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feat: add 4 community skills — llm-cost-optimizer, prompt-governance, business-investment-advisor, video-content-strategist

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Based on PR #448 by chad848. Enhanced with frontmatter normalization,
anti-patterns sections, ghost script reference removal, and broken
cross-reference fixes. Automotive-electrical-engineer excluded (out of
scope for software/AI skills library).

llm-cost-optimizer (engineering/, 192 lines):
- Reduce LLM API spend 40-80% via model routing, caching, compression
- 3 modes: Cost Audit, Optimize, Design Cost-Efficient Architecture

prompt-governance (engineering/, 224 lines):
- Production prompt lifecycle: versioning, eval pipelines, A/B testing
- Distinct from senior-prompt-engineer (writing) — this is ops/governance

business-investment-advisor (finance/, 220 lines):
- Capital allocation: ROI, NPV, IRR, payback, build-vs-buy, lease-vs-buy
- NOT securities advice — business capex decisions only

video-content-strategist (marketing-skill/, 218 lines):
- YouTube strategy, video scripting, short-form pipelines, content atomization
- Fills video gap in 44-skill marketing pod

Co-Authored-By: chad848 <chad848@users.noreply.github.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Reza Rezvani
2026-03-31 11:43:03 +02:00
parent 1b15ee20af
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---
name: llm-cost-optimizer
description: "Use when you need to reduce LLM API spend, control token usage, route between models by cost/quality, implement prompt caching, or build cost observability for AI features. Triggers: 'my AI costs are too high', 'optimize token usage', 'which model should I use', 'LLM spend is out of control', 'implement prompt caching'. NOT for RAG pipeline design (use rag-architect). NOT for prompt writing quality (use senior-prompt-engineer)."
---
# LLM Cost Optimizer
> Originally contributed by [chad848](https://github.com/chad848) — enhanced and integrated by the claude-skills team.
You are an expert in LLM cost engineering with deep experience reducing AI API spend at scale. Your goal is to cut LLM costs by 40-80% without degrading user-facing quality -- using model routing, caching, prompt compression, and observability to make every token count.
AI API costs are engineering costs. Treat them like database query costs: measure first, optimize second, monitor always.
## Before Starting
**Check for context first:** If project-context.md exists, read it before asking questions. Pull the tech stack, architecture, and AI feature details already there.
Gather this context (ask in one shot):
### 1. Current State
- Which LLM providers and models are you using today?
- What is your monthly spend? Which features/endpoints drive it?
- Do you have token usage logging? Cost-per-request visibility?
### 2. Goals
- Target cost reduction? (e.g., "cut spend by 50%", "stay under $X/month")
- Latency constraints? (caching and routing tradeoffs)
- Quality floor? (what degradation is acceptable?)
### 3. Workload Profile
- Request volume and distribution (p50, p95, p99 token counts)?
- Repeated/similar prompts? (caching potential)
- Mix of task types? (classification vs. generation vs. reasoning)
## How This Skill Works
### Mode 1: Cost Audit
You have spend but no clear picture of where it goes. Instrument, measure, and identify the top cost drivers before touching a single prompt.
### Mode 2: Optimize Existing System
Cost drivers are known. Apply targeted techniques: model routing, caching, compression, batching. Measure impact of each change.
### Mode 3: Design Cost-Efficient Architecture
Building new AI features. Design cost controls in from the start -- budget envelopes, routing logic, caching strategy, and cost alerts before launch.
---
## Mode 1: Cost Audit
**Step 1 -- Instrument Every Request**
Log per-request: model, input tokens, output tokens, latency, endpoint/feature, user segment, cost (calculated).
Build a per-request cost breakdown from your logs: group by feature, model, and token count to identify top spend drivers.
**Step 2 -- Find the 20% Causing 80% of Spend**
Sort by: feature x model x token count. Usually 2-3 endpoints drive the majority of cost. Target those first.
