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Create a Model/System Card (MCARD) document through an interactive step-by-step flow. Guides the user through each section with specific questions and example responses.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
Execute an external audit of a Charter inside an auditor-side CLI (gemini-cli, claude-cli, copilot-cli, codex-cli). Reads the resolved audit prompt from the canonical location, audits with tool use, writes the report. Operator invokes one instance per CLI.
Generate the unified audit prompt for a Charter at the canonical filesystem location. The operator then opens N auditor-side CLIs (gemini-cli, claude-cli, copilot-cli, etc.) and invokes /straymark-audit-execute in each — no copy/paste. Counterpart of /straymark-audit-review.
Consolidate N external auditor reports into a critical review document with verdicts, remediation plan, and auditor ratings. Then merge the external_audit YAML block into the Charter telemetry. Counterpart of /straymark-audit-prompt and /straymark-audit-execute.
Execute an external audit of a Charter inside an auditor-side CLI (gemini-cli, claude-cli, copilot-cli, codex-cli). Reads the resolved audit prompt from the canonical location, audits with tool use, writes the report. Operator invokes one instance per CLI.
Generate the unified audit prompt for a Charter at the canonical filesystem location. The operator then opens N auditor-side CLIs (gemini-cli, claude-cli, copilot-cli, etc.) and invokes /straymark-audit-execute in each — no copy/paste. Counterpart of /straymark-audit-review.
Consolidate N external auditor reports into a critical review document with verdicts, remediation plan, and auditor ratings. Then merge the external_audit YAML block into the Charter telemetry. Counterpart of /straymark-audit-prompt and /straymark-audit-execute.
SOC 職業分類に基づく
| name | straymark-mcard |
| description | Create a Model/System Card (MCARD) document through an interactive step-by-step flow. Guides the user through each section with specific questions and example responses. |
Create AI Model/System Card documentation through an interactive guided flow.
When invoked, follow these steps:
If the user specified a model name (e.g., /straymark-mcard GPT-4o), use it as the model name and proceed to step 2 asking only for the model type.
If no parameter is given, proceed to step 2 asking for both model name and type.
Ask the user:
╔══════════════════════════════════════════════════════════════════╗
║ StrayMark MCARD — Model/System Card ║
╠══════════════════════════════════════════════════════════════════╣
║ ║
║ Step 1/7: Model Identity ║
║ ║
║ Please provide the following: ║
║ ║
║ 1. Model name: [e.g., "GPT-4o", "BERT-base-uncased"] ║
║ 2. Model type: ║
║ • LLM — Large Language Model ║
║ • classifier — Classification model ║
║ • regressor — Regression model ║
║ • generator — Generative model (image, audio, etc.) ║
║ • recommender — Recommendation system ║
║ • other — Specify ║
║ 3. Provider: [e.g., "OpenAI", "Google", "Hugging Face"] ║
║ 4. Version: [e.g., "2024-05-13", "v1.0", "gpt-4o-2024-05-13"] ║
║ 5. License: [e.g., "Proprietary", "Apache 2.0", "MIT"] ║
║ ║
╚══════════════════════════════════════════════════════════════════╝
Wait for user response before proceeding.
Ask the user:
╔══════════════════════════════════════════════════════════════════╗
║ Step 2/7: Intended Use ║
╠══════════════════════════════════════════════════════════════════╣
║ ║
║ 1. Primary intended uses: ║
║ Example: "Code generation", "Customer support chatbot", ║
║ "Sentiment analysis on product reviews" ║
║ ║
║ 2. Primary intended users: ║
║ Example: "Internal engineering team", "End users via API", ║
║ "Data science team" ║
║ ║
║ 3. Out-of-scope uses (what the model should NOT be used for): ║
║ Example: "Medical diagnosis", "Legal advice", ║
║ "Autonomous decision-making without human review" ║
║ ║
╚══════════════════════════════════════════════════════════════════╝
Wait for user response before proceeding.
