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 trilwu/secskills --skill orchestrating-vulnerability-researchgit clone --depth 1 https://github.com/trilwu/secskillsWrote 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/trilwu/secskills/orchestrating-vulnerability-research)<a href="https://agentmods.dev/skills/trilwu/secskills/orchestrating-vulnerability-research"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/orchestrating-vulnerability-research/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/trilwu/secskills/orchestrating-vulnerability-research"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/orchestrating-vulnerability-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 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 Memory Poisoning · line 101 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium Excessive Agency · line 134 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00176 | $0.03182 |
| Opus 5 | $0.00088 | $0.01591 |
| Sonnet 5 | $0.00035 | $0.00636 |
| Haiku 4.5 | $0.00018 | $0.00318 |
Grade A, and why
orchestrating-vulnerability-research 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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- orchestrating-vulnerability-research — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrating Vulnerability Research
One agent hunting one target rationalizes. It finds a "probably exploitable" path, writes a confident paragraph, and grades its own paragraph as a finding. The paragraph is not the bug. This skill is the harness that stops that: give the hunt a bar it cannot talk its way around, split the target so pieces are worked in parallel, and never let the agent that built a candidate be the one that decides it is real.
It is a loop, not a pass. You run it until findings are proven or the target is genuinely exhausted — not until the first plausible writeup appears.
When to Use
- Told to find previously-unknown vulnerabilities in a whole codebase, a binary, or a named live target, with room to run many agents
- Running a bug-bounty or research campaign where depth and novelty matter more than a one-pass coverage report
- A single audit or test pass has stalled or produced only unproven "maybe" findings, and you want independent critics to break or confirm them
- You have the budget to fan out and iterate, and want the builder/critic separation and a demonstrated-trigger bar enforced across the whole effort
When NOT to Use
- One focused review of a source tree for coverage (client audit, one pass,
a deliverable coverage table) — use
auditing-code-for-vulnerabilitiesdirectly; this skill dispatches it, it does not replace it - Reversing or triaging a single binary — use
analyzing-binaries - Black-box testing one web app or API methodically — use
testing-web-applicationsortesting-apis - Writing up the confirmed findings — use
reporting-security-findings - Tracking the campaign's evidence, provenance, and dead ends — use
maintaining-engagement-state; this skill produces that record, it does not define its format - Hunting a webshell or backdoor someone already planted (not a latent
vulnerability) — use
hunting-web-backdoors - A stateless spot check — "is this one function injectable?" is one builder call, not a campaign. The harness overhead only pays off at scale.
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.
- 10d ago First seen · 250 lines · 176 tokens per session scan A 7cca26284cfe
orchestrating-vulnerability-research is a skill published in the GitHub repository trilwu/secskills (138 stars, last pushed 5d ago), licensed MIT. It adds 176 tokens to every session and 3,182 once invoked, about $0.0009 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 2026-08-30.
Other skills, from other repositories
claude-md-improver
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project…
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (auditcluster.sh), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…