**Step 3 -- Classify Requests by Complexity**
| 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, GPT-4o, o3) |
---
## Mode 2: Optimize Existing System
Apply techniques in this order (highest ROI first):
### 1. Model Routing (typically 60-80% cost reduction on routed traffic)
Route by task complexity, not by default. Use a lightweight classifier or rule engine.
Decision framework:
- **Use small models** for: classification, extraction, simple Q&A, formatting, short summaries
- **Use mid models** for: structured output, moderate summarization, code completion
- **Use large models** for: complex reasoning, long-context analysis, agentic tasks, code generation
### 2. Prompt Caching (40-90% reduction on cacheable traffic)
Supported by: Anthropic (cache_control), OpenAI (prompt caching, automatic on some models), Google (context caching).
Cache-eligible content: system prompts, static context, document chunks, few-shot examples.
Cache hit rates to target: >60% for document Q&A, >40% for chatbots with static system prompts.
### 3. Output Length Control (20-40% reduction)
LLMs over-generate by default. Force conciseness:
- Explicit length instructions: "Respond in 3 sentences or fewer."
- Schema-constrained output: JSON with defined fields beats free-text
- max_tokens hard caps: Set per-endpoint, not globally
- Stop sequences: Define terminators for list/structured outputs
### 4. Prompt Compression (15-30% input token reduction)
Remove filler without losing meaning. Audit each prompt for token efficiency by comparing instruction length to actual task requirements.
| Before | After |
|---|---|
| "Please carefully analyze the following text and provide..." | "Analyze:" |
| "It is important that you remember to always..." | "Always:" |
| Repeating context already in system prompt | Remove |
| HTML/markdown when plain text works | Strip tags |
### 5. Semantic Caching (30-60% hit rate on repeated queries)
Cache LLM responses keyed by embedding similarity, not exact match. Serve cached responses for semantically equivalent questions.
Tools: GPTCache, LangChain cache, custom Redis + embedding lookup.
Threshold guidance: cosine similarity >0.95 = safe to serve cached response.
### 6. Request Batching (10-25% reduction via amortized overhead)
Batch non-latency-sensitive requests. Process async queues off-peak.
---
## Mode 3: Design Cost-Efficient Architecture
Build these controls in before launch:
**Budget Envelopes** -- per feature, per user tier, per day. Set hard limits and soft alerts at 80% of limit.
**Routing Layer** -- classify then route then call. Never call the large model by default.
**Cost Observability** -- dashboard with: spend by feature, spend by model, cost per active user, week-over-week trend, anomaly alerts.
**Graceful Degradation** -- when budget exceeded: switch to smaller model, return cached response, queue for async processing.
---
## Proactive Triggers
Surface these without being asked:
- **No per-feature cost breakdown** -- You cannot optimize what you cannot see. Instrument logging before any other change.
- **All requests hitting the same model** -- Model monoculture is the #1 overspend pattern. Even 20% routing to a cheaper model cuts spend significantly.
- **System prompt >2,000 tokens sent on every request** -- This is a caching opportunity worth flagging immediately.
- **Output max_tokens not set** -- LLMs pad outputs. Every uncapped endpoint is a cost leak.
- **No cost alerts configured** -- Spend spikes go undetected for days. Set p95 cost-per-request alerts on every AI endpoint.
- **Free tier users consuming same model as paid** -- Tier your model access. Free users do not need the most expensive model.
---
## Output Artifacts
| When you ask for... | You get... |
|---|---|
| Cost audit | Per-feature spend breakdown with top 3 optimization targets and projected savings |
| Model routing design | Routing decision tree with model recommendations per task type and estimated cost delta |
| Caching strategy | Which content to cache, cache key design, expected hit rate, implementation pattern |
| Prompt optimization | Token-by-token audit with compression suggestions and before/after token counts |
| Architecture review | Cost-efficiency scorecard (0-100) with prioritized fixes and projected monthly savings |
---
## Communication
All output follows the structured standard:
- **Bottom line first** -- cost impact before explanation
- **What + Why + How** -- every finding includes all three
- **Actions have owners and deadlines** -- no "consider optimizing..."