Ask the user:
╔══════════════════════════════════════════════════════════════════╗
║ Step 3/7: Training Data ║
╠══════════════════════════════════════════════════════════════════╣
║ ║
║ 1. Dataset name: [e.g., "Common Crawl", "Internal corpus v3"] ║
║ 2. Source: [e.g., "Web scraping", "Licensed data provider"] ║
║ 3. Size: [e.g., "1.5T tokens", "500K samples", "200GB"] ║
║ 4. Collection methodology: ║
║ Example: "Web crawling with quality filters" ║
║ 5. Preprocessing steps: ║
║ Example: "Deduplication, PII removal, language filtering" ║
║ 6. Known limitations in the data: ║
║ Example: "English-centric, underrepresents African languages" ║
║ 7. PII assessment: ║
║ Example: "PII filtered using regex + NER; residual risk low" ║
║ 8. Data license: [e.g., "CC-BY-4.0", "Proprietary"] ║
║ ║
║ Type "unknown" or "N/A" for fields you cannot fill. ║
║ ║
╚══════════════════════════════════════════════════════════════════╝
Wait for user response before proceeding.
Ask the user:
╔══════════════════════════════════════════════════════════════════╗
║ Step 4/7: Performance & Evaluation ║
╠══════════════════════════════════════════════════════════════════╣
║ ║
║ Performance Metrics (provide at least one): ║
║ Format: Metric | Value | Test Dataset | Conditions ║
║ Example: "Accuracy | 92.3% | MMLU | 5-shot" ║
║ "F1 | 0.87 | SQuAD v2 | zero-shot" ║
║ "BLEU | 34.2 | WMT-22 en-de | beam search k=5" ║
║ ║
║ Disaggregated evaluation (optional): ║
║ Format: Subgroup | Metric | Value ║
║ Example: "English | Accuracy | 95.1%" ║
║ "Spanish | Accuracy | 88.4%" ║
║ ║
║ Type "unknown" if metrics are not available. ║
║ ║
╚══════════════════════════════════════════════════════════════════╝
Wait for user response before proceeding.
Ask the user:
╔══════════════════════════════════════════════════════════════════╗
║ Step 5/7: Bias, Security & Ethics ║
╠══════════════════════════════════════════════════════════════════╣
║ ║
║ Bias & Fairness (if evaluated): ║
║ Format: Group | Metric | Performance | Mitigation ║
║ Example: "Female | Toxicity | 0.02 | Content filter applied" ║
║ Type "not evaluated" if no bias analysis was performed. ║
║ ║
║ Security Concerns: ║
║ • Known vulnerabilities: [None / describe] ║
║ • Adversarial robustness: [Low / Medium / High] ║
║ • Prompt injection risk: [Low / Medium / High / N/A] ║
║ • Data poisoning risk: [Low / Medium / High] ║
║ • Model extraction risk: [Low / Medium / High] ║
║ ║
║ Ethical Considerations: ║
║ • Was sensitive data used in training? [Yes / No / Unknown] ║
║ • Human subjects involved? [Yes / No / Unknown] ║
║ • Dual-use potential? [Yes / No] ║
║ • Societal impact notes: [Brief description] ║
║ ║
╚══════════════════════════════════════════════════════════════════╝
Wait for user response before proceeding.
Ask the user:
╔══════════════════════════════════════════════════════════════════╗
║ Step 6/7: Environmental Impact ║
╠══════════════════════════════════════════════════════════════════╣
║ ║
║ 1. Training energy (kWh): [e.g., "1,287 kWh", "Unknown"] ║
║ 2. CO2 equivalent (tons): [e.g., "0.58 tCO2", "Unknown"] ║
║ 3. Hardware used: [e.g., "8x NVIDIA A100 80GB"] ║
║ 4. Training duration: [e.g., "72 hours", "Unknown"] ║
║ 5. Inference cost: [e.g., "$0.005 per 1K tokens", "Unknown"] ║
║ 6. Region / Grid carbon intensity: [e.g., "US-East", "Unknown"] ║
║ ║
║ Type "unknown" for fields you cannot fill. ║
║ ║
╚══════════════════════════════════════════════════════════════════╝
Wait for user response before proceeding.
