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/create-pass-through-approximationnpx skills add seqra/opentaint --skill create-pass-through-approximationgit 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 | $0.00030 | $0.01487 |
| Opus 5 | $0.00015 | $0.00744 |
| Sonnet 5 | $0.00006 | $0.00297 |
| Haiku 4.5 | $0.00003 | $0.00149 |
Grade A, and why
create-pass-through-approximation 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 2d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Create PassThrough Approximation
Model a dropped method's taint propagation as a passThrough approximation — a config that tells the engine how data moves from a method's inputs to its outputs, so a flow the analyzer lost through the opaque call is restored.
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 instructionsbatch(required) — the batch id; itspassthroughentries live in.opentaint/tracking/approximations/<batch>.yaml, and you append the ones you build to that file'sbuild.donemethods(optional) — a specific{ method, signature }subset to (re)work instead of the whole passthrough bucket, when the caller needs only those
Workflow
1. Understand the propagation
Take the batch's passthrough methods not yet in build.done (or the specific methods you were handed) and study each from its real code: read the source (per the language reference). Model each method purely from what its own code does, independent of how the project uses it — the config describes the method's intrinsic propagation. Answer: where does the input data go? Data that arrives on the receiver or an argument — does it come back out, through the return value, an argument the method writes into, the receiver, or an object or field it stores into? Note too whether the object holds the data between calls (a setter stashes it and a getter hands it back later, or a builder accumulates it) — that needs a virtual field. That shape is what the config expresses.
2. Write the config
Write one passThrough config per package under .opentaint/pass-through (the format and patterns are in the language reference). A method already in build.done and not explicitly handed in methods is built and trusted — leave it and its config as-is. Repair an explicitly handed method in its existing config; add a new method to its existing package config rather than rewriting the file. Two ideas drive the copy:
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.
- 2d ago First seen · 85 lines · 30 tokens per session scan A b6ce6a09eb3c
create-pass-through-approximation is a skill published in the GitHub repository seqra/opentaint (149 stars, last pushed 4d ago), licensed Apache-2.0. It adds 30 tokens to every session and 1,487 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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