gstack/docs/model-policy.md

Model policy

Use a frontier model to challenge a plan, then a smart model to build it. These are curated recommendations, not a claim that one model wins every task. User choices take precedence, and a recommendation never switches the running session or edits the host's native configuration.

Tier	Default job	Anthropic	OpenAI
frontier	Independent plan review	claude-fable-5-1	gpt-6-astra
smart	Implementation recommendation	claude-opus-5-5	gpt-6.1-sol

Inspect and change your policy

This quickstart assumes gstack is installed. It uses no network, credentials or paid model calls. Run it from the project's repository root in Bash, Git Bash or MSYS: automated outside reviews use that directory. Inspection from a subdirectory reflects that subdirectory's native settings and may differ. If your install is vendored or elsewhere, set GSTACK_ROOT to that installed gstack runtime instead.

if [ -x "$HOME/.claude/skills/gstack/bin/gstack-models" ]; then
  GSTACK_ROOT="$HOME/.claude/skills/gstack"
elif [ -x "${CODEX_HOME:-$HOME/.codex}/skills/gstack/bin/gstack-models" ]; then
  GSTACK_ROOT="${CODEX_HOME:-$HOME/.codex}/skills/gstack"
else
  printf '%s\n' 'Upgrade the gstack install for your host, then rerun this quickstart.' >&2
  exit 1
fi
"$GSTACK_ROOT/bin/gstack-models" list
"$GSTACK_ROOT/bin/gstack-models" resolve --role plan-review --provider openai

The output explains the selected model and which setting won. It also shows catalog verification metadata. Offline selection does not prove that a CLI is installed, that your account can use the model, or that a vendor's current catalog has been checked.

Choose the less expensive review tier, then inspect the effective result:

"$GSTACK_ROOT/bin/gstack-config" set plan_review_tier smart
"$GSTACK_ROOT/bin/gstack-models" resolve --role plan-review --provider openai

Pin a provider's model within a tier, or reset a pin to the shipped default:

"$GSTACK_ROOT/bin/gstack-config" set model_frontier_openai gpt-6-astra
"$GSTACK_ROOT/bin/gstack-config" set model_frontier_claude claude-fable-5-1
"$GSTACK_ROOT/bin/gstack-config" unset model_frontier_openai
"$GSTACK_ROOT/bin/gstack-config" unset plan_review_tier

To keep using native CLI model settings for plan reviews:

"$GSTACK_ROOT/bin/gstack-config" set plan_review_tier host

Settings apply to the next invocation without rerendering skills. An invocation already being checked keeps its selected model through execution. Configuration uses the existing gstack state root, not a new per-repository settings file. Invalid writes are rejected and leave the previous value intact; use unset, not an empty value, to restore a default.

A malformed known setting (for example plan_review_tier = smart) stops selection rather than acting as an absent key. Correct or remove the named line in the reported config file; unset only removes correctly spelled key: records.

Codex plan-review readiness and dispatch share one deadline. Readiness is budgeted half the remaining time, with the existing termination grace. A slow preflight leaves less time for the review instead of extending the caller's limit. An expired deadline starts no new command and means missing review coverage, not a completed review.

Settings and precedence

Key	Default	Accepted value
plan_review_tier	frontier	frontier, smart, host
implementation_tier	smart	frontier, smart
model_frontier_claude	Catalog's Anthropic frontier model	Explicit Anthropic model identifier
model_frontier_openai	Catalog's OpenAI frontier model	Explicit OpenAI model identifier
model_smart_claude	Catalog's Anthropic smart model	Explicit Anthropic model identifier
model_smart_openai	Catalog's OpenAI smart model	Explicit OpenAI model identifier
agent_model	unset (host)	host, a family or <provider>/<model>; see your model preference

For plan reviews, a model named for that invocation wins over GSTACK_CLAUDE_MODEL or GSTACK_CODEX_MODEL. That environment override wins over the selected tier's configured model, which wins over the shipped catalog. Generic coding-model settings do not silently select a cheaper plan reviewer; choose host mode when that is the behavior you want.

