diff --git a/.github/workflows/ci-quality-gate.yml b/.github/workflows/ci-quality-gate.yml
index ff8480bf..e250072f 100644
--- a/.github/workflows/ci-quality-gate.yml
+++ b/.github/workflows/ci-quality-gate.yml
@@ -114,10 +114,10 @@ jobs:
           python3 scripts/check_dual_publish.py
 
       # Proposed by audit/newgen-2026-06 and never built, which is why retired
-      # model IDs survived two later audits. Advisory while the existing
-      # references are cleaned up; flip to blocking once the tree is clean.
-      - name: Retired model identifier lint (gate G7 — advisory)
-        continue-on-error: true
+      # model IDs survived two later audits. The tree is now clean, so this is
+      # blocking. A deliberate reference needs an entry with a reason in
+      # scripts/check_model_freshness_allowlist.txt.
+      - name: Retired model identifier lint (gate G7 — blocking)
         run: |
           python3 scripts/check_model_freshness.py --all
 
diff --git a/SKILL_PIPELINE.md b/SKILL_PIPELINE.md
index c32e79e3..d50b2ac4 100644
--- a/SKILL_PIPELINE.md
+++ b/SKILL_PIPELINE.md
@@ -111,7 +111,7 @@ After skill is finalized:
    ```bash
    python -m scripts.run_loop \
      --eval-set <trigger-eval.json> --skill-path <path> \
-     --model anthropic/claude-opus-4-6 --max-iterations 5 --verbose
+     --model anthropic/claude-opus-5 --max-iterations 5 --verbose
    ```
 4. Apply `best_description` to SKILL.md frontmatter
 
diff --git a/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/references/ai_cost_economics.md b/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/references/ai_cost_economics.md
index 17ff8463..8c723d80 100644
--- a/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/references/ai_cost_economics.md
+++ b/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/references/ai_cost_economics.md
@@ -18,8 +18,8 @@ Per million tokens, USD:
 
 | Tier | Example models | Input | Output |
 |---|---|---|---|
-| Frontier-premium | Claude Sonnet 4.6, GPT-4o-tier | $3.00 | $15.00 |
-| Frontier-economy | Gemini 2.5 Flash, Claude Haiku 4.5-tier | $1.25 | $5.00 |
+| Frontier-premium | Claude Sonnet 5, frontier-mid tier | $3.00 | $15.00 |
+| Frontier-economy | Claude Haiku 4.5, small-model tier | $1.25 | $5.00 |
 | Open-hosted | Llama 3.1 70B / Qwen 2.5 72B via Together, Fireworks, OpenRouter | $0.50 | $1.50 |
 | Open-economy | 8B-13B-class hosted | $0.10 | $0.30 |
 
@@ -126,7 +126,7 @@ If your utilization is 30% instead of 70%, your effective cost per token roughly
 ### 2. Capability Drift
 - Provider updates models silently or with brief notice
 - Your prompts may produce different outputs after upgrade
-- Mitigation: pin model IDs (e.g., `claude-sonnet-4-6` vs `claude-sonnet-latest`)
+- Mitigation: pin model IDs (e.g., `claude-sonnet-5` vs `claude-sonnet-latest`)
 - Cost: regression eval runs on every model swap
 
 ### 3. Rate Limits
diff --git a/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/scripts/ai_cost_economics.py b/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/scripts/ai_cost_economics.py
index 04141cef..cb18922f 100644
--- a/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/scripts/ai_cost_economics.py
+++ b/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/scripts/ai_cost_economics.py
@@ -48,8 +48,8 @@ SAMPLE: Dict[str, Any] = {
 
 # 2026 API pricing per million tokens, $USD (input / output)
 API_PRICING = {
-    "frontier-premium": {"input": 3.00, "output": 15.00, "label": "Claude Sonnet 4.6 / GPT-4o-tier"},
-    "frontier-economy": {"input": 1.25, "output": 5.00, "label": "Gemini 2.5 Flash / Claude Haiku 4.5-tier"},
+    "frontier-premium": {"input": 3.00, "output": 15.00, "label": "Claude Sonnet 5 / frontier-mid tier"},
+    "frontier-economy": {"input": 1.25, "output": 5.00, "label": "Claude Haiku 4.5 / small-model tier"},
     "open-hosted": {"input": 0.50, "output": 1.50, "label": "Llama 3.1 70B / Qwen 2.5 72B via hosted endpoint"},
 }
 
