Agent skill

Create Test Project

by seqra in seqra/opentaint

Create an OpenTaint test project with positive/negative samples for verifying a rule or approximation.

Apache-2.0Auto-check passedSecurity

Install Create Test Project

skills CLI
$ npx skills add seqra/opentaint --skill create-test-project -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install seqra/opentaint create-test-project --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/seqra/opentaint.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/create-test-project .claude/skills/create-test-project && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
create-test-project
GitHub stars
162
Token cost
~2.3k tokens
SKILL.md length
1,075 words
Files
4 (incl. references)
Skills in repo
16
Repo updated
First seen
Licence
Apache-2.0

At a glance

Create an OpenTaint test project with positive/negative samples for verifying a rule or approximation.

  • Works in 4 steps: Scaffold the project → Write the samples → Compile to the model → …
  • Approximation needs a test project to check against
  • SKILL.md covers Inputs, Workflow, Output and Tracking, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Create Test Project is an agent skill from seqra/opentaint. Create an OpenTaint test project with positive/negative samples for verifying a rule or approximation. Use when a rule or approximation needs a test project to check against

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/java-approximation.md`, `references/java-rule.md` and `references/java-spring-multimodule.md`).

It sits in Security, covering Static analysis and SAST. The repository describes itself as: The open source taint analysis engine for the AI era. A formal dataflow analysis tool you can customize and self-host, built so AI agents drive your application security analysis… The licence is Apache-2.0.

When your agent uses it

  • Approximation needs a test project to check against
  • Tasks that involve Static analysis and SAST

Example prompts

  • “/create-test-project”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Scaffold the project
  2. Write the samples
  3. Compile to the model
  4. Escalate

What it can do on your machine

Read from SKILL.md and the folder at commit f945f92. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Create Test Project loads about 2.3k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 48 tokens; SKILL.md has 1,075 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~48
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from seqra/opentaint at commit f945f92, republished under its Apache-2.0 licence (© seqra). 1,075 words, ~2,310 tokens.

Download SKILL.mdSave it as .claude/skills/create-test-project/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
create-test-project
description
Create an OpenTaint test project with positive/negative samples for verifying a rule or approximation. Use when a rule or approximation needs a test project to check against
license
Apache-2.0
metadata.author
opentaint
metadata.version
0.3.0

Skill: Create Test Project

Build a minimal compiled test project whose samples reproduce the flow a rule or approximation is verified against. A sample routes data between a real library method and the generic taint marker the scaffold provides, with the one verdict it must produce — a positive that must flag, a negative that must not. The compiled model is the deliverable; its sample sources sit alongside it.

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
  • type (required) — what this project verifies, selecting the sample style and the identifying inputs below: rule-source, rule-sink, or dataflow
  • for rule-source / rule-sink — unit: the unit id; its members and dependencies come from .opentaint/tracking/rules/sources|sinks/<unit>.yaml
  • for dataflow — batch: the batch whose .opentaint/tracking/approximations/<batch>.yaml provides the dataflow methods to exercise and their dependencies

The project folder <name> is that identifier — the unit for a rule side, the batch for a dataflow approximation.

Workflow

1. Scaffold the project

The scaffold command and sample form are language-specific — read the reference for your type: references/<lang>-rule.md for rule-source / rule-sink, or references/<lang>-approximation.md for dataflow (one self-contained reference, no need to read the other). Scaffold the project for the type, passing each of the unit's dependencies at its pinned version. The scaffold provides the generic taint marker and the fixed test rule the samples run against, so you author only the samples. If the <name> project already exists — re-invoked because its surface grew or a dependency moved — extend it instead: add the missing samples and recompile rather than scaffolding fresh. The init command is in that reference.

2. Write the samples

For a unit or batch, each entry records its signature; shape a faithful sample from how that callable is really used in the project. The app's real path is irrelevant, only that data flows between the boundary and the marker:

For a sink unit, the methods are nested under groups[].sinks, exercise every method while preserving the group boundaries for the later rule author.

  • the counterpart is always the generic marker, never a real source/sink, so the sample exercises only the unit under test
  • register each sample under the single verdict it must produce — a positive that must flag, and, where the type calls for it, a negative that must not — in the test's rule-test.yaml

The sample code, the rule-test.yaml form, and which verdicts a type needs are in that reference.

3. Compile to the model

Compile the project you built to .opentaint/test-compiled/<name> — a rule side compiles the one sub-project you scaffolded (sources/ or sinks/) to the matching sub-model, a dataflow project compiles once:

bash
# rule side — the one you built
opentaint compile .opentaint/test-projects/<name>/sources -o .opentaint/test-compiled/<name>/sources
# dataflow
opentaint compile .opentaint/test-projects/<name> -o .opentaint/test-compiled/<name>

A clean compile is the deliverable. Feedback loop: a build failure is a fixable samples-or-dependencies problem — surface the real error, fix it, and recompile. On a clean compile set the test-project stage done (per Tracking).

4. Escalate

When a project won't compile after ~3 fixes with no clear cause → report the failure and leave the test-project stage pending, for the orchestrator to intervene.

