hermes: align classifier examples with capability routes
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@ -793,7 +793,7 @@ data:
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3. Route image creation and editing before general provider preference.
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When image-generation/edit tools are available and the user asks to create,
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transform, restore, colorize, or continue editing an image: select
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codex_auto_medium. The image tool—not the conversational model—honors the
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codex_auto_balanced_medium. The image tool—not the conversational model—honors the
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user's local, OpenAI/hosted, or AUTO image-backend choice. Do not select a
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local Qwen or Claude target for an image-tool boundary: local Qwen cannot
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reliably carry the full Hermes tool context, and Anthropic supplies the
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@ -809,7 +809,7 @@ data:
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the other hosted provider at the same floor.
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5. Choose the provider AUTO target at the exact effort floor. AUTO resolves
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an account-visible economy, balanced, or advanced general-purpose model
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an account-visible economy, balanced, advanced, or frontier general-purpose model
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from provider metadata. It uses comparable provider cost data only to break
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ties between adequate models. Re-evaluate every boundary and resolve
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"continue" or "do it" from recent context. The manual local route remains
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@ -880,7 +880,7 @@ data:
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3. Route image creation and editing before general provider preference.
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When image-generation/edit tools are available and the user asks to create,
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transform, restore, colorize, or continue editing an image: select
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codex_auto_medium. The image tool—not the conversational model—honors the
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codex_auto_balanced_medium. The image tool—not the conversational model—honors the
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user's local, OpenAI/hosted, or AUTO image-backend choice. Do not select a
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local Qwen or Claude target for an image-tool boundary: local Qwen cannot
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reliably carry the full Hermes tool context, and Anthropic supplies the
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@ -896,7 +896,7 @@ data:
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the other hosted provider at the same floor.
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5. Choose the provider AUTO target at the exact effort floor. AUTO resolves
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an account-visible economy, balanced, or advanced general-purpose model
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an account-visible economy, balanced, advanced, or frontier general-purpose model
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from provider metadata. It uses comparable provider cost data only to break
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ties between adequate models. Re-evaluate every boundary and resolve
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"continue" or "do it" from recent context. The manual local route remains
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@ -973,7 +973,7 @@ data:
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other provider at the same floor.
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4. Choose a provider AUTO target at the exact effort floor. AUTO resolves
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an account-visible balanced or advanced general-purpose model from provider
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an account-visible balanced, advanced, or frontier general-purpose model from provider
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metadata; economy targets are intentionally unavailable on this route.
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Re-evaluate every boundary and resolve "continue" or "do it" from recent
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context.
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@ -1043,7 +1043,7 @@ data:
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other provider at the same floor.
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4. Choose a provider AUTO target at high or xhigh only. AUTO resolves an
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account-visible advanced general-purpose model from provider metadata.
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account-visible advanced or frontier general-purpose model from provider metadata.
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Medium and low targets are intentionally unavailable on this route.
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Re-evaluate every boundary and resolve "continue" or "do it" from recent
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context.
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@ -1121,21 +1121,21 @@ data:
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A final independent review is Claude; implementing review findings is Codex.
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4. Choose an available provider AUTO target at the exact effort floor.
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AUTO resolves the current account-visible economy, balanced, or advanced
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AUTO resolves the current account-visible economy, balanced, advanced, or frontier
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general-purpose model. It uses comparable provider cost data only to break
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ties among models that already satisfy the capability requirement. Do not
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infer a tier from an unfamiliar model name or unadvertised effort support.
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Examples:
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Critical security migration final review -> worker_claude_auto_xhigh.
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Implement critical security review fixes -> worker_codex_auto_xhigh.
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Difficult intermittent production failure -> worker_codex_auto_high.
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Ordinary component design -> worker_claude_auto_medium.
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One spelling correction -> worker_codex_auto_low.
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Critical security migration final review -> worker_claude_auto_frontier_xhigh.
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Implement critical security review fixes -> worker_codex_auto_advanced_xhigh.
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Difficult intermittent production failure -> worker_codex_auto_advanced_high.
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Ordinary component design -> worker_claude_auto_balanced_medium.
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One spelling correction -> worker_codex_auto_economy_low.
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Anthropic exhausted + critical independent final review ->
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worker_codex_auto_xhigh.
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worker_codex_auto_frontier_xhigh.
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OpenAI exhausted + difficult repository implementation ->
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worker_claude_auto_high.
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worker_claude_auto_advanced_high.
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Before responding, verify the provider is available and effort is not below
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the floor. Return only the required decision object.
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@ -22,7 +22,7 @@ spec:
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labels:
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app: hermes-switchyard
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annotations:
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ai.bstein.dev/config-rev: "20260913-capability-effort-v3"
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ai.bstein.dev/config-rev: "20260913-capability-effort-v4"
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prometheus.io/scrape: "true"
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prometheus.io/port: "9005"
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prometheus.io/path: /metrics
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@ -4,6 +4,7 @@ from __future__ import annotations
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import importlib.util
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import json
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import re
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import sqlite3
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import sys
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import tomllib
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@ -411,7 +412,7 @@ def test_local_flux_runtime_and_gpu_handoff_are_flux_managed():
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assert "route/local/qwen2.5-14b/medium" in switchyard
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assert "Anthropic and Claude name the same provider" in switchyard
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assert "OpenAI and Codex name the same provider" in switchyard
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assert "account-visible economy, balanced, or advanced" in switchyard
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assert "account-visible economy, balanced, advanced, or frontier" in switchyard
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assert "Choose across every configured Codex and Claude family" not in switchyard
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assert switchyard.count('Treat "think hard"') == 4
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assert switchyard.count("Never choose below the") >= 5
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@ -526,3 +527,32 @@ def test_local_flux_runtime_and_gpu_handoff_are_flux_managed():
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"model_gate.py"
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]
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assert "qwen2.5:14b-instruct-q4_0" in model_gate
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def test_classifier_prompt_examples_are_allowed_and_schema_valid():
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"""Classifier prompt examples must name configured capability targets."""
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switchyard = _documents(HERMES / "switchyard-configmap.yaml")[0]["data"][
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"routes.toml"
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]
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routes = tomllib.loads(switchyard)["routes"]
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target_pattern = re.compile(
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r"\b(?:worker_)?(?:codex|claude)_auto_[a-z0-9_]+\b"
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)
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classifier_count = 0
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example_count = 0
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for route in routes.values():
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if route.get("type") != "llm_classifier":
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continue
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classifier_count += 1
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examples = target_pattern.findall(route["prompt"])
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example_count += len(examples)
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schema = json.loads(route["response_schema"])
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pattern = schema["properties"]["decision"]["properties"]["target"][
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"pattern"
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]
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allowed_targets = set(route["targets"])
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assert all(target in allowed_targets for target in examples), route["id"]
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assert all(re.fullmatch(pattern, target) for target in examples), route["id"]
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assert classifier_count >= 5
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assert example_count >= 8
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