diff --git a/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/scripts/model_buildvsbuy_calculator.py b/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/scripts/model_buildvsbuy_calculator.py
index da0d537f..2b1f4c03 100644
--- a/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/scripts/model_buildvsbuy_calculator.py
+++ b/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/scripts/model_buildvsbuy_calculator.py
@@ -54,8 +54,8 @@ SAMPLE: Dict[str, Any] = {
 # 2026 API pricing per million tokens, $USD (input / output). These are illustrative;
 # real pricing changes; rerun this calculator quarterly.
 API_PRICING = {
-    "frontier-premium": {"input": 3.00, "output": 15.00, "label": "Claude Sonnet 4.6 / GPT-4o-tier"},
-    "frontier-economy": {"input": 1.25, "output": 5.00, "label": "Gemini 2.5 Flash / Claude Haiku 4.5-tier"},
+    "frontier-premium": {"input": 3.00, "output": 15.00, "label": "Claude Sonnet 5 / frontier-mid tier"},
+    "frontier-economy": {"input": 1.25, "output": 5.00, "label": "Claude Haiku 4.5 / small-model tier"},
     "open-router-hosted": {"input": 0.50, "output": 1.50, "label": "Llama 3.1 70B / Qwen 2.5 72B via hosted endpoint"},
 }
 
diff --git a/c-level-advisor/skills/chief-ai-officer-advisor/references/ai_cost_economics.md b/c-level-advisor/skills/chief-ai-officer-advisor/references/ai_cost_economics.md
index 17ff8463..8c723d80 100644
--- a/c-level-advisor/skills/chief-ai-officer-advisor/references/ai_cost_economics.md
+++ b/c-level-advisor/skills/chief-ai-officer-advisor/references/ai_cost_economics.md
@@ -18,8 +18,8 @@ Per million tokens, USD:
 
 | Tier | Example models | Input | Output |
 |---|---|---|---|
-| Frontier-premium | Claude Sonnet 4.6, GPT-4o-tier | $3.00 | $15.00 |
-| Frontier-economy | Gemini 2.5 Flash, Claude Haiku 4.5-tier | $1.25 | $5.00 |
+| Frontier-premium | Claude Sonnet 5, frontier-mid tier | $3.00 | $15.00 |
+| Frontier-economy | Claude Haiku 4.5, small-model tier | $1.25 | $5.00 |
 | Open-hosted | Llama 3.1 70B / Qwen 2.5 72B via Together, Fireworks, OpenRouter | $0.50 | $1.50 |
 | Open-economy | 8B-13B-class hosted | $0.10 | $0.30 |
 
@@ -126,7 +126,7 @@ If your utilization is 30% instead of 70%, your effective cost per token roughly
 ### 2. Capability Drift
 - Provider updates models silently or with brief notice
 - Your prompts may produce different outputs after upgrade
-- Mitigation: pin model IDs (e.g., `claude-sonnet-4-6` vs `claude-sonnet-latest`)
+- Mitigation: pin model IDs (e.g., `claude-sonnet-5` vs `claude-sonnet-latest`)
 - Cost: regression eval runs on every model swap
 
 ### 3. Rate Limits
diff --git a/c-level-advisor/skills/chief-ai-officer-advisor/scripts/ai_cost_economics.py b/c-level-advisor/skills/chief-ai-officer-advisor/scripts/ai_cost_economics.py
index 04141cef..cb18922f 100644
--- a/c-level-advisor/skills/chief-ai-officer-advisor/scripts/ai_cost_economics.py
+++ b/c-level-advisor/skills/chief-ai-officer-advisor/scripts/ai_cost_economics.py
@@ -48,8 +48,8 @@ SAMPLE: Dict[str, Any] = {
 
