create-pass-through-approximation

A tool for describing simple data copying through a method that a security analyzer cannot inspect. The description tells the analyzer which input data reaches which output.

In plain words
What is it for?
Use it to model dropped methods whose output directly passes along data from their inputs, using a selected batch of methods or the whole available group.
Why use it?
It restores a simple data path that the analyzer lost because the method was opaque to it.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/seqra/opentaint/create-pass-through-approximation
Any agent
npx skills add seqra/opentaint --skill create-pass-through-approximation
Clone the repo
git clone --depth 1 https://github.com/seqra/opentaint

Made for: Claude Code, Codex.

Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,487 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash b6ce6a09eb3c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

skills/create-pass-through-approximation/SKILL.md · 85 lines

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 directory
  • language (required) — target language for this project and language-specific instructions
  • batch (required) — the batch id; its passthrough entries live in .opentaint/tracking/approximations/<batch>.yaml, and you append the ones you build to that file's build.done
  • methods (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:

Read the full file on GitHub · 85 lines

Files

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.

Changes

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.

  1. 2d ago First seen · 85 lines · 30 tokens per session scan A b6ce6a09eb3c

Subscribe to this mod's changes

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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