zjp1997720
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codex-cli-model-bridge
Install, audit, repair, and manage Codex custom model
Registry indexed
Install, audit, repair, and manage Codex custom model
Install, audit, repair, and manage Codex custom model
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Manage subscription-backed or local proxy models in Codex without treating Codex like WorkBuddy. Codex uses a Responses API Provider plus a model catalog; WorkBuddy uses independent JSON entries. Share CLIProxyAPI infrastructure and verified Provider facts, but keep each application's writer and state separate.
Codex selects one model_provider for a task. Model catalog entries do not carry per-model Provider routing. Preserve the Provider identity that owns the majority of indexed task history (normally openai). For normal Desktop use, the supported bridge design keeps model_provider = "openai", keeps ChatGPT subscription auth intact, and points the built-in Provider's openai_base_url at an owner-only loopback header-rewriting proxy. CLIProxyAPI then routes native GPT subscription models and verified third-party models behind one catalog without changing the task Provider identity. Keep the isolated $CODEX_HOME/cli-proxy.config.toml profile as the default path on Windows and as a fallback everywhere else.
This is transparent single-Provider routing, not per-model Provider routing. Never set the Desktop default to cli_proxy or vendor ZAI when most history belongs to openai.
When the user wants GLM-5.3 from a Coding Plan key, read glm-coding-plan.md. If Desktop already uses Codex Router on port 4202, add zai-coding there and keep the OpenAI Provider identity. Do not run npx @z_ai/coding-helper.
On Windows, start with the isolated profile. Read windows.md. Do not require Homebrew, LaunchAgents, or Codex Router.
Resolve this loaded Skill's directory as <skill-dir>. Resolve <python> as the first available of python3, py -3, and python. Use the deterministic entry point:
<python> <skill-dir>/scripts/bridge.py
Examples below use python3. Substitute <python> when that command is missing.
python3 <skill-dir>/scripts/bridge.py audit
The audit must redact secrets and verify:
~/.codex/config.toml, file permissions, and TOML validitymodel_provider, indexed task counts by Provider, SQLite integrity, and the dominant history Provideropenai_base_url, ChatGPT auth continuity, loopback health, or the isolated profile's command-backed authentication/v1/modelsCodex officially supports only the Responses wire API for custom Providers. Do not register a Chat Completions-only route and call it Codex-compatible.
If the default Provider differs from the dominant indexed-history Provider, treat history restoration as the first repair. Do not edit task rows to make the current Provider fit.
Preview:
python3 <skill-dir>/scripts/bridge.py restore-default
The preview reports one finding ID, the current config SHA-256, the exact single-file diff, and the before-state thread inventory. Apply only after the repair is authorized and the SHA is still current:
python3 <skill-dir>/scripts/bridge.py restore-default \
--expected-sha256 <approved-sha256> \
--apply
The default target is the Provider with the largest indexed task count. The command refuses a minority Provider unless --allow-minority-provider is explicit, restores a native model, removes the custom root catalog override, preserves unrelated TOML, creates a 0600 backup, and proves the task inventory digest did not change.
This is the default path on Windows. Preview and apply:
python3 <skill-dir>/scripts/bridge.py configure
python3 <skill-dir>/scripts/bridge.py configure --apply
The command does not rewrite ~/.codex/config.toml. It preserves unrelated profile sections, creates timestamped 0600 backups, installs an owner-only credential helper that reads the existing CLIProxyAPI client key without copying it, and configures ~/.codex/cli-proxy.config.toml with:
model_provider = "cli_proxy"model_catalog_json = "~/.codex/model-catalog-cli-proxy.json"[model_providers.cli_proxy] with a loopback URL and wire_api = "responses"[model_providers.cli_proxy.auth] using the local helper commandThe helper is Python by default so Windows does not need Ruby. An existing .rb helper is left in place. Do not set or change the user's default model unless they explicitly ask. Do not overwrite built-in Provider IDs. After this profile exists, use codex --profile cli-proxy.