In host mode, Codex keeps its existing native-config resolution, including CODEX_HOME, active profiles and the exec versus native review distinction. Claude can delegate model selection to its CLI without forcing --model; the inspector reports that delegation rather than inventing an exact model ID. Actual returned model usage remains separate from the requested model.

Implementation recommendations use a model explicitly requested for that job, then the configured implementation tier and its provider override, then the catalog. Outside-review environment overrides do not change implementation recommendations. Known Claude Code and Codex hosts recommend their own provider; an unknown or multi-provider host can show both choices.

Custom endpoints and partner platforms need an explicitly compatible model or host mode. Gstack does not guess that public API IDs work with Bedrock, Vertex, Foundry or a custom Codex provider. A model identifier passing syntax validation is not proof of account availability. See provider recovery.

Detection is conservative, not a replacement for the CLI's settings engine. Claude checks environment, user settings, project/shared/local settings, local settings at the repository and worktree's main checkout, and local managed files. Codex checks environment, user/active-profile settings and system configuration; project-local Codex config is not inspected. Any detected local custom-route signal requires an explicit model, even if native trust or precedence rules might suppress it. Server-managed or MDM policy and extra native CLI flags are not visible to this check; --help and JSON output list these limits. Use an explicit compatible model for those cases, or host mode for plan reviews. Implementation recommendations require an explicit implementation model rather than delegating to a hidden native choice.

Your model preference

gstack takes no position for or against any model. If you prefer one, gstack remembers it and uses it whenever it picks a model for a working agent: a lane, a subagent or a reviewer. When it is unset, every agent keeps its own model.

"$GSTACK_ROOT/bin/gstack-config" set agent_model opus
"$GSTACK_ROOT/bin/gstack-models" resolve --role agent

Value	Meaning
host (the default)	keep the running agent's own model and leave the model field out
a family: opus, fable, sonnet, haiku, astra, sol, luna, terra	today's model in that family (opus is anthropic/claude-opus-5-5); it moves when gstack's family table moves
<provider>/<model>	that exact model, for example anthropic/claude-opus-5-5

The first match wins: a request for one job (resolve --role agent --request sol, never saved), then agent_model in this machine's config.yaml, then agent_model in the file GSTACK_PREFERENCES_FILE names, then host. The output names the winner and shows both stored values; --json prints the provider, model, provider-qualified id and source.

Two cases ignore the preference on purpose: a cross-family outside voice (the plan-review role picks the other provider) and a model under test in an eval (eval-arms and /eval-plan arms).

A value gstack can't use stops only the agent role, naming the file and the fix. plan-review, implementation and eval-arms keep resolving, and gstack-models list shows the problem in its agent block.

On Capy. Each cloud machine starts fresh, so config.yaml does not carry over. Keep the preference in preferences.yaml next to the gstack skill's SKILL.md in your Drive: one agent_model: <value> line, with # comments allowed (any other key is ignored with a warning). The session start puts the file's path in the launch file as GSTACK_PREFERENCES_FILE and gstack reads it where it is: nothing is copied, and --check never writes the skill folder. Agents write that file only when you ask them to remember a preference. To change or clear it for good, edit or delete that line; gstack-config unset agent_model clears this machine only. Copying a newer capy/skill/gstack/ over the skill folder leaves preferences.yaml alone, because the shipped folder has no such file.

Reasoning effort

Codex calls carry a model_reasoning_effort. It follows the same shape as the model: a per-request flag on /codex, a per-run environment override, then a stored setting, then the default each gate ships with.