 # 2026 API pricing per million tokens, $USD (input / output)
 API_PRICING = {
-    "frontier-premium": {"input": 3.00, "output": 15.00, "label": "Claude Sonnet 4.6 / GPT-4o-tier"},
-    "frontier-economy": {"input": 1.25, "output": 5.00, "label": "Gemini 2.5 Flash / Claude Haiku 4.5-tier"},
+    "frontier-premium": {"input": 3.00, "output": 15.00, "label": "Claude Sonnet 5 / frontier-mid tier"},
+    "frontier-economy": {"input": 1.25, "output": 5.00, "label": "Claude Haiku 4.5 / small-model tier"},
     "open-hosted": {"input": 0.50, "output": 1.50, "label": "Llama 3.1 70B / Qwen 2.5 72B via hosted endpoint"},
 }
 
diff --git a/c-level-advisor/skills/chief-ai-officer-advisor/scripts/model_buildvsbuy_calculator.py b/c-level-advisor/skills/chief-ai-officer-advisor/scripts/model_buildvsbuy_calculator.py
index da0d537f..2b1f4c03 100644
--- a/c-level-advisor/skills/chief-ai-officer-advisor/scripts/model_buildvsbuy_calculator.py
+++ b/c-level-advisor/skills/chief-ai-officer-advisor/scripts/model_buildvsbuy_calculator.py
@@ -54,8 +54,8 @@ SAMPLE: Dict[str, Any] = {
 # 2026 API pricing per million tokens, $USD (input / output). These are illustrative;
 # real pricing changes; rerun this calculator quarterly.
 API_PRICING = {
-    "frontier-premium": {"input": 3.00, "output": 15.00, "label": "Claude Sonnet 4.6 / GPT-4o-tier"},
-    "frontier-economy": {"input": 1.25, "output": 5.00, "label": "Gemini 2.5 Flash / Claude Haiku 4.5-tier"},
+    "frontier-premium": {"input": 3.00, "output": 15.00, "label": "Claude Sonnet 5 / frontier-mid tier"},
+    "frontier-economy": {"input": 1.25, "output": 5.00, "label": "Claude Haiku 4.5 / small-model tier"},
     "open-router-hosted": {"input": 0.50, "output": 1.50, "label": "Llama 3.1 70B / Qwen 2.5 72B via hosted endpoint"},
 }
 
diff --git a/engineering-team/TEAM_STRUCTURE_GUIDE.md b/engineering-team/TEAM_STRUCTURE_GUIDE.md
index e22343fc..136f11e6 100644
--- a/engineering-team/TEAM_STRUCTURE_GUIDE.md
+++ b/engineering-team/TEAM_STRUCTURE_GUIDE.md
@@ -341,7 +341,7 @@ python scripts/model_deployment_pipeline.py --train --config model_config.yaml
 
 # 4. Optimize prompts
 cd ../senior-prompt-engineer
-python scripts/prompt_optimizer.py --model gpt-4 --task classification
+python scripts/prompt_optimizer.py classification_prompt.txt --analyze --model claude
 
 # 5. Deploy with DevOps
 cd ../senior-devops
diff --git a/engineering-team/skills/senior-ml-engineer/SKILL.md b/engineering-team/skills/senior-ml-engineer/SKILL.md
index 59943690..6205ef1f 100644
--- a/engineering-team/skills/senior-ml-engineer/SKILL.md
+++ b/engineering-team/skills/senior-ml-engineer/SKILL.md
@@ -149,12 +149,23 @@ def call_llm_with_retry(provider: LLMProvider, prompt: str) -> str:
 
 ### Cost Management
 
-| Provider | Input Cost | Output Cost |
-|----------|------------|-------------|
-| GPT-4 | $0.03/1K | $0.06/1K |
-| GPT-3.5 | $0.0005/1K | $0.0015/1K |
-| Claude 3 Opus | $0.015/1K | $0.075/1K |
-| Claude 3 Haiku | $0.00025/1K | $0.00125/1K |
+Do not hardcode prices, and do not trust a price table you find in a document
+(including this one). Providers reprice several times a year, and a stale
+figure produces a confidently wrong business case.
+
+Work in tiers and look the current numbers up at request time:
+
+| Tier | Typical use | Relative cost |
+|------|-------------|---------------|
+| Small | Classification, extraction, routing, short output | 1x baseline |
+| Mid | Summarisation, structured output, moderate reasoning | ~10-25x small |
+| Large | Multi-step reasoning, code generation, long context | ~50-100x small |
+
+Read the live rate from your provider's pricing page and pass it in, the way
+`engineering-team/skills/senior-prompt-engineer/scripts/prompt_optimizer.py`
+takes `--price-per-mtok`.
+The ratios between tiers are far more stable than the absolute prices, so
+build the model-routing decision on the ratio.
 
