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◆SkillAgentic capabilityFree

Ponytail

Makes your AI agent think like the laziest senior dev — the best code is the code you never wrote (YAGNI-first).

@ai-supply
Instalaciones30k
⟳ upstream v4.8.4 · updated 1mo ago
↗ Repositorio fuente
← More Agentic capabilityAgentic capability leaderboard →How we grade security →Source ↗
✓ Grade A · 100/100 · SafeSecurity assessment
✓No compromise signals12capabilities surfaced8of 20 OWASP controls clear
Suspicious network referencesBroad capability surfaceBroad capability surfaceInternal host / private infrastructure reference
scanned 24d ago·osv · gitleaks · opengrep · picklescan + heuristics·full breakdown in the Security tab ↓

Ponytail

Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote. Before writing anything, the agent stops at the first rung that holds:

  1. Does this need to exist? → no: skip it (YAGNI)
  2. Stdlib does it? → use it
  3. Native platform feature? → use it
  4. Installed dependency? → use it
  5. One line? → one line
  6. Only then: the minimum that works

Lazy, not negligent — trust-boundary validation, data-loss handling, security and accessibility are never on the chopping block.

Reported impact vs a no-skill agent: 80–94% less code, 47–77% less cost, 3–6× faster across models.

Install

Portable across Claude Code, Codex, Gemini CLI, OpenCode and more (SKILL-based). With the skills CLI:

npx skills add DietrichGebert/ponytail

Or add it via your agent's plugin/marketplace system. See the repository for exact per-client steps.

  • Repo: https://github.com/DietrichGebert/ponytail
  • License: MIT
Rating rank
#1
of 35 in Agentic capability
Install rank
#33
of 35 in Agentic capability
Security score
100/100 · A
safe
Security rank
#1
of 35 in Agentic capability
Installs
30k
cat avg 186k
This listing vs category average
Installs
this
cat avg
Security (of 100)
this
cat avg
Adoption trend
See the Agentic capability leaderboard →
✓ Security: Safe · 100100/100 · grade Ascanned 24d ago
✓ no compromise signals12 risk-surface · 6/20 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.

What this capability can do · high confidence (static)
Tools (1)
ponytail_instructions
⚑ filesystem⚑ shell⚑ network⚑ secrets
egress → anthropic.com, img.shields.io, trendshift.io, ponytail.dev, greenpt.com, www.star-history.com, api.star-history.com, kuldeepb19.github.io +6
skill: ponytail-audit12 scripts

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, except a known CVE, which carries a small bounded penalty (high/critical → Review).

OWASP Top 10 for LLM Applications
⚠LLM05Improper Output Handlinghigh
Code that pipes model/user output into shell, eval, SQL or paths unsafely.
•Suspicious code patterns — OS command execution · DietrichGebert-ponytail-14a0d79/benchmarks/agentic/run.py (CWE-78)expected
•Suspicious code patterns — OS command execution; environment/secret exfiltration · DietrichGebert-ponytail-14a0d79/benchmarks/agentic/tasks.py (CWE-78)expected
•Suspicious code patterns — child_process exec; dynamic code execution · DietrichGebert-ponytail-14a0d79/benchmarks/correctness.js (CWE-78)expected
•Suspicious code patterns — child_process exec · DietrichGebert-ponytail-14a0d79/scripts/publish-openclaw-skills.js (CWE-78)expected
⚠LLM07System Prompt Leakagemedium
Secrets, internal hosts or proprietary logic exposed in shipped prompts.
•Internal host / private infrastructure reference — shipped content references a private IP range or internal-only host · DietrichGebert-ponytail-14a0d79/benchmarks/robustness-audit.js (CWE-200)risk surface
⚠LLM03Supply Chainlow
Vulnerable/compromised dependencies, models or archives in the artifact.
•Dependency manifest — 2 npm dependencies declared · DietrichGebert-ponytail-14a0d79/ponytail-mcp/package.jsonrisk surface
⚠LLM06Excessive Agencylow
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 1 distinct host(s) · DietrichGebert-ponytail-14a0d79/.agents/plugins/marketplace.jsonexpected
•External endpoints declared — 2 distinct host(s) · DietrichGebert-ponytail-14a0d79/.claude-plugin/marketplace.jsonexpected
•External endpoints declared — 7 distinct host(s) · DietrichGebert-ponytail-14a0d79/README.es.mdexpected
•Broad capability surface — 4 high-impact capability categories referenced — verify least-privilege · DietrichGebert-ponytail-14a0d79/benchmarks/agentic/tasks.py (CWE-272)risk surface
•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege · DietrichGebert-ponytail-14a0d79/benchmarks/claude-email.js (CWE-272)risk surface
•External endpoints declared — 4 distinct host(s) · DietrichGebert-ponytail-14a0d79/benchmarks/robustness-audit.jsexpected
§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
✓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.
✓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
⚠ML09Output Integrityhigh
Middleware tampering with model outputs in transit.
Gateway enforces TLS + response integrity; static check flags output-rewriting code.
•Suspicious code patterns — OS command execution · DietrichGebert-ponytail-14a0d79/benchmarks/agentic/run.py (CWE-78)expected
•Suspicious code patterns — OS command execution; environment/secret exfiltration · DietrichGebert-ponytail-14a0d79/benchmarks/agentic/tasks.py (CWE-78)expected
•Suspicious code patterns — child_process exec; dynamic code execution · DietrichGebert-ponytail-14a0d79/benchmarks/correctness.js (CWE-78)expected
•Suspicious code patterns — child_process exec · DietrichGebert-ponytail-14a0d79/scripts/publish-openclaw-skills.js (CWE-78)expected
⚠ML06AI Supply Chainlow
Compromised PyPI/npm packages, typosquats, unsafe serialized models.
•Dependency manifest — 2 npm dependencies declared · DietrichGebert-ponytail-14a0d79/ponytail-mcp/package.jsonrisk surface
§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.
✓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.
✓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 (7) · hygiene / uncategorized
•Unrecognized file type — '.mdc' is not on the allowlist · DietrichGebert-ponytail-14a0d79/.cursor/rules/ponytail.mdcrisk surface
•Unrecognized file type — '.gitignore' is not on the allowlist · DietrichGebert-ponytail-14a0d79/.gitignorerisk surface
•Unrecognized file type — '.cjs' is not on the allowlist · DietrichGebert-ponytail-14a0d79/.opencode/plugins/ponytail-frontmatter.cjsrisk surface
•Unrecognized file type — '.mjs' is not on the allowlist · DietrichGebert-ponytail-14a0d79/.opencode/plugins/ponytail.mjsrisk surface
•Unrecognized file type — '.?' is not on the allowlist · DietrichGebert-ponytail-14a0d79/LICENSErisk surface
•Suspicious network references — raw IP URL (2 URLs) · DietrichGebert-ponytail-14a0d79/benchmarks/agentic/tasks.pyrisk surface
•Disallowed file type — '.ps1' executables are not permitted · DietrichGebert-ponytail-14a0d79/hooks/ponytail-statusline.ps1 (CWE-434)risk surface
✔ verified source · pinned DietrichGebert-ponytail-14a0d79
Check against a policy

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

Consume Ponytail 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/ponytail

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

# CLI
npx ai-supply add ponytail

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

# MCP tool
install_listing({ "slug": "ponytail" })
OpenAPI spec →
vlatest
✓ Security: Safe · 1001mo ago

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

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