Preview bundled models:
python3 <skill-dir>/scripts/bridge.py sync
Apply after live route verification:
python3 <skill-dir>/scripts/bridge.py sync --apply
The sync command starts with Codex's native model cache only to inherit required runtime metadata, overlays verified model manifests from <skill-dir>/models/, preserves unmanaged/manual profile entries, refuses to overwrite an unowned collision unless --adopt is explicit, backs up the target, writes atomically, and records managed IDs under ~/.config/codex-cli-model-bridge/state.json.
The picker policy lives at <skill-dir>/policies/catalog.json. IDs in hidden_native_model_ids remain in the catalog with Codex's native visibility = "hide" semantics, so existing tasks and routes keep working while those entries disappear from the model picker. Always change this canonical policy instead of hand-editing the generated catalog; every later sync reapplies it after a Codex update refreshes models_cache.json.
IDs in protected_native_model_ids must also remain under their exact native slugs. Codex App create_thread validates those IDs independently of cosmetic catalog aliases, so a managed manifest must never supersede them. Represent Fast through the service tier; do not replace gpt-5.6-sol with a *-standard picker alias. If WorkBuddy needs extra Fast/standard aliases, CLIProxyAPI oauth-model-alias must set fork: true so the native slug stays in live /v1/models. Audit fails when a listed catalog model or the current default model is missing from that live list.
Native entries copied into the bridge catalog are metadata only. In isolated-profile mode they route through cli_proxy; in Desktop-transparent mode they route through the built-in openai Provider identity and its loopback openai_base_url. The catalog itself never chooses the Provider.
Use --models <comma-separated-ids> to select a subset. Use --catalog-policy <path> only for an explicit alternate policy or an isolated test. Use --prune-managed only when the user explicitly asked to remove stale bridge-managed models. Never prune native or manual entries; hide native picker entries through the policy.
When onboarding a new model, read model-manifests.md. A manifest is metadata, not proof. Its route must appear in live /v1/models, and a real codex exec probe must pass before success is reported.
Skip this on Windows unless the user explicitly wants the normal Desktop picker and will keep a Node process running. Isolated profile is enough.
First preview the exact root config diff and history guard:
python3 <skill-dir>/scripts/bridge.py configure-desktop
After the finding-level diff is authorized, apply with the reported SHA-256:
python3 <skill-dir>/scripts/bridge.py configure-desktop \
--expected-sha256 <approved-sha256> \
--apply
The command refuses to proceed unless openai owns the majority of indexed history, auth.json still contains healthy ChatGPT tokens, both endpoints are loopback-only, and the selected default model exists in the catalog. It installs:
~/.config/codex-cli-model-bridge/transparent_proxy.mjs, owner-executable~/Library/LaunchAgents/com.zhijian.codex-cli-model-bridge-transparent-proxy.plistnode process instead of a LaunchAgent127.0.0.1:8318 that rewrites only the downstream Authorization header before forwarding to authenticated CLIProxyAPI on 127.0.0.1:8317It then keeps model_provider = "openai", sets openai_base_url = "http://127.0.0.1:8318/v1", activates the verified catalog, preserves ChatGPT login and unrelated TOML, creates a 0600 backup, and proves the task inventory digest did not change. Do not run a second CLIProxyAPI instance against the same OAuth directory; concurrent token refresh can invalidate credentials.
Codex Fast mode is a service tier on a model, not normally a second model entry. For a model whose catalog advertises the Fast tier, use:
service_tier = "fast"
or launch a one-off run with:
codex -c 'service_tier="fast"'
Codex maps fast to the priority request value. Do not create *-fast as a cosmetic catalog alias. A separate route is acceptable only when the upstream truly requires it and a live Responses probe verifies the distinct routing semantics.