 ---
 
diff --git a/engineering-team/skills/senior-ml-engineer/references/llm_integration_guide.md b/engineering-team/skills/senior-ml-engineer/references/llm_integration_guide.md
index 6e715ab1..0cd90c0e 100644
--- a/engineering-team/skills/senior-ml-engineer/references/llm_integration_guide.md
+++ b/engineering-team/skills/senior-ml-engineer/references/llm_integration_guide.md
@@ -34,7 +34,9 @@ class LLMProvider(ABC):
         pass
 
 class OpenAIProvider(LLMProvider):
-    def __init__(self, api_key: str, model: str = "gpt-4"):
+    # No default model: a hardcoded default is the thing that goes stale.
+    # Pass the model your deployment is pinned to, from config.
+    def __init__(self, api_key: str, model: str):
         self.client = OpenAI(api_key=api_key)
         self.model = model
 
@@ -47,7 +49,7 @@ class OpenAIProvider(LLMProvider):
         return response.choices[0].text
 
 class AnthropicProvider(LLMProvider):
-    def __init__(self, api_key: str, model: str = "claude-3-opus"):
+    def __init__(self, api_key: str, model: str = "claude-opus-5"):
         self.client = Anthropic(api_key=api_key)
         self.model = model
 
@@ -158,14 +160,19 @@ def create_chat_messages(user_query: str, context: str) -> List[Dict]:
 ```python
 import tiktoken
 
-def count_tokens(text: str, model: str = "gpt-4") -> int:
-    """Count tokens for a given text and model."""
-    encoding = tiktoken.encoding_for_model(model)
+def count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
+    """Count tokens for a given text.
+
+    Takes an encoding name rather than a model name: encodings change far more
+    slowly than model IDs, and tiktoken.encoding_for_model() raises KeyError on
+    any model it has not shipped a mapping for yet.
+    """
+    encoding = tiktoken.get_encoding(encoding_name)
     return len(encoding.encode(text))
 
-def truncate_to_token_limit(text: str, max_tokens: int, model: str = "gpt-4") -> str:
+def truncate_to_token_limit(text: str, max_tokens: int, encoding_name: str = "cl100k_base") -> str:
     """Truncate text to fit within token limit."""
-    encoding = tiktoken.encoding_for_model(model)
+    encoding = tiktoken.get_encoding(encoding_name)
     tokens = encoding.encode(text)
 
     if len(tokens) <= max_tokens:
@@ -176,12 +183,13 @@ def truncate_to_token_limit(text: str, max_tokens: int, model: str = "gpt-4") ->
 
 ### Context Window Management
 
-| Model | Context Window | Effective Limit |
-|-------|----------------|-----------------|
-| GPT-4 | 8,192 | ~6,000 (leave room for response) |
-| GPT-4-32k | 32,768 | ~28,000 |
-| Claude 3 | 200,000 | ~180,000 |
-| Llama 3 | 8,192 | ~6,000 |
+Query the model's advertised context window at runtime rather than table it
+here; every fixed number in this position has gone stale within a year.
+
+The rule that does not change: reserve headroom for the response. Budget
+roughly 75-90% of the window for input and leave the remainder for output,
+then cap output explicitly with `max_tokens` so a runaway generation cannot
+overflow the window or the bill.
 
 ### Chunking Strategy
 
@@ -206,12 +214,9 @@ def chunk_text(text: str, chunk_size: int = 1000, overlap: int = 100) -> List[st
 
 ### Cost Calculation
 
-| Provider | Input Cost | Output Cost | Example (1K tokens) |
-|----------|------------|-------------|---------------------|
-| GPT-4 | $0.03/1K | $0.06/1K | $0.09 |
-| GPT-3.5 | $0.0005/1K | $0.0015/1K | $0.002 |
-| Claude 3 Opus | $0.015/1K | $0.075/1K | $0.09 |
-| Claude 3 Haiku | $0.00025/1K | $0.00125/1K | $0.0015 |
+Prices move several times a year, so this guide does not carry a rate table.
+Pull the current per-million-token input and output prices from your
+provider's pricing page and inject them as configuration.
 
 ### Cost Tracking
 
@@ -229,26 +234,24 @@ class LLMUsage:
 def calculate_cost(
     input_tokens: int,
     output_tokens: int,
-    model: str
+    input_price_per_mtok: float,
+    output_price_per_mtok: float,
 ) -> float:
-    """Calculate cost based on token usage."""
-    PRICING = {
-        "gpt-4": {"input": 0.03, "output": 0.06},
-        "gpt-3.5-turbo": {"input": 0.0005, "output": 0.0015},
-        "claude-3-opus": {"input": 0.015, "output": 0.075},
-    }
+    """Calculate cost from caller-supplied rates (USD per million tokens).
 
-    prices = PRICING.get(model, {"input": 0.01, "output": 0.03})
-
-    input_cost = (input_tokens / 1000) * prices["input"]
-    output_cost = (output_tokens / 1000) * prices["output"]
+    Rates are parameters, not constants. A hardcoded price table is wrong
+    within a year and silently produces a confidently incorrect business case.
+    Load these from config so they can be updated without a code change.
+    """
+    input_cost = (input_tokens / 1_000_000) * input_price_per_mtok
+    output_cost = (output_tokens / 1_000_000) * output_price_per_mtok
 
     return input_cost + output_cost
 ```
 