Order	Source	Set it with
0	/codex ... --effort <level> (this request only; /codex alone)	/codex review --effort medium
1	GSTACK_CODEX_EFFORT (this shell, this run)	export GSTACK_CODEX_EFFORT=max
2	codex_effort in config.yaml	gstack-config set codex_effort low (unset restores the defaults)
3	the gate's default	nothing: high for reviews, plan reviews and /codex review and challenge; medium for /codex consult and design direction

Levels: minimal, low, medium, high, xhigh, max, ultra. Support is model-dependent, so an unsupported level for your model fails at request time through the usual model error. An invalid value never reaches Codex: gstack-config set rejects it, and gstack-codex-probe run-with-timeout --effort <default> (which resolves the order above and appends the flag) stops the call before anything is sent, naming the source to fix. gstack-codex-probe check-effort <default> prints the same resolution without a call.

For one request, /codex review --effort medium (or --effort=<level>; --xhigh is shorthand for --effort xhigh) outranks the other three, including an exported GSTACK_CODEX_EFFORT, and changes nothing that persists. The flag counts only directly after /codex or its mode word, among the leading flags (-m/--model, --role, in any order) and before the prompt text; a later --effort is part of the prompt, and /codex says so. A Consult follow-up resolves its effort afresh. /codex passes the level to the probe as --request-effort, so an unknown level stops before any Codex call, naming the flag.

/spec's quality gate keeps medium inside its 120-second budget whatever you set, because a higher effort would spend the time the gate is sized for. gstack-models list prints the effective effort and where it came from (--json: codexEffort). The Claude Code runner takes no effort setting.

Review effort tiers

Every reviewer that can block a ship runs at full effort on the lane's model: the core checklist, each specialist, the Red Team, the native adversarial pass and the fix-check. gstack-review-plan names each dispatch's effort, and host for the outside voice (its provider and codex_effort above). Only work that cannot block runs light, the same model at a medium or low setting and never a cheaper model family: the coverage, plan-completion and docs audits, decision-trail reviewers and watchers (AUDIT_EFFORT: in the plan's output). A paid replay showed why reviewers stay at full: a medium-effort fix-check missed a fail-open in gate code that the same check at full effort caught. A host that cannot set a subagent's effort runs it at its default. gstack-config set review_effort_tiers off (default on) runs the audits and watchers at the host default too (review-policy.md).

Which workflows use the tiers?

The plan-review role applies to autoplan's CEO, design, DX and engineering outside voices; standalone CEO, engineering, developer-experience and design plan reviews; and the spec quality gate. Existing reasoning effort, access restrictions, timeouts, review gates and codex_reviews consent remain in force.

The host selects the opposing provider independently from the tier. Claude Code uses the Codex wrapper; Codex uses the Claude Code wrapper. A model's name does not establish which harness is running.

Manual opposing-model wrappers can opt into --role plan-review. Ordinary code reviews, live design reviews, design consultation, office-hours second opinions and documentation reviews retain their existing model-selection behavior. Historical no-role defaults, eval actors/judges, cheap helper models and setup-time behavioral overlays do not follow this catalog.

Before the first affected paid plan review after upgrade, gstack explains the independent frontier default and the winning source, with smart and host alternatives. Inspection does not mark that notice as seen. Disabled outside reviews stay disabled; an unavailable model is reported, never silently replaced.

First completed outside review

Policy setup and a completed review are different milestones. Install and authenticate the opposing CLI using the host setup instructions before invoking a paid review. From Claude Code, use /codex consult --role plan-review. From Codex, use /claude-code consult --role plan-review (or its installed namespaced name). Ask it to challenge the plan and supply the full plan text. Consult is prompt-driven and works before there is a repository diff; Challenge mode is for reviewing code changes.

Confirm the announced selection, the provider's actual result and the coverage status. A resolved model, a successful availability probe or a native fallback is not a completed outside review. An unusable explicit choice needs a repair at the source named in the error; changing a lower-priority tier cannot override an environment pin. See selection recovery.

