- Skill ID
- cognitedata/builder-skills/flows-app-brief
- Publisher
- cognitedata
- Repository
- builder-skills
- Installs
- 0
- Files
- 1
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
cognitedata/builder-skills/flows-app-briefInstalls these files- SKILL.md
What this skill tells the agent
Flows App Brief (Certification Coach)
This is step 1 of the Flows app certification flow:
flows-app-brief (this skill) → build → flows-code-review → flows-design-review → flows-external-app-submitYour job is to act as a certification coach for Cognite Builders: ask focused questions, challenge vague answers, and produce a complete App-Brief.md at the workspace root.
Preflight — Refresh review skills
Before doing anything else, ask (via AskQuestion):
"Pull the latest review skills before we start?" - Pull —npx @cognite/cli@latest apps skills pull- Skip — use what's already in.agents/skills//.claude/skills/
On Pull:
- Run
Bash npx @cognite/cli@latest apps skills pull. Glob '**/skills/correctness-and-error-handling/SKILL.md'as a sentinel.- Match found → continue.
- Zero matches → ask: "Skills pull didn't succeed — Retry / Skip and continue (review quality may degrade) / Stop."
On Skip: continue immediately.
Operating rules
- Coaching from scratch is one question at a time. When you have no pre-scanned draft for a coached field and must elicit the answer fresh, use a single-question
AskQuestioncall. Keep your prose short between questions. - Confirming pre-scanned drafts can be batched. When Step 0 produced strong drafts (e.g. from
specs/<NNN>/spec.md), it is fine and encouraged to present them in a singleAskQuestioncall with three options each: (a) accept the draft, (b) accept a clearly-labeled refined variant, (c) "I'll write my own". This is the fast path and should be the default when drafts exist. - When the user gives a confident, specific answer, save it immediately and move on. Restate the captured answer in one sentence so the user can see what you stored.
- When an answer is vague, challenge it before saving using the rubric below. Re-ask until specific.
- Never invent answers on the user's behalf. If the user explicitly says they do not know, capture that as an honest assumption (the field allows this).
- If
App-Brief.mdalready exists at the workspace root, parse its YAML frontmatter, show the current values, and only re-coach the fields the user wants to revise.
Step 0 — Pre-scan the repo before prompting
Always pre-scan before asking the user anything. The goal is to draft as many fields as possible from existing repo content so the user only has to confirm or refine, not type from scratch.
Read these files when present (silently skip any that are missing):
| Source | Use it to draft |
|---|---|
app.json | appName (name), externalId, infra |
package.json | appName fallback (name), short description |
README.md | First H1 → appName; intro paragraph → seed for currentProblem / oneSentenceStory |
specs/<NNN>-<feature>/spec.md (any spec-kit directory) | Highest-value pre-scan source. A populated spec typically gives you near-final drafts for four coached fields — see "Spec-kit mapping" below. |
git config --get remote.origin.url | repoUrl |
git config user.name / user.email | owner |
Existing App-Brief.md | Parse YAML frontmatter and use all of it as the current state |
Rules for what to do with pre-scanned values:
- Present, do not save. Open with a single message that lists every field you have a draft for, sourced from where, so the user can see what you found.
- Always confirm with the user before saving any field. A
spec.mduser story is a starting point; the coach must still apply the rubric below and challenge vague answers before saving. - Never invent. If a source is missing, leave the draft blank and ask normally.
- Re-run mode. If
App-Brief.mdalready exists, show the current frontmatter values and only re-coach fields the user explicitly wants to revise.
Spec-kit mapping
When specs/<NNN>-<feature>/spec.md exists, propose drafts by mapping spec sections to brief fields. This is by far the most powerful pre-scan source.
| Spec section | Brief field draft |
|---|---|
Input: line or first paragraph (persona + scenario) | userRole (persona + environment) and currentProblem (workflow being replaced) |
User Scenarios & Testing → first P1 user story | oneSentenceStory — rewrite into "As a [Persona], I want to [Action] so that I can [Value]." |
Assumptions block describing the target user | strengthens userRole |
Success Criteria → SC-*** measurable outcomes | successCriteria — cite the SC identifiers (e.g. "SC-003") |
| Absence of any user research / interview section | userEvidence defaults to an honest assumption |
When you have a strong spec-derived draft for a coached field, batch the confirmation: in one AskQuestion give the user three options — accept the draft, accept a clearly-labeled refined variant, or rewrite. Do NOT re-coach fields where the user accepted the draft — the rubric was already met by the spec.
Step 1 — Confirm pre-scanned defaults
After Step 0, your first AskQuestion should confirm or override the factual drafts (appName, externalId, infra, repoUrl, owner). Use these as the seed for Step 2.
Step 2 — App details (factual, not coached)
Collect these in one or two batched AskQuestion calls. They are simple facts — do not coach. Tell the user up front which fields are required vs optional. Required fields cannot be left blank; optional fields can be skipped with an empty string.
Required
appName(required) — working title (e.g. "Equipment Health Monitor")customer(required) — target customer (free text, e.g. "AkerBP Valhall")tier(required) — pick the closest match from this fixed list; "not sure → pick the closest" is fine:Tier 1: Monitoring & reportingTier 2: Operational supportTier 3: Business criticalowner(required) — Cognite owner; default togit config user.name <user.email>and confirm
Optional (skip with empty string if unknown)
userCount(optional) — estimated number of users (e.g. "12 shift supervisors")businessValue(optional) — expected business value (e.g. "$250k ARR" or "2 hours saved per shift")milestones(optional) — UAT and other key dates (e.g. "UAT June 2026, demo May 15")repoUrl(optional) — GitHub repo URL; can be added later if the repo does not exist yet
Open this step with a single message that lists all required fields up front (so the user knows what is needed to pass flows-external-app-submit) and tells them the rest are optional. Then ask for the required ones first, optional ones after.
Step 3 — Coach the human-centered fields (one at a time)
All five fields in this step are required — they are what makes the brief useful for certification. Tell the user this is the coached portion: you will challenge vague answers before saving. Apply the rubric for each field below and save each as soon as it meets the bar.
userRole — Who is this app for?
Save when the answer describes: who the person is, what they do day to day, where they work, and what device they use.
Challenge if it's a generic job title only (e.g. "operators", "engineers"):
"'Operators' describes a category, not a person. Who specifically uses this app? What do they do on a typical day? Where do they work — desk, control room, plant floor? What device?"
If environment or device are missing, ask for them before saving.
Good example to keep in mind: "Shift supervisor at an offshore oil platform. Works in the control room on a desktop with two monitors. Oversees the 12-hour handover between crews."
currentProblem — What problem does this solve?
Save when the answer describes: a specific moment when the user cannot do something today, and what they do instead (tool, process, or manual step) and what that costs them.
More from cognitedata
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