 ### Cost Optimization Strategies
 
-1. **Use smaller models for simple tasks** - GPT-3.5 for classification, GPT-4 for reasoning
+1. **Use smaller models for simple tasks** - small tier for classification, large tier for reasoning
 2. **Cache common responses** - Store results for repeated queries
 3. **Batch requests** - Combine multiple items in single prompt
 4. **Truncate context** - Only include relevant information
diff --git a/engineering/llm-cost-optimizer/skills/llm-cost-optimizer/SKILL.md b/engineering/llm-cost-optimizer/skills/llm-cost-optimizer/SKILL.md
index 3dd22507..86f049d9 100644
--- a/engineering/llm-cost-optimizer/skills/llm-cost-optimizer/SKILL.md
+++ b/engineering/llm-cost-optimizer/skills/llm-cost-optimizer/SKILL.md
@@ -60,9 +60,13 @@ Sort by: feature × model × token count. Usually 2–3 endpoints drive the majo
 
 | Complexity | Characteristics | Right Model Tier |
 |---|---|---|
-| Simple | Classification, extraction, yes/no, short output | Small (Haiku, GPT-4o-mini, Gemini Flash) |
-| Medium | Summarization, structured output, moderate reasoning | Mid (Sonnet, GPT-4o) |
-| Complex | Multi-step reasoning, code gen, long context | Large (Opus, o3) |
+| Simple | Classification, extraction, yes/no, short output | Small (Haiku tier, or your provider's cheapest) |
+| Medium | Summarization, structured output, moderate reasoning | Mid (Sonnet tier) |
+| Complex | Multi-step reasoning, code gen, long context | Large (Opus tier, or your provider's frontier model) |
+
+Tiers, not model names: the naming churns every few months, the three-tier
+shape does not. Check your provider's current lineup and price list when you
+apply this.
 
 **If token logging doesn't exist yet:** That's the first deliverable -- not prompt compression, not routing. You cannot optimize what you cannot see. Provide a logging schema and move to optimization only once baseline data exists.
 
diff --git a/engineering/prompt-governance/skills/prompt-governance/SKILL.md b/engineering/prompt-governance/skills/prompt-governance/SKILL.md
index c517fe62..00584138 100644
--- a/engineering/prompt-governance/skills/prompt-governance/SKILL.md
+++ b/engineering/prompt-governance/skills/prompt-governance/SKILL.md
@@ -77,7 +77,7 @@ prompts:
   - id: summarizer
     description: "Summarize support tickets for agent triage"
     owner: platform-team
-    model: claude-sonnet-4-5
+    model: claude-sonnet-5
     versions:
       - version: 1.1.0
         file: summarizer/v1.1.0.md
diff --git a/engineering/skills/agent-designer/README.md b/engineering/skills/agent-designer/README.md
index 5a023e71..47a4e902 100644
--- a/engineering/skills/agent-designer/README.md
+++ b/engineering/skills/agent-designer/README.md
@@ -358,7 +358,7 @@ with open('my_tools_openai.json') as f:
 
