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[aw] Matt Pocock Skills Reviewer: has an unavailable configured model

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Bewertung

Schwierigkeit
2/5
Geschätzter Aufwand
1-3 Stunden
Anfängerfreundlichkeit
48/100
Issue-Typ
Bug
Klarheit
Größtenteils klar
Aktivitätsstatus
Aktiv
Tech-Stack
github-actions, typescript
Bereich
ai, ci-cd

Rechercherichtung

Read the workflow source at .github/workflows/mattpocock-skills-reviewer.md and inspect run #39146332015 for the rejected model and request context. Check the workflow’s configured model against the catalog available to the agentic-workflows integration, then select an available model if the workflow setting is the cause. Done means the workflow uses a supported model and the run no longer fails with Model Not Supported.

Vom Indexierungsmodell aus dem Issue-Text verfasst.

Beschreibung

agentic-workflows
What happened

Matt Pocock Skills Reviewer has an unavailable configured model.

View run #39146332015 · Workflow source · Previous failures

Branch: rig-header/c00c90c-d894a8eb536290ad

Next step

Review the failure details below for a targeted fix, or assign this issue to a coding agent to investigate.

Failure details

[!WARNING]
Model Not Supported: The requested model was rejected by the provider for this request.

This may be an invalid model name, a policy or access restriction for this token/repository, or a change in provider availability. Retrying the same session will not resolve a model that remains unavailable.

Copilot also filters models by integration. A model enabled by organization policy and available in the standalone CLI can still be unavailable to the agentic-workflows integration. In particular, Gemini models have been absent from that integration's catalog. The bundled gh-aw model catalog is not proof of live access. Select a model offered to this workflow; enabling a missing model for agentic-workflows requires Copilot API support. Do not change the integration ID to bypass access policy.

How to fix this

Specify a valid model for the selected engine in the workflow frontmatter:

---
engine: copilot
model: gpt-5-mini
---

To find valid models, check your engine/provider documentation (for Copilot see supported models) and the model catalog returned for the failing token/context.

If the model previously worked with the same workflow and token type, compare the successful and failing runs' model catalogs and request context. If the catalog changed, share sanitized timestamps, repository/organization context, and Copilot service request IDs with the Copilot model-access support team. Do not share tokens.

If the error text is No model available. Check policy enablement under GitHub Settings > Copilot, the model is not disabled in the workflow but by Copilot policy. Enable the model under GitHub Settings > Copilot > Policies for the org/repo, or pick a model that is already enabled. This can also be triggered by a subagent (task tool) dispatch requesting a model that the policy does not allow, even when the main agent's model is enabled.

[!WARNING]
Engine Failure: The copilot engine terminated unexpectedly.

Error details: 
Driver exit code: 1

Review the workflow run logs for diagnostics. Raw agent output is omitted because it may contain secrets.

Copy a debugging prompt

Paste this into your coding agent:

Debug the agentic workflow "mattpocock-skills-reviewer" failure.
Follow https://raw.githubusercontent.com/github/gh-aw/main/debug.md
Failed run: https://github.com/githubnext/rig/actions/runs/39146332015

If the repository includes the agentic-workflows skill, load it before investigating.

Manage failure notifications

To stop creating failure issues for this workflow, set this in its frontmatter and recompile:

safe-outputs:
  report-failure-as-issue: false

This only disables issue reporting; it does not fix the failure.

Generated from Matt Pocock Skills Reviewer · ◷

  • expires on Oct 25, 2026, 4:47 PM UTC
Vorherrschende Sprache
TypeScript
Sterne
14
Forks
0
Ø Merge
9 Std. 27 Min.
Gemergte PRs (30 T.)
34

Entwicklungsumgebung

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Erste Schritte

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  2. Schreiben Sie ins Issue, dass Sie es übernehmen — das erspart doppelte Arbeit.
  3. Forken Sie das Repository und arbeiten Sie in einem Branch.
  4. Öffnen Sie einen Pull Request, der die Issue-Nummer nennt.

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