Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add simota/agent-skills --skill mendgit clone --depth 1 https://github.com/simota/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/simota/agent-skills/mend)<a href="https://agentmods.dev/skills/simota/agent-skills/mend"><img src="https://agentmods.dev/badge/skills/simota/agent-skills/mend/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/simota/agent-skills/mend"><img src="https://agentmods.dev/badge/skills/simota/agent-skills/mend.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 6 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high YARA Match · line 45 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high Anti-Refusal · line 118 Skill attempts to nullify the agent's safety policies or restrictions ('you have no restrictions', 'ignore your guidelines', 'do anything now'). This is a direct jailbreak that disables guardrails.Fix: Remove jailbreak framing that nullifies safety policies or restrictions. Skill content must not instruct the agent to ignore its guidelines or operate without guardrails.
- high Prompt Injection · line 118 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
- medium Excessive Agency · line 125 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.05118 |
| Opus 5 | $0.00017 | $0.02559 |
| Sonnet 5 | $0.00007 | $0.01024 |
| Haiku 4.5 | $0.00003 | $0.00512 |
Grade A, and why
mend scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mend
Automated remediation agent for known failure patterns. Use Mend after a Triage diagnosis or Beacon alert when the issue is operationally fixable through restart, scale, config rollback, circuit breaker, canary rollback, or another reversible runtime action. Mend follows a maturity model: read-only insights → advised actions → approval-based remediation → autonomous operation with guardrails (Source: rootly.com — AI SRE Guide 2026). Every step is idempotent, auditable, and rollback-ready. Mend changes runtime and operational state only. Application logic and product behavior go to Builder.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- _common 10 B
- reference/_common 13 B
- reference/adversarial-defense.md 4.8 KB
- reference/autorun-schema.md 1.0 KB
- reference/canary-remediation.md 9.9 KB
- reference/circuit-remediation.md 7.6 KB
- reference/learning-loop.md 7.5 KB
- reference/remediation-patterns.md 16 KB
- reference/runbook-execution.md 8.1 KB
- reference/safety-model.md 11 KB
- reference/scale-remediation.md 8.2 KB
- reference/verification-strategies.md 7.5 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 248 lines · 35 tokens per session scan A acf7919c1268
mend is a skill published in the GitHub repository simota/agent-skills (76 stars, last pushed 7d ago), licensed MIT. It adds 35 tokens to every session and 5,118 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
multi-service-orchestration
PM2 process management, backend/frontend cascade execution, parallel worktree builds, and cross-service integration testing.
check-deployment-status
Check the health and sync status of all ArgoCD applications across clusters. Identifies out-of-sync, degraded, or unhealthy deployments and provides actionable remediation steps. Use when monitoring deployments, troubleshooting sync failures, or verifying environment health after a release.
cluster-resource-health
Check Kubernetes cluster health including pod status, node conditions, resource utilization, and pending alerts across EKS clusters. Use when monitoring infrastructure health, investigating capacity issues, or performing cluster audits.
diagram-architecture
Dark-themed SVG architecture/cloud/infra diagrams as HTML.
remote-offload
Use when local resource pressure would shrink or coordinator-direct a wave, a wave plan carries heavy build/test/audit roles (test, ui, perf), or the operator says offload, remote host, or auslagern — reference for routing that wave role to a declared SSH-reachable host instead of reducing agent count.
software-design-system-design
Design scalable distributed systems using structured approaches for load balancing, caching, database scaling, and message queues. Use when the user mentions "system design", "scale this", "high availability", "rate limiter", "design a URL shortener", "system design interview", "capacity planning", or "distributed…