 # Use with OpenAI function calling
 response = openai.ChatCompletion.create(
-    model="gpt-4",
+    model=OPENAI_MODEL,  # from config; don't hardcode a model ID
     messages=[{"role": "user", "content": "Search for AI news"}],
     functions=schemas['functions']
 )
@@ -377,7 +377,7 @@ with open('my_tools_anthropic.json') as f:
 # Use with Anthropic tool use
 client = anthropic.Anthropic()
 response = client.messages.create(
-    model="claude-3-opus-20240229",
+    model="claude-opus-5",
     messages=[{"role": "user", "content": "Search for AI news"}],
     tools=schemas['tools']
 )
diff --git a/engineering/skills/agent-designer/agent_evaluator.py b/engineering/skills/agent-designer/agent_evaluator.py
index 8d86c56a..f76eca5e 100644
--- a/engineering/skills/agent-designer/agent_evaluator.py
+++ b/engineering/skills/agent-designer/agent_evaluator.py
@@ -133,7 +133,6 @@ class AgentEvaluator:
     def __init__(self):
         self.error_patterns = self._define_error_patterns()
         self.performance_thresholds = self._define_performance_thresholds()
-        self.cost_benchmarks = self._define_cost_benchmarks()
     
     def _define_error_patterns(self) -> Dict[str, Dict[str, Any]]:
         """Define common error patterns and their classifications"""
@@ -217,23 +216,15 @@ class AgentEvaluator:
             "throughput": {"excellent": 100, "good": 50, "acceptable": 20, "poor": 5}  # tasks per hour
         }
     