After catalog sync, probe affected models:
python3 <skill-dir>/scripts/bridge.py probe --models grok-4.6,deepseek-v4-pro
The probe runs codex exec --profile cli-proxy in ephemeral, read-only mode for each model and verifies a successful final response. Use --fast only for a model that advertises Fast. Keep prompts non-sensitive and do not persist sessions.
For the normal Desktop-transparent path, probe without switching Provider identity:
python3 <skill-dir>/scripts/bridge.py probe \
--desktop \
--models grok-4.6,deepseek-v4-pro,deepseek-v4-flash,gpt-5.6-sol
With --desktop, the probe reads the active root model_catalog_json from
~/.codex/config.toml; use --catalog only as an explicit override.
Direct HTTP probes can diagnose the proxy, but they do not prove that Codex consumed the Provider and model catalog. Completion requires the Codex-level probe.
When a model can chat but Codex reports an empty or incompatible Shell payload, require an actual read-only command event:
python3 <skill-dir>/scripts/bridge.py probe \
--desktop \
--shell \
--models grok-4.6
This passes only when Codex records a successful pwd command execution; a model that merely prints or simulates a path does not pass. If the failing custom model inherited tool_mode = "code_mode_only" from an OpenAI template, set "tool_mode": null in that model manifest and resync. Do not remove code mode from native OpenAI models globally.
Codex Multi-Agent v2 uses a private Responses input item named agent_message. Native OpenAI/Codex routes accept it, while xAI and other third-party Resp
name: codex-cli-model-bridge description: Install, audit, repair, and manage Codex custom model Providers and model catalogs backed by a loopback CLIProxyAPI or Codex Router. Use whenever the user asks to add, remove, upgrade, restore, or diagnose third-party models in Codex or the Codex desktop model picker; mentions Grok, OpenCode Go, DeepSeek, Gemini, GLM Coding Plan, GLM-5.3, subscription-backed CLI models, custom model_provider, model_catalog_json, or Codex Fast mode; or wants the same local proxy models synchronized between Codex and WorkBuddy. Preserve unrelated Codex configuration and credentials. Isolated profile is the default Windows path.
--- name: codex-cli-model-bridge description: Install, audit, repair, and manage Codex custom model Providers and model catalogs backed by a loopback CLIProxyAPI or Codex Router. Use whenever the user asks to add, remove, upgrade, restore, or diagnose third-party models in Codex or the Codex desktop model picker; mentions Grok, OpenCode Go, DeepSeek, Gemini, GLM Coding Plan, GLM-5.3, subscription-backed CLI models, custom model_provider, model_catalog_json, or Codex Fast mode; or wants the same local proxy models synchronized between Codex and WorkBuddy. Preserve unrelated Codex configuration and credentials. Isolated profile is the default Windows path. --- # Codex CLI Model Bridge Manage subscription-backed or local proxy models in Codex without treating Codex like WorkBuddy. Codex uses a Responses API Provider plus a model catalog; WorkBuddy uses independent JSON entries. Share CLIProxyAPI infrastructure and verified Provider facts, but keep each application's writer and state separate. Codex selects one `model_provider` for a task. Model catalog entries do not carry per-model Provider routing. Preserve the Provider identity that owns the majority of indexed task history (normally `openai`). For normal Desktop use, the supported bridge design keeps `model_provider = "openai"`, keeps ChatGPT subscription auth intact, and points the built-in Provider's `openai_base_url` at an owner-only loopback header-rewriting proxy. CLIProxyAPI then routes native GPT subscription models and verified third-party models behind one catalog without changing the task Provider identity. Keep the isolated `$CODEX_HOME/cli-proxy.config.toml` profile as the default path on Windows and as a fallback everywhere else. This is transparent single-Provider routing, not per-model Provider routing. Never set the Desktop default to `cli_proxy` or vendor `ZAI` when most history belongs to `openai`. When the user wants GLM-5.3 from a Coding Plan key, read [glm-coding-plan.md](references/glm-coding-plan.md). If Desktop already uses Codex Router on port 4202, add `zai-coding` there and keep the OpenAI Provider identity. Do not run `npx @z_ai/coding-helper`. On Windows, start with the isolated profile. Read [windows.md](references/windows.md). Do not require