next Step
orderWhich pairs cross, and wheresheetSummary, Every Step As A Line, Where They Cross, What Each Pair Costs, Next Step
readerHow much of a font-size request each step answerssheetSummary, Every Step At Every Width, What The Reader Gets, The Bounds, Next Step
decideDecide what changes: a number, the shape, or the ideasheet, fixedSummary, One Number Fixes, Only A Rewrite Fixes, Nothing Fixes, Next Step

Only task and the lane's own required fields are mandatory: sheet on audit, order, reader and decide; brief on plan. Every field is a string - there are no number fields on this app. sheet is the scale sheet itself: a header of KEY: value lines and a SCALE: block, one step per line.

A step is a clamp(min, A rem + B vw, max), or the same three parts written as min= preferred= max=, or a fixed size=. The preferred term must be a rem term plus a viewport term; a nested calc(), a min()/max() inside the clamp or an unresolved var() is reported rather than guessed at.

The order you write the steps in is the hierarchy this app checks. Largest first. Every ordered pair is compared rather than only adjacent ones, because a step that overtakes one two rows above it is the same bug.

ROOT multiplies every rem term, so it sets the intercept of every line and moves every crossover viewport at once. Assumed 16. VIEWPORTS is the list you actually test at and it decides whether an inversion is visible: an inverted band is bounded on both sides, so it can sit entirely between two of your breakpoints and pass on both. RATIO is the ratio you intended, if any - without it the measured ratios are reported and nothing is compared to an intention.

A bound carries its unit and it matters as much as the preferred term's. A clamp spends most of its range PINNED, and when it is pinned the bound is what is being used. clamp(40px, …, 72px) and clamp(2.5rem, …, 4.5rem) render identically at a 16px root and are completely different for a reader who enlarges text.

cqw and cqi work in place of vw: the arithmetic is identical and the variable is the query container rather than the window, so every crossover in the answer becomes a crossover CONTAINER width.

Anything the reader cannot place is listed as a problem rather than skipped, and so is a step named twice. A missing step is a comparison that was never made.

A size is reported in pixels to one or two places, a viewport in whole pixels, a ratio as a number to three places, a share as a percentage, and a slope as pixels per hundred pixels of viewport. The engine works in linear functions: it has not parsed CSS at large or laid out any text.

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 audit, order and reader lanes rather than shorten them - a findings table, a corrected sheet, or a row per step with its crossovers and its zoom response, 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": "audit",
  "sheet": "<ROOT, VIEWPORTS, RATIO and a SCALE block of clamp() steps; 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": "audit",
  "sheet": "<ROOT, VIEWPORTS, RATIO and a SCALE block of clamp() steps; 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": "audit",
  "sheet": "<ROOT, VIEWPORTS, RATIO and a SCALE block of clamp() steps; 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