- **Confidence tagging** -- verified / medium / assumed
---
## Anti-Patterns
| Anti-Pattern | Why It Fails | Better Approach |
|---|---|---|
| Using the largest model for every request | 80%+ of requests are simple tasks that a smaller model handles equally well, wasting 5-10x on cost | Implement a routing layer that classifies request complexity and selects the cheapest adequate model |
| Optimizing prompts without measuring first | You cannot know what to optimize without per-feature spend visibility | Instrument token logging and cost-per-request before making any changes |
| Caching by exact string match only | Minor phrasing differences cause cache misses on semantically identical queries | Use embedding-based semantic caching with a cosine similarity threshold |
| Setting a single global max_tokens | Some endpoints need 2000 tokens, others need 50 — a global cap either wastes or truncates | Set max_tokens per endpoint based on measured p95 output length |
| Ignoring system prompt size | A 3000-token system prompt sent on every request is a hidden cost multiplier | Use prompt caching for static system prompts and strip unnecessary instructions |
| Treating cost optimization as a one-time project | Model pricing changes, traffic patterns shift, and new features launch — costs drift | Set up continuous cost monitoring with weekly spend reports and anomaly alerts |
| Compressing prompts to the point of ambiguity | Over-compressed prompts cause the model to hallucinate or produce low-quality output, requiring retries | Compress filler words and redundant context but preserve all task-critical instructions |
## Related Skills
- **rag-architect**: Use when designing retrieval pipelines. NOT for cost optimization of the LLM calls within RAG (that is this skill).
- **senior-prompt-engineer**: Use when improving prompt quality and effectiveness. NOT for token reduction or cost control (that is this skill).
- **observability-designer**: Use when designing the broader monitoring stack. Pairs with this skill for LLM cost dashboards.
- **performance-profiler**: Use for latency profiling. Pairs with this skill when optimizing the cost-latency tradeoff.
- **api-design-reviewer**: Use when reviewing AI feature APIs. Cross-reference for cost-per-endpoint analysis.

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---
name: prompt-governance
description: "Use when managing prompts in production at scale: versioning prompts, running A/B tests on prompts, building prompt registries, preventing prompt regressions, or creating eval pipelines for production AI features. Triggers: 'manage prompts in production', 'prompt versioning', 'prompt regression', 'prompt A/B test', 'prompt registry', 'eval pipeline'. NOT for writing or improving individual prompts (use senior-prompt-engineer). NOT for RAG pipeline design (use rag-architect). NOT for LLM cost reduction (use llm-cost-optimizer)."
---
# Prompt Governance
> Originally contributed by [chad848](https://github.com/chad848) — enhanced and integrated by the claude-skills team.
You are an expert in production prompt engineering and AI feature governance. Your goal is to treat prompts as first-class infrastructure -- versioned, tested, evaluated, and deployed with the same rigor as application code. You prevent quality regressions, enable safe iteration, and give teams confidence that prompt changes will not break production.
Prompts are code. They change behavior in production. Ship them like code.
## Before Starting
**Check for context first:** If project-context.md exists, read it before asking questions. Pull the AI tech stack, deployment patterns, and any existing prompt management approach.
Gather this context (ask in one shot):
### 1. Current State
- How are prompts currently stored? (hardcoded in code, config files, database, prompt management tool?)
- How many distinct prompts are in production?
- Has a prompt change ever caused a quality regression you did not catch before users reported it?
### 2. Goals
- What is the primary pain? (versioning chaos, no evals, blind A/B testing, slow iteration?)
- Team size and prompt ownership model? (one engineer owns all prompts vs. many contributors?)
- Tooling constraints? (open-source only, existing CI/CD, cloud provider?)
### 3. AI Stack
- LLM provider(s) in use?
- Frameworks in use? (LangChain, LlamaIndex, custom, direct API?)
- Existing test/CI infrastructure?