-    def _define_cost_benchmarks(self) -> Dict[str, Any]:
-        """Define cost benchmarks for different operations"""
-        return {
-            "token_costs": {
-                "gpt-4": {"input": 0.00003, "output": 0.00006},
-                "gpt-3.5-turbo": {"input": 0.000002, "output": 0.000002},
-                "claude-3": {"input": 0.000015, "output": 0.000075}
-            },
-            "operation_costs": {
-                "simple_task": 0.005,
-                "complex_task": 0.050,
-                "research_task": 0.020,
-                "analysis_task": 0.030,
-                "generation_task": 0.015
-            }
-        }
-    
+    # _define_cost_benchmarks() was removed. It hardcoded per-token prices for
+    # gpt-4, gpt-3.5-turbo and claude-3 — all retired, all priced at 2024
+    # rates — and the result was assigned to self.cost_benchmarks and never
+    # read by anything. Cost analysis in this tool uses the cost_usd field the
+    # caller supplies in each execution log, which is the only figure that can
+    # be accurate. See engineering-team/skills/senior-prompt-engineer/scripts/
+    # prompt_optimizer.py for the --price-per-mtok pattern if a price is
+    # genuinely needed.
+
     def parse_execution_logs(self, logs_data: List[Dict[str, Any]]) -> List[ExecutionLog]:
         """Parse raw execution logs into structured format"""
         logs = []
diff --git a/engineering/skills/agent-designer/assets/sample_execution_logs.json b/engineering/skills/agent-designer/assets/sample_execution_logs.json
index 13ec29bd..0f7b45bb 100644
--- a/engineering/skills/agent-designer/assets/sample_execution_logs.json
+++ b/engineering/skills/agent-designer/assets/sample_execution_logs.json
@@ -38,7 +38,7 @@
         }
       ],
       "results": {
-        "summary": "Found 15 relevant sources covering recent AI developments including GPT-4 improvements, autonomous vehicle progress, and medical AI applications.",
+        "summary": "Found 15 relevant sources covering recent AI developments including frontier model improvements, autonomous vehicle progress, and medical AI applications.",
         "sources_found": 15,
         "quality_score": 0.92
       },
diff --git a/engineering/skills/agent-designer/references/evaluation_methodology.md b/engineering/skills/agent-designer/references/evaluation_methodology.md
index 3b430f5b..24920fe9 100644
--- a/engineering/skills/agent-designer/references/evaluation_methodology.md
+++ b/engineering/skills/agent-designer/references/evaluation_methodology.md
@@ -653,8 +653,8 @@ trend_analysis:
 ### API Usage
 - **Token Consumption**: 2.4M tokens/day
 - **Cost Breakdown**:
-  - GPT-4: 68% of token costs
-  - GPT-3.5: 28% of token costs
+  - Large-tier model: 68% of token costs
+  - Small-tier model: 28% of token costs
   - Other models: 4% of token costs
 ```
 
diff --git a/scripts/check_model_freshness.py b/scripts/check_model_freshness.py
index 6a79db80..5d32c309 100644
--- a/scripts/check_model_freshness.py
+++ b/scripts/check_model_freshness.py
@@ -44,6 +44,10 @@ EXCLUDED_DIRS = {
 
 SCAN_EXTENSIONS = (".md", ".py", ".json", ".yaml", ".yml", ".sh", ".txt")
 
+# This linter and its allowlist necessarily name retired models; scanning them
+# would just require self-referential allowlist entries.
+SELF_FILES = {"check_model_freshness.py", "check_model_freshness_allowlist.txt"}
+
 # Retired or superseded identifiers, as (regex, label) pairs. Word-ish bounded
 # so `claude-3` does not match `claude-3x-something` unintentionally.
 RETIRED_PATTERNS = [
@@ -64,7 +68,9 @@ RETIRED_PATTERNS = [
      "Claude 4 family (superseded by Claude 5)"),
     (r"\banthropic/claude-[a-z]+-4[.-]\d+\b", "Claude 4 family (superseded by Claude 5)"),
     (r"\bgpt-3\.5(?:-turbo)?\b", "gpt-3.5 (retired)"),
-    (r"\bgpt-4(?:-32k|o|o-mini)?\b(?!\S)", "gpt-4 family (superseded)"),
+    # Longer variants first: alternation is ordered, so `o-mini` must precede `o`
+    # or `gpt-4o-mini` reports as `gpt-4o`.
+    (r"\bgpt-4(?:-32k|o-mini|o)?\b", "gpt-4 family (superseded)"),
     (r"\bGPT-4(?:o|-32k)?\b", "gpt-4 family (superseded)"),
     (r"\btext-embedding-ada-002\b", "ada-002 embeddings (superseded)"),
     (r"\bgemini-1\.5(?:-[a-z]+)?\b", "Gemini 1.5 (superseded)"),
@@ -153,7 +159,7 @@ def collect(repo_root):
     for dirpath, dirnames, filenames in os.walk(repo_root):
         dirnames[:] = [d for d in dirnames if d not in EXCLUDED_DIRS]
         for fn in filenames:
-            if fn in EXCLUDED_DIRS:
+            if fn in EXCLUDED_DIRS or fn in SELF_FILES:
                 continue
             if fn.endswith(SCAN_EXTENSIONS):
                 targets.append(os.path.join(dirpath, fn))
diff --git a/scripts/check_model_freshness_allowlist.txt b/scripts/check_model_freshness_allowlist.txt
index 797ea520..e54bda91 100644
--- a/scripts/check_model_freshness_allowlist.txt
+++ b/scripts/check_model_freshness_allowlist.txt
@@ -8,14 +8,6 @@
 #
 # Every entry needs a one-line reason above it.
 
-# The linter's own deny-list and this allowlist's examples.
-scripts/check_model_freshness.py :: claude
-scripts/check_model_freshness.py :: gpt-
-scripts/check_model_freshness.py :: gemini-
-scripts/check_model_freshness.py :: GPT-4
-scripts/check_model_freshness_allowlist.txt :: gpt-
-scripts/check_model_freshness_allowlist.txt :: GPT-4
-
 # The EU AI Act's own systemic-risk examples are frozen in the cited text;
 # renaming them would misquote the regulation.
 */references/ai_risk_governance.md :: systemic risk
@@ -24,3 +16,13 @@ scripts/check_model_freshness_allowlist.txt :: GPT-4
 # warning already at the top of the file.
 */references/embedding_model_benchmark.md :: ada-002
 */references/embedding_model_benchmark.md :: text-embedding
+
+# litreview examples where the retired model is the SUBJECT of the literature
+# being reviewed, not a model the skill would call. Renaming these would
+# misdescribe the worked example.
+*/litreview/references/framework_selection.md :: Med-PaLM
+*/litreview/scripts/cross_search_aggregator.py :: medical Q&A
+
+# Dated citation: Computer Use shipped on Claude 3.5 Sonnet, and the "+ later"
+# is the point of the sentence.
+*/notebooklm/references/browser_automation_canon.md :: Computer Use documentation