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❝PromptLanguage & NLPFree

ChatGPT System Prompts

Curated collection of high-quality system prompts for ChatGPT, organized by role and use case.

@ai-supply
Installs710
↗ Source repository
← More Language & NLPLanguage & NLP leaderboard →How we grade security →Source ↗

ChatGPT System Prompts

A curated collection of high-quality system prompts for ChatGPT and other conversational LLMs, organized by role and use case (teachers, developers, assistants, entertainers, and more).

Each entry is a ready-to-paste system message that steers the model into a specific persona or behavior, making it a handy reference for prompt engineers and anyone bootstrapping a new assistant.

Licensed MIT and dependency-free — it is plain markdown, so it is easy to browse, fork, and adapt into your own prompt library.

Rating rank
#1
of 30 in Language & NLP
Install rank
#28
of 30 in Language & NLP
Security score
100/100 · A
safe
Security rank
#1
of 30 in Language & NLP
Installs
710
cat avg 145k
This listing vs category average
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✓ Security: Safe · 100100/100 · grade Ascanned 14h ago
✓ no compromise signals0 risk-surface · 0/20 OWASP controls flagged

Compromise signals — malicious or tampered code (leaked secrets, backdoors, a dropped executable) — reduce the score, and known dependency CVEs carry a bounded penalty (they warrant review but never QUARANTINE — update the dependency to clear). Other dangerous-by-capability traits are risk surface, expected for some capabilities. Every finding is mapped to its OWASP control below.

Prompt card · low confidence (static)
no template variables detected

Findings mapped to the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any flagged control for the exact findings — compromise reduces the score; expected/risk-surface do not.

OWASP Top 10 for LLM Applications
§LLM09MisinformationGovernance
Artifacts designed to produce false/deceptive output.
Detectable only by runtime behavioral evaluation; addressed via responsible-use attestation.
◷LLM10Unbounded ConsumptionRuntime-enforced
Unbounded loops/recursion causing DoS or runaway cost.
Enforced at runtime by the gateway (rate limits + spend caps + size caps); static check flags unbounded loops.
✓LLM01Prompt InjectionPassed
✓LLM02Sensitive Information DisclosurePassed
✓LLM03Supply ChainPassed
✓LLM04Data and Model PoisoningPassed
Backdoors/poisoning in training data or serialized models.
Behavioral poisoning needs model execution; static check covers unsafe serialization + dataset skew only.
✓LLM05Improper Output HandlingPassed
✓LLM06Excessive AgencyPassed
✓LLM07System Prompt LeakagePassed
✓LLM08Vector and Embedding WeaknessesPassed
PII or plaintext source leakage in embedding/vector exports.
Embedding inversion/poisoning is largely runtime; static check covers PII in vector exports.
OWASP Machine Learning Security Top 10
§ML01Input Manipulation (Adversarial)Governance
Models vulnerable to adversarial perturbations.
Requires runtime robustness evaluation; addressed via publisher robustness attestation.
§ML03Model InversionGovernance
Training data reconstructable from a model's outputs.
Runtime/evaluation property; addressed via model-card data-provenance + DP attestation.
§ML04Membership InferenceGovernance
Determining whether a record was in the training set.
Runtime/evaluation property; addressed via overfitting disclosure + DP attestation.
§ML08Model SkewingGovernance
Models trained on skewed data producing biased output.
Requires fairness evaluation; addressed via model-card bias/limitations disclosure.
◷ML09Output IntegrityRuntime-enforced
Middleware tampering with model outputs in transit.
Gateway enforces TLS + response integrity; static check flags output-rewriting code.
✓ML02Data PoisoningPassed
Poisoned training datasets with triggers or anomalous distributions.
Static check covers trigger phrasing, PII and label skew; full poisoning detection is runtime.
✓ML05Model TheftPassed
Unlicensed re-distribution / license-incompatible derivatives.
Static check verifies license declaration; extraction throttling is runtime.
✓ML06AI Supply ChainPassed
✓ML07Transfer Learning AttackPassed
Backdoored base models / LoRA adapters propagating to derivatives.
Backdoor detection needs behavioral probing; static check covers unsafe serialization + provenance.
✓ML10Model Poisoning (Weights)Passed
Tampered model weight files; integrity must be verifiable.
Static check enforces safe formats + records a content hash for downstream verification.
Other findings (1) · hygiene / uncategorized
•Unrecognized file type — '.?' is not on the allowlist · mustvlad-ChatGPT-System-Prompts-e19d82a/LICENSErisk surface
✔ verified source · pinned mustvlad-ChatGPT-System-Prompts-e19d82a
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/chatgpt-system-prompts/check). Click a policy:

Consume ChatGPT System Prompts programmatically. Authenticate with an API key or session — see Authorize an agent.

# Agents: CHECK BEFORE YOU INSTALL (no auth) — score, grade, level, capability manifest
curl https://ai-supply.store/api/v1/trust/chatgpt-system-prompts

# Gate against your org policy (returns { pass, violations })
curl -X POST https://ai-supply.store/api/v1/trust/chatgpt-system-prompts/check \
  -H "Content-Type: application/json" \
  -d '{"minGrade":"B","denyPermissions":["shell"],"denyUnknownEgress":true}'

# CLI
npx ai-supply add chatgpt-system-prompts

# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/chatgpt-system-prompts/install \
  -H "Authorization: Bearer $AIM_KEY"

# MCP tool
install_listing({ "slug": "chatgpt-system-prompts" })
OpenAPI spec →
vlatest
✓ Security: Safe · 10015h ago

Curated mirror — latest upstream source. See the repository for tagged releases.

Sign in and install this listing to leave a review.

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