Homebrew, LaunchAgents, or Codex Router. ## Resolve the Skill directory Resolve this loaded Skill's directory as `<skill-dir>`. Resolve `<python>` as the first available of `python3`, `py -3`, and `python`. Use the deterministic entry point: ```bash <python> <skill-dir>/scripts/bridge.py ``` Examples below use `python3`. Substitute `<python>` when that command is missing. ## Default workflow ### 1. Audit before mutation ```bash python3 <skill-dir>/scripts/bridge.py audit ``` The audit must redact secrets and verify: - Codex CLI version, `~/.codex/config.toml`, file permissions, and TOML validity - default `model_provider`, indexed task counts by Provider, SQLite integrity, and the dominant history Provider - active bridge mode: Desktop-transparent or isolated-profile - Desktop-transparent `openai_base_url`, ChatGPT auth continuity, loopback health, or the isolated profile's command-backed authentication - loopback-only CLIProxyAPI reachability and live `/v1/models` - the active catalog's validity, visible model IDs, and bridge ownership state - stale catalog entries, missing live routes for managed models, listed native models, or the current default model, and models present in the proxy but absent from Codex Codex officially supports only the Responses wire API for custom Providers. Do not register a Chat Completions-only route and call it Codex-compatible. If the default Provider differs from the dominant indexed-history Provider, treat history restoration as the first repair. Do not edit task rows to make the current Provider fit. ### 2. Restore the desktop default and history Preview: ```bash python3 <skill-dir>/scripts/bridge.py restore-default ``` The preview reports one finding ID, the current config SHA-256, the exact single-file diff, and the before-state thread inventory. Apply only after the repair is authorized and the SHA is still current: ```bash python3 <skill-dir>/scripts/bridge.py restore-default \ --expected-sha256 <approved-sha256> \ --apply ``` The default target is the Provider with the largest indexed task count. The command refuses a minority Provider unless `--allow-minority-provider` is explicit, restores a native model, removes the custom root catalog override, preserves unrelated TOML, creates a `0600` backup, and proves the task inventory digest did not change. ### 3. Configure or repair the isolated CLIProxyAPI profile This is the default path on Windows. Preview and apply: ```bash python3 <skill-dir>/scripts/bridge.py configure python3 <skill-dir>/scripts/bridge.py configure --apply ``` The command does not rewrite `~/.codex/config.toml`. It preserves unrelated profile sections, creates timestamped `0600` backups, installs an owner-only credential helper that reads the existing CLIProxyAPI client key without copying it, and configures `~/.codex/cli-proxy.config.toml` with: - `model_provider = "cli_proxy"` - `model_catalog_json = "~/.codex/model-catalog-cli-proxy.json"` - `[model_providers.cli_proxy]` with a loopback URL and `wire_api = "responses"` - `[model_providers.cli_proxy.auth]` using the local helper command The helper is Python by default so Windows does not need Ruby. An existing `.rb` helper is left in place. Do not set or change the user's default model unless they explicitly ask. Do not overwrite built-in Provider IDs. After this profile exists, use `codex --profile cli-proxy`. ### 4. Synchronize the profile model catalog Preview bundled models: ```bash python3 <skill-dir>/scripts/bridge.py sync ``` Apply after live route verification: ```bash python3 <skill-dir>/scripts/bridge.py sync --apply ``` The sync command starts with Codex's native model cache only to inherit required runtime metadata, overlays verified model manifests from `<skill-dir>/models/`, preserves unmanaged/manual profile entries, refuses to overwrite an unowned collision unless `--adopt` is explicit, backs up the target, writes atomically, and records managed IDs under `~/.config/codex-cli-model-bridge/state.json`. The picker policy lives at `<skill-dir>/policies/catalog.json`. IDs in `hidden_native_model_ids` remain in the catalog with Codex's native `visibility = "hide"` semantics, so existing tasks and routes keep working while those entries disappear from the model picker. Always change this canonical policy instead of hand-editing the generated catalog; every later sync reapplies it after a Codex update refreshes `models_cache.json`. IDs in `protected_native_model_ids` must also remain under their