Machine-readable inspection

gstack-models with no arguments is list. Both list and resolve accept --json. Plan-review resolution requires --provider anthropic|openai; implementation resolution can omit the provider to return both recommendations. Agent resolution (--role agent) takes no --provider; its JSON carries an agent object instead of selections, and list --json adds the same top-level agent key.

"$GSTACK_ROOT/bin/gstack-models" resolve --role plan-review --provider anthropic --json
"$GSTACK_ROOT/bin/gstack-models" resolve --role implementation --json

JSON output is one document with schemaVersion: 1, a selections array and structured errors on failure. Human diagnostics go to stderr. Exit 0 means valid resolution, including host delegation; 1 means configuration or selection failure; 2 means invalid usage. Neither inspection nor resolution writes notice markers, probes a model or fetches vendor sources.

Keeping shipped recommendations current

The model-policy freshness workflow runs weekly and supports manual dispatch. It checks official Anthropic and OpenAI recommendation and deprecation sources, without provider credentials or paid model calls. Normal PR checks use offline fixtures; a vendor outage does not make unrelated PRs fail.

In GitHub Actions, select Model-policy freshness to run it manually. From a gstack checkout, bun scripts/model-policy-freshness.ts performs the same public source check without changing an issue; unlike gstack-models, it uses the network. The workflow's guarded --publish path owns issue updates. Its JSON report and job summary retain the source evidence.

The report's primary status distinguishes current, update-candidate and unknown/source-unavailable. Its separate evidenceStatus marks retained successful evidence stale after thirty days; a failed fetch remains unknown in the headline even when that retained evidence is stale. Newer or newly recommended models are candidates, not automatic replacements. A support promise such as “not sooner than” is not a scheduled retirement, and an incidental model mention is not a new default. This evidence covers public API recommendations, not partner-platform entitlement.

One Actions-bot-owned tracking issue preserves the successful check's catalog/source/ parser identity and any unresolved lifecycle findings. Healthy state remains readable when the issue is closed. A failed or partial check cannot erase a known retirement, borrow a success from an older catalog, or pretend its evidence is fresh. A catalog rollback reopens historical findings for a model put back in use. Only current default-branch runs may update that issue; copied markers in user-owned issues are not trusted state.

A documented retain decision preserves the current default, but does not make a vendor mismatch current: the advisory issue stays open, or reopens, while that candidate remains. Record retain rationales in issue comments, which the workflow never edits, rather than racing a scheduled update to the issue body.

Thirty-day staleness means an absence of trustworthy freshness evidence, not that a model was released thirty days ago. An unchanged recommendation does not require a date-only catalog commit or a monthly paid benchmark. The recorded next-check due date helps maintenance, but a workflow that never runs cannot alert on its own absence.

Maintainer update checklist

1. Check the latest freshness run and its due date. Resolve missing, corrupt or source-unavailable evidence before declaring the catalog current.
2. Verify the official recommendation and lifecycle evidence. Triage announced retirement/removal within one working day, stale/unavailable evidence within seven days and discretionary update candidates within thirty days.
3. Open a catalog change with the source URLs, exact model IDs and qualification evidence, or record why the current default should remain. Never edit users' personal pins or silently substitute another provider.
4. Confirm CLI model/profile support and tool/reasoning compatibility. Keep the pinned test runtimes and benchmark rulers stable. A required CLI upgrade is a separate reviewed change, not an automatic side effect of discovery.
5. Predeclare any budgeted baseline/candidate comparison using existing eval machinery, fixed prompts/panels and unchanged thresholds. Keep every attempt, measure useful and unsupported findings, latency and available usage, and do not treat vendor marketing or a creation timestamp as quality evidence.
6. Pass the affected free checks and selected workflow evaluations, review the change, and ship it through the normal release process. Only unpinned role-bearing defaults follow the new shipped catalog.

The workflow does not open an automatic model-upgrade PR, merge changes, switch active sessions, alter benchmark models or schedule paid evaluations.