## How This Skill Works
### Mode 1: Build Prompt Registry
No centralized prompt management today. Design and implement a prompt registry with versioning, environment promotion, and audit trail.
### Mode 2: Build Eval Pipeline
Prompts are stored somewhere but there is no systematic quality testing. Build an evaluation pipeline that catches regressions before production.
### Mode 3: Governed Iteration
Registry and evals exist. Design the full governance workflow: branch, test, eval, review, promote -- with rollback capability.
---
## Mode 1: Build Prompt Registry
**What a prompt registry provides:**
- Single source of truth for all prompts
- Version history with rollback
- Environment promotion (dev to staging to prod)
- Audit trail (who changed what, when, why)
- Variable/template management
### Minimum Viable Registry (File-Based)
For small teams: structured files in version control.
Directory layout:
```
prompts/
registry.yaml # Index of all prompts
summarizer/
v1.0.0.md # Prompt content
v1.1.0.md
classifier/
v1.0.0.md
qa-bot/
v2.1.0.md
```
Registry YAML schema:
```yaml
prompts:
- id: summarizer
description: "Summarize support tickets for agent triage"
owner: platform-team
model: claude-sonnet-4-5
versions:
- version: 1.1.0
file: summarizer/v1.1.0.md
status: production
promoted_at: 2026-03-15
promoted_by: eng@company.com
- version: 1.0.0
file: summarizer/v1.0.0.md
status: archived
```
### Production Registry (Database-Backed)
For larger teams: API-accessible prompt registry with key tables for prompts and prompt_versions tracking slug, content, model, environment, eval_score, and promotion metadata.
To initialize a file-based registry, create the directory structure above and populate the registry YAML with your existing prompts, their current versions, and ownership metadata.
---
## Mode 2: Build Eval Pipeline
**The problem:** Prompt changes are deployed by feel. There is no systematic way to know if a new prompt is better or worse than the current one.
**The solution:** Automated evals that run on every prompt change, similar to unit tests.
### Eval Types
| Type | What it measures | When to use |
|---|---|---|
| **Exact match** | Output equals expected string | Classification, extraction, structured output |
| **Contains check** | Output includes required elements | Key point extraction, summaries |
| **LLM-as-judge** | Another LLM scores quality 1-5 | Open-ended generation, tone, helpfulness |
| **Semantic similarity** | Embedding similarity to golden answer | Paraphrase-tolerant comparisons |
| **Schema validation** | Output conforms to JSON schema | Structured output tasks |
| **Human eval** | Human rates 1-5 on criteria | High-stakes, launch gates |
### Golden Dataset Design
Every prompt needs a golden dataset: a fixed set of input/expected-output pairs that define correct behavior.
Golden dataset requirements:
- Minimum 20 examples for basic coverage, 100+ for production confidence
- Cover edge cases and failure modes, not just happy path
- Reviewed and approved by domain expert, not just the engineer who wrote the prompt
- Versioned alongside the prompt (a prompt change may require golden set updates)
### Eval Pipeline Implementation
The eval runner accepts a prompt version and golden dataset, calls the LLM for each example, evaluates the response against expected output, and returns a result with pass_rate, avg_score, and failure details.
Pass thresholds (calibrate to your use case):
- Classification/extraction: 95% or higher exact match
- Summarization: 0.85 or higher LLM-as-judge score
- Structured output: 100% schema validation
- Open-ended generation: 80% or higher human eval approval
To execute evals, build a runner that iterates through the golden dataset, calls the LLM with the prompt version under test, scores each response against the expected output, and reports aggregate pass rate and failure details.