exact native slugs. Codex App `create_thread` validates those IDs independently of cosmetic catalog aliases, so a managed manifest must never `supersede` them. Represent Fast through the service tier; do not replace `gpt-5.6-sol` with a `*-standard` picker alias. If WorkBuddy needs extra Fast/standard aliases, CLIProxyAPI `oauth-model-alias` must set `fork: true` so the native slug stays in live `/v1/models`. Audit fails when a listed catalog model or the current default model is missing from that live list. Native entries copied into the bridge catalog are metadata only. In isolated-profile mode they route through `cli_proxy`; in Desktop-transparent mode they route through the built-in `openai` Provider identity and its loopback `openai_base_url`. The catalog itself never chooses the Provider. Use `--models <comma-separated-ids>` to select a subset. Use `--catalog-policy <path>` only for an explicit alternate policy or an isolated test. Use `--prune-managed` only when the user explicitly asked to remove stale bridge-managed models. Never prune native or manual entries; hide native picker entries through the policy. When onboarding a new model, read [model-manifests.md](references/model-manifests.md). A manifest is metadata, not proof. Its route must appear in live `/v1/models`, and a real `codex exec` probe must pass before success is reported. ### 5. Enable transparent Desktop coexistence Skip this on Windows unless the user explicitly wants the normal Desktop picker and will keep a Node process running. Isolated profile is enough. First preview the exact root config diff and history guard: ```bash python3 <skill-dir>/scripts/bridge.py configure-desktop ``` After the finding-level diff is authorized, apply with the reported SHA-256: ```bash python3 <skill-dir>/scripts/bridge.py configure-desktop \ --expected-sha256 <approved-sha256> \ --apply ``` The command refuses to proceed unless `openai` owns the majority of indexed history, `auth.json` still contains healthy ChatGPT tokens, both endpoints are loopback-only, and the selected default model exists in the catalog. It installs: - `~/.config/codex-cli-model-bridge/transparent_proxy.mjs`, owner-executable - on macOS, `~/Library/LaunchAgents/com.zhijian.codex-cli-model-bridge-transparent-proxy.plist` - on Windows and Linux, a detached `node` process instead of a LaunchAgent - a listener on `127.0.0.1:8318` that rewrites only the downstream Authorization header before forwarding to authenticated CLIProxyAPI on `127.0.0.1:8317` It then keeps `model_provider = "openai"`, sets `openai_base_url = "http://127.0.0.1:8318/v1"`, activates the verified catalog, preserves ChatGPT login and unrelated TOML, creates a `0600` backup, and proves the task inventory digest did not change. Do not run a second CLIProxyAPI instance against the same OAuth directory; concurrent token refresh can invalidate credentials. ### 6. Handle Fast mode correctly Codex Fast mode is a service tier on a model, not normally a second model entry. For a model whose catalog advertises the Fast tier, use: ```toml service_tier = "fast" ``` or launch a one-off run with: ```bash codex -c 'service_tier="fast"' ``` Codex maps `fast` to the priority request value. Do not create `*-fast` as a cosmetic catalog alias. A separate route is acceptable only when the upstream truly requires it and a live Responses probe verifies the distinct routing semantics. ### 7. Probe through Codex itself After catalog sync, probe affected models: ```bash python3 <skill-dir>/scripts/bridge.py probe --models grok-4.6,deepseek-v4-pro ``` The probe runs `codex exec --profile cli-proxy` in ephemeral, read-only mode for each model and verifies a successful final response. Use `--fast` only for a model that advertises Fast. Keep prompts non-sensitive and do not persist sessions. For the normal Desktop-transparent path, probe without switching Provider identity: ```bash python3 <skill-dir>/scripts/bridge.py probe \ --desktop \ --models grok-4.6,deepseek-v4-pro,deepseek-v4-flash,gpt-5.6-sol ``` With `--desktop`, the probe reads the active root `model_catalog_json` from `~/.codex/config.toml`; use `--catalog` only as an explicit override. Direct HTTP probes can diagnose the proxy, but they do not prove that Codex consumed the Provider and model catalog. Completion requires the Codex-level probe. When a model can chat but Codex reports an empty or incompatible Shell payload, require an actual read-only command event: ```bash python3 <skill-dir>/scripts/bridge.py probe \ --desktop \ --shell \ --models grok-4.6 ``` This passes only when Codex records a successful `pwd` command execution; a model that merely prints or simulates a path does not pass. If the failing custom model inherited `tool_mode = "code_mode_only"` from an OpenAI template, set `"tool_mode": null` in that model manifest and resync. Do not remove code mode from native OpenAI models globally. ### 7.1 Repair Codex Multi-Agent input for third-party models Codex Multi-Agent v2 uses a private Responses input item named `agent_message`. Native OpenAI/Codex routes accept it, while xAI and other third-party Resp
