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 seqra/opentaint --skill appsec-agentgit clone --depth 1 https://github.com/seqra/opentaintWrote 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/seqra/opentaint/appsec-agent)<a href="https://agentmods.dev/skills/seqra/opentaint/appsec-agent"><img src="https://agentmods.dev/badge/skills/seqra/opentaint/appsec-agent/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/seqra/opentaint/appsec-agent"><img src="https://agentmods.dev/badge/skills/seqra/opentaint/appsec-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00047 | $0.01968 |
| Opus 5.5 | $0.00019 | $0.00787 |
| Sonnet 5.5 | $0.00009 | $0.00394 |
| Haiku 4.5 | $0.00005 | $0.00197 |
Grade A, and why
appsec-agent 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 1mo 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AppSec Agent
Orchestrate an end-to-end OpenTaint security analysis. Keep the long project build and every full-project scan in this main session; delegate each bounded source, approximation, sink, triage, and PoC stage to an orchestrate-stage subagent, which owns its leaf fan-out and joins.
OpenTaint is a whole-program, interprocedural, field-sensitive alias analysis SAST. The run produces confirmed vulnerabilities plus reusable project-specific rules and approximations under one self-contained .opentaint/ directory at the project root.
Setup
1. Confirm the toolchain
Confirm opentaint is on PATH with opentaint -v. If it's missing, don't proceed silently — tell the user and offer the install command for their platform, run an install only on explicit confirmation:
- macOS / Linux, in order:
brew install --cask seqra/tap/opentaint·npm install -g @seqra/opentaint - Windows:
npm install -g @seqra/opentaint
After installing, run opentaint health to confirm everything's resolved.
2. Confirm agent nesting
This workflow requires two subagent levels: MAIN → stage orchestrator → leaf. Confirm the harness permits depth 2 before starting; otherwise ask the user to enable it.
3. Determine the language
Read the project's build files to fix the target language — Maven/Gradle → java, go.mod → go, and so on. Record it at bootstrap; stage orchestrators pass it to language-coupled leaves.
4. Choose the workflow
Ask the user for both levels together:
- Scan level —
lite·normal·deep- lite — build + scan (expected, when there are already existing artifacts)
- normal — build + scan + custom approximations
- deep — build + scan + custom approximations + custom rules
- recommend by what's on disk: a cold start (no
.opentaintartifacts) → deep; a prior run's artifacts already present → lite
- Triage level —
static·dynamic- static — classify findings from the model, no running app
- dynamic — static + PoC per confirmed TP. This launches a few test services on the user's machine (local instances and ports), torn down at the end of the run. Make that clear in the option
What ships with it
3 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.
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.
- 1mo ago Changed · +1 lines f065d6668970
- 1mo ago First seen · 148 lines · 47 tokens per session scan A f63e977514a9
appsec-agent is a skill published in the GitHub repository seqra/opentaint (164 stars, last pushed today), licensed Apache-2.0. It adds 47 tokens to every session and 1,968 once invoked, about $0.0002 per session on Opus 5.5. 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.
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