Ask the user:
╔══════════════════════════════════════════════════════════════════╗
║ Step 7/7: Limitations & Recommendations ║
╠══════════════════════════════════════════════════════════════════╣
║ ║
║ Known limitations: ║
║ Example: "Poor performance on low-resource languages", ║
║ "Context window limited to 128K tokens", ║
║ "Hallucination rate ~5% on factual queries" ║
║ ║
║ Known failure modes: ║
║ Example: "Produces incorrect math on multi-step problems", ║
║ "May refuse safe queries due to over-filtering" ║
║ ║
║ Recommendations for deployers: ║
║ Example: "Implement output filtering for PII", ║
║ "Use human-in-the-loop for high-stakes decisions", ║
║ "Monitor for performance drift monthly" ║
║ ║
╚══════════════════════════════════════════════════════════════════╝
Wait for user response before proceeding.
Based on the gathered information, determine:
EU AI Act risk level: Classify based on intended use:
unacceptable: social scoring, real-time biometric identificationhigh: employment decisions, credit scoring, law enforcement, educationlimited: chatbots, emotion recognition, deepfake generationminimal: spam filters, game AI, search optimizationnot_applicable: no EU deployment plannedNIST GenAI risks: Select applicable risks from:
privacy, bias, confabulation, cbrn, dangerous_content, environmental, human_ai_config, information_integrity, information_security, intellectual_property, obscene_content, value_chain
Overall risk level: low, medium, high, or critical
Read .straymark/config.yml to determine language:
language: en # or es
Use template path based on language:
en (default): .straymark/templates/TEMPLATE-MCARD.mdes: .straymark/templates/i18n/es/TEMPLATE-MCARD.mdDetermine the next sequence number:
# Get current date
date +%Y-%m-%d
# Find existing MCARD documents for today
ls .straymark/09-ai-models/MCARD-$(date +%Y-%m-%d)-*.md 2>/dev/null | wc -l
ID format: MCARD-YYYY-MM-DD-NNN
Display a summary and ask for confirmation:
╔══════════════════════════════════════════════════════════════════╗
║ StrayMark MCARD — Summary ║
╠══════════════════════════════════════════════════════════════════╣
║ ║
║ Model: [model_name] ([model_type]) ║
║ Provider: [provider] — Version: [version] ║
║ EU AI Act Risk: [risk classification] ║
║ NIST GenAI Risks: [applicable risks] ║
║ Overall Risk: [risk level] ║
║ ║
║ Proposed filename: ║
║ MCARD-YYYY-MM-DD-NNN-[model-name-slug].md ║
║ ║
║ Location: ║
║ .straymark/09-ai-models/ ║
║ ║
║ Review required: YES ║
║ ║
╚══════════════════════════════════════════════════════════════════╝
Confirm creation? [Y/n]:
Wait for user confirmation before proceeding.
YYYY-MM-DD -> Current dateNNN -> Sequence number (001, 002, etc.)[agent-name] -> your runtime's canonical agent identity (see AGENT-RULES.md §1 — e.g. claude-code-v1.0, gemini-cli-v1.0, codex-cli-v1.0, cursor-v1.0; do not assume Claude)[Model Name] -> User-provided model namereview_required: true in the frontmatter.straymark/09-ai-models/MCARD-YYYY-MM-DD-NNN-[model-name-slug].mdAfter creation, display:
StrayMark MCARD created:
.straymark/09-ai-models/MCARD-YYYY-MM-DD-NNN-[description].md
Model: [model_name] ([model_type])
Provider: [provider]
Review required: yes
Risk level: [risk_level]
EU AI Act: [classification]
.straymark/09-ai-models/ directory: Create it before saving[To be determined][Information not publicly available] and note in limitations.straymark/config.yml: Default to English (en)Terminal compatibility: If the terminal does not support box-drawing characters (Unicode), use plain-text formatting with dashes and pipes instead (e.g.,
+--+instead of╔══╗).