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
74/100
Strong
Trust
57/100
Do not auto-install
Audit
76/100
Needs review
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"reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"best_for": [
"security",
"agent-skill"
],
"known_risks": [
"The skill relies on external scripts (bridge.py) and referenced documentation (glm-coding-plan.md, windows.md) that are not fully included in the excerpt, but this is acceptable as SKILL.md provides sufficient guidance.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"agent_proven": {
"version": "agent-proven-v1",
"score": 0,
"tier": "unproven",
"label": "Needs first agent run",
"summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
"metrics": {
"totalOutcomes": 0,
"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
"recentSuccessRate": null,
"recentFailureRate": null,
"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 76,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"The skill relies on external scripts (bridge.py) and referenced documentation (glm-coding-plan.md, windows.md) that are not fully included in the excerpt, but this is acceptable as SKILL.md provides sufficient guidance.",
"The skill modifies Codex configuration files, which carries inherent risk, but it mitigates with previews, backups, and SHA-256 verification.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
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"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 74,
"label": "Strong"
},
"supply": {
"track": "Data, BI, and analytics",
"scenario": "Security and compliance",
"maintenance": "5d since push",
"risk": "Needs review"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"production agents without a repository review",
"The skill relies on external scripts (bridge.py) and referenced documentation (glm-coding-plan.md, windows.md) that are not fully included in the excerpt, but this is acceptable as SKILL.md provides sufficient guidance.",
"No OpenAgentSkill engagement data yet",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"The skill modifies Codex configuration files, which carries inherent risk, but it mitigates with previews, backups, and SHA-256 verification."
],
"agent_contract": {
"task_input": "Use codex-cli-model-bridge in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 65/100 Manual review",
"Audit: 76/100 Needs review",
"Safety: 32/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "zjp1997720-codex-cli-model-bridge (codex-cli-model-bridge)",
"install_command": "npx skills add zjp1997720/zhijian-skills --skill codex-cli-model-bridge",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
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"method": "POST",
"requires_resolve_event_id": true,
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"expected_outcomes": [
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"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "zjp1997720-codex-cli-model-bridge",
"task": "Use codex-cli-model-bridge in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/zjp1997720-codex-cli-model-bridge",
"api": "https://www.openagentskill.com/api/agent/skills/zjp1997720-codex-cli-model-bridge",
"audit": "https://www.openagentskill.com/skills/zjp1997720-codex-cli-model-bridge/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=zjp1997720-codex-cli-model-bridge&task=Use%20codex-cli-model-bridge%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20codex-cli-model-bridge%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20codex-cli-model-bridge%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/zjp1997720-codex-cli-model-bridge/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/zjp1997720-codex-cli-model-bridge"
}
}Listing source
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