Reporting Scenario bugs and change requests

SkillCommunication

Helps your agent turn errors or problems into clear bug reports or feature requests for the Scenario team.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Reporting Scenario bugs and change requests skill

About this skill

Use when a Scenario user wants to report a bug or request a change, on the public tracker at github.com/scenario-labs/skills or by email to support@scenario.com: turning a failed generation, MCP tool error, or unclear skill into a reproducible report, collecting trace ids with diagnostics_run, strip

What this skill tells your AI

The instructions your AI receives, as published by scenario-labs/skills in skills/scenario-report/SKILL.md and read by ahel’s review.

Overview

Public tracker: https://github.com/scenario-labs/skills/issues, forms bug.yml and change-request.yml. Offer support@scenario.com as the private alternative, and let the user pick.

Two constraints. A public issue is permanent, so redact first and post only after the user approves the exact text. The reader has no access to the user's account, so the report must carry everything needed to reproduce from outside.

Connection and the core generation loop: see the scenario skill in this repo. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Account, billing, credit, quota, sign-in, and security problems never go in a public issue: send those to support@scenario.com or in-app support at app.scenario.com.

Quick reference

StepDo
Classifybug or change request, one problem each
Gatherfailing call, verbatim error, diagnostics_run from that session
Redactstrip identity and secrets; ask before any team or project id
Dedupesearch open and closed issues, comment on a match
Draftfill every field, label guesses as guesses
Approveshow the final text, post only on a yes
PostGitHub tool, prefilled URL, or email

Redact before drafting

Never include API keys or tokens, signed URLs from asset_download (the URL is a credential), account emails or names, local paths containing a username, or unreleased art, prompts, and model names the user has not agreed to publish.

Team and project ids identify the account, so strip them by default. Ask for one only when it changes the diagnosis (a Forbidden or scope error, a model only one team can see): quote the id you would post, and treat anything short of a yes as no. Email is the better home for it.

diagnostics_run is the best evidence and the biggest leak: self_test.detail can enumerate every team and project the user belongs to, and tenant can encode their account. Copy only version, endpoint, auth_type, self_test.layer, self_test.latencyMs, generated_at, and the ids under confirmed_trace_ids and candidate_trace_ids. Nothing else from that report.

Job, asset, and model ids match the report to a server-side request: include them with the user's agreement. Signed download URLs, never.

Make it reproducible

Name the model id and whether search finds it with public=true, the exact parameters sent to model_run, the verbatim error, the UTC timestamp, and at least one trace id. Substitute a short public prompt for a confidential one and say so. Say when a model is team-private: the reader reproduces against a public equivalent.

Worked example: a texture upscale that never finishes

  1. diagnostics_run in the failing session, keeping only the fields listed above and the trace ids.
  2. Re-run the failing call once: arguments, verbatim response, UTC time. Check with search whether the upscaler is public, so the reader can run it too, then search the tracker, open and closed, for jobs_wait pending.
  3. Draft. Title: jobs_wait keeps returning the same texture upscale job in pending_job_ids for 20 minutes. Steps: the model_schema_get, model_run, and repeated jobs_wait calls with their arguments. Expected: the job completes or fails. Actual: status stays in_progress, pending_job_ids unchanged. Evidence: job id, trace ids, timestamp.
  4. Show the user the full text, and post only on a yes.

Posting

  • With GitHub tooling: search repo:scenario-labs/skills for duplicates, then create the issue with the label bug or enhancement, mirroring the form headings in the body.
  • Browser only: build https://github.com/scenario-labs/skills/issues/new?template=bug.yml&summary=...&repro=..., percent-encoding each value. Parameter names are the form field ids: summary, area, repro, expected, actual, evidence, env, frequency; change requests switch to template=change-request.yml and use problem, area, today, proposal, impact (a param matching no field id on the selected form is dropped silently). The user ticks the confirmation boxes and submits. Past about 8000 characters the URL returns 414: drop evidence.
  • By email: support@scenario.com, subject [bug] <title> or [change request] <title>, body carrying those same fields as labeled lines. Whatever was withheld from a public issue (team id, project id, account email) travels safely here.

Common mistakes

  • Pasting the whole diagnostics_run output, which leaks the team and project list.
  • Publishing a team or project id nobody asked for, or asking for one the diagnosis does not need.
  • A title like "generation is broken": name the surface, the symptom, and the condition.
  • Three problems in one issue: each needs its own reproduction and its own close.
  • Reporting the hypothesis ("the API is down") instead of the observation. Observation first, hypothesis last and marked as one.

Signals

GitHub stars
681
Forks
82
Last commit
Sep 2026
Advanced
Item type
skill
Key
scenario-report
Source
github.com/scenario-labs/skills