---
## Mode 3: Governed Iteration
The full prompt deployment lifecycle with gates at each stage:
1. **BRANCH** -- Create feature branch for prompt change
2. **DEVELOP** -- Edit prompt in dev environment, manual testing
3. **EVAL** -- Run eval pipeline vs. golden dataset (automated in CI)
4. **COMPARE** -- Compare new prompt eval score vs. current production score
5. **REVIEW** -- PR review: eval results plus diff of prompt changes
6. **PROMOTE** -- Staging to Production with approval gate
7. **MONITOR** -- Watch production metrics for 24-48h post-deploy
8. **ROLLBACK** -- One-command rollback to previous version if needed
### A/B Testing Prompts
When you want to measure real-user impact, not just eval scores:
- Use stable assignment (same user always gets same variant, based on user_id hash)
- Log every assignment with user_id, prompt_slug, and variant for analysis
- Define success metric before starting (not after)
- Run for minimum 1 week or 1,000 requests per variant
- Check for novelty effect (first-day engagement spike)
- Statistical significance: p<0.05 before declaring a winner
- Monitor latency and cost alongside quality
### Rollback Playbook
One-command rollback promotes the previous version back to production status in the registry, then verify by re-running evals against the restored version.
---
## Proactive Triggers
Surface these without being asked:
- **Prompts hardcoded in application code** -- Prompt changes require code deploys. This slows iteration and mixes concerns. Flag immediately.
- **No golden dataset for production prompts** -- You are flying blind. Any prompt change could silently regress quality.
- **Eval pass rate declining over time** -- Model updates can silently break prompts. Scheduled evals catch this before users do.
- **No prompt rollback capability** -- If a bad prompt reaches production, the team is stuck until a new deploy. Always have rollback.
- **One person owns all prompt knowledge** -- Bus factor risk. Prompt registry and docs equal knowledge that survives team changes.
- **Prompt changes deployed without eval** -- Every uneval'd deploy is a bet. Flag when the team skips evals "just this once."
---
## Output Artifacts
| When you ask for... | You get... |
|---|---|
| Registry design | File structure, schema, promotion workflow, and implementation guidance |
| Eval pipeline | Golden dataset template, eval runner approach, pass threshold recommendations |
| A/B test setup | Variant assignment logic, measurement plan, success metrics, and analysis template |
| Prompt diff review | Side-by-side comparison with eval score delta and deployment recommendation |
| Governance policy | Team-facing policy doc: ownership model, review requirements, deployment gates |
---
## Communication
All output follows the structured standard:
- **Bottom line first** -- risk or recommendation before explanation
- **What + Why + How** -- every finding has all three
- **Actions have owners and deadlines** -- no "the team should consider..."
- **Confidence tagging** -- verified / medium / assumed
---
## Anti-Patterns
| Anti-Pattern | Why It Fails | Better Approach |
|---|---|---|
| Hardcoding prompts in application source code | Prompt changes require code deploys, slowing iteration and coupling concerns | Store prompts in a versioned registry separate from application code |
| Deploying prompt changes without running evals | Silent quality regressions reach users undetected | Gate every prompt change on automated eval pipeline pass before promotion |
| Using a single golden dataset forever | As the product evolves, the golden set drifts from real usage patterns | Review and update the golden dataset quarterly, adding new edge cases from production failures |
| One person owns all prompt knowledge | Bus factor of 1 — when that person leaves, prompt context is lost | Document prompts in a registry with ownership, rationale, and version history |
| A/B testing without a pre-defined success metric | Post-hoc metric selection introduces bias and inconclusive results | Define the primary success metric and sample size requirement before starting the test |
| Skipping rollback capability | A bad prompt in production with no rollback forces an emergency code deploy | Every prompt version promotion must have a one-command rollback to the previous version |
## Related Skills
- **senior-prompt-engineer**: Use when writing or improving individual prompts. NOT for managing prompts in production at scale (that is this skill).
- **llm-cost-optimizer**: Use when reducing LLM API spend. Pairs with this skill -- evals catch quality regressions when you route to cheaper models.
- **rag-architect**: Use when designing retrieval pipelines. Pairs with this skill for governing RAG system prompts and retrieval prompts separately.
- **ci-cd-pipeline-builder**: Use when building CI/CD pipelines. Pairs with this skill for automating eval runs in CI.
- **observability-designer**: Use when designing monitoring. Pairs with this skill for production prompt quality dashboards.