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 agentmods add skills/seqra/opentaint/discover-attack-surfacenpx skills add seqra/opentaint --skill discover-attack-surfacegit clone --depth 1 https://github.com/seqra/opentaintWhat 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 | $0.00036 | $0.01643 |
| Opus 5 | $0.00018 | $0.00822 |
| Sonnet 5 | $0.00007 | $0.00329 |
| Haiku 4.5 | $0.00004 | $0.00164 |
Grade A, and why
discover-attack-surface 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 yesterday.
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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Discover Attack Surface
Work one assignment of project-used dependency members and pick out the taint sources among them — the methods where untrusted data first enters. The concrete inspection commands and value formats are language-specific — read references/<language>.md per Inputs and follow its numbered steps, which key to the ones below
Inputs
Provided by the caller, fall back to the default value when omitted. Ask back only when a required input is missing and has no sensible default
project-root(optional) — root of the target project. Opentaint keeps all analysis artifacts under the fixed<project-root>/.opentaint/directory, so every.opentaint/...path below resolves there. Default: current directorylanguage(required) — target language for this project and language-specific instructionsplan(required) — path to this agent's partition plan.opentaint/tracking/rules/plans/<id>.yaml: the project-used members to classify
Workflow
1. Settle built-in coverage first
Before anything, for each package the plan touches see what the built-in source rules already match for its members — opentaint health --rules prints the built-in rules root path; browse it and the project's own rules (per the language reference). This decides whether you write a source unit:
- full — existing rules already match the project-used sources → write no unit, stop, don't drill further
- partial — some project-used sources matched, others missed → plan only the missing used members
- none — plan the package's project-used sources from scratch
2. Classify the plan's members
The members are the FQNs under the plan's scopes — the project-used scope, already extracted, and only the members not yet classified in a prior run. Confirm each package's dependency identity and inspect its signatures/docs while classifying (per the language reference); read app source, dependency API/docs, and framework config to classify the listed members.
What ships with it
1 file 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.
- yesterday First seen · 98 lines · 36 tokens per session scan A 639698ea3091
discover-attack-surface is a skill published in the GitHub repository seqra/opentaint (149 stars, last pushed 2d ago), licensed Apache-2.0. It adds 36 tokens to every session and 1,643 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 2026-08-30.
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