Output

Short and concise report of what was done

Artifacts
  • .opentaint/test-compiled/<name> — the compiled test model a later stage runs against; report each path and the exact compile command used
  • .opentaint/test-projects/<name> — the sample sources and the rule-test.yaml that records their verdicts, alongside the model
Summary
  • the number of samples written per case
  • any method excluded because no sample could be written (marked failed), with a brief reason
  • failed projects (if any)
Show full SKILL.md (466 more words)Show less

Tracking

This skill writes only the test-project stage back:

  • a rule side → stages.test_project: done in the source or sink unit
  • a dataflow approximation → one build.test_project entry per method in the batch file, status: done for a method whose sample made it into the project, status: failed for one no sample could be written for (excluded)

.opentaint/tracking/rules/sources/<package-kebab>.yaml — one source unit per package (a dependency can span several packages), the file named for that package with . → -. tag is always the reusable untrusted-data-source group. dependencies names the dependency the package comes from, sources each an entry point { method, signature, note, rule_id } (method and signature use the exact language-specific identifiers from the plan), stages tracks the unit through rule authoring, and a blocker string is added under it when the unit can't be made to pass. Keep it clear from comments

yaml
dependencies:
  - <dependency-id>
tag: untrusted-data-source
sources:
  - { method: "<qualified-member>", signature: "<language-signature>", note: untrusted message payload, rule_id: null }
stages:
  test_project: pending
  tests_passing: pending

.opentaint/tracking/rules/sinks/<package-kebab>.yaml — one sink unit per package (a dependency can span several packages, each its own unit), the file named for that package with . → -. dependencies names the dependency the package comes from. groups partitions the package's dangerous operations by reusable sink tag; each group is { tag, sinks }, and each sink is { method, signature, note, rule_id }. The tag belongs to the group, never to an individual method: one rule may cover several methods and several rules may extend the same vulnerability family. method and signature use the exact language-specific identifiers from the plan. stages tracks the unit through rule authoring. Keep it clear from comments

yaml
dependencies:
  - <dependency-id>
groups:
  - tag: path-traversal-sink
    sinks:
      - { method: "<qualified-member>", signature: "<language-signature>", note: writes data to an untrusted path, rule_id: null }
stages:
  test_project: pending
  tests_passing: pending

.opentaint/tracking/approximations/<batch>.yaml — one batch's callable classification, <batch> the plan's filename stem. Every callable sits in exactly one verdict bucket, keyed with the exact language-specific method and signature from the plan so distinct variants stay separate:

  • passthrough, dataflow — modeled carriers; each entry { method, signature }
  • skipped — terminal non-carriers; each { method, signature, reason }
  • engine_issues — a separate bucket for carriers the engine provably can't propagate (built but still dropped); each { method, signature, reason }. Terminal and treated just like skipped — the only difference is the reason. merge-skipped carries it into skipped.yaml as its own engine_issues group alongside the regular skipped methods.

dependencies lists the dependency identifiers a dataflow test project needs. The build block tracks the build — test_project records each dataflow method's test-project status (done if a sample was written into the batch's test project, failed if none could be written so the method was excluded from it), and done holds the finished { method, signature }. Keep it clear from comments

yaml
passthrough:
  - { method: "<qualified-member-a>", signature: "<language-signature-a>" }
dataflow:
  - { method: "<qualified-member-b>", signature: "<language-signature-b>" }
skipped:
  - { method: "<qualified-member-c>", signature: "<language-signature-c>", reason: "retains none of its input data" }
engine_issues: []
dependencies: []
build:
  test_project:
    - { method: "<qualified-member-b>", signature: "<language-signature-b>", status: done }
  done: []

Constraints

OpenTaint is a whole-program, interprocedural, field-sensitive alias analysis engine. It already propagates through visible application code, calls, aliases, and individual fields; custom rules and approximations model only the assigned source, sink, or opaque-method boundary. Compile-time constants and literals carry no taint, so a source or carrier whose output is only a constant introduces nothing.

  • One <name> folder per unit — never write into another unit's test project, so concurrent agents don't race

© seqra, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in skills/create-test-project of seqra/opentaint.

  • SKILL.md
  • references/java-approximation.md
  • references/java-rule.md
  • references/java-spring-multimodule.md

Open the folder on GitHubat commit f945f92

Compare with similar skills

Create Test Project next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Semgrep Security Scantrailofbits/skills7.4k—~3.7kAutomated safety check: NotesCC-BY-SA-4.0
LLM Sast ScannerSunWeb3Sec/llm-sast-scanner286—~6.2kAutomated safety check: PassNone
Sast SemgrepAgentSecOps/SecOpsAgentKit2192 repos~2.4kAutomated safety check: PassCustom licence

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Categories

Questions about Create Test Project

What does Create Test Project do?

Create an OpenTaint test project with positive/negative samples for verifying a rule or approximation. Create Test Project is an agent skill from seqra/opentaint. Create an OpenTaint test project with positive/negative samples for verifying a rule or approximation.

When should I use Create Test Project?

Create Test Project fits situations like: approximation needs a test project to check against; tasks that involve Static analysis and SAST.

How do I install Create Test Project in Claude Code?

Run `npx skills add seqra/opentaint --skill create-test-project -a claude-code`. Or copy the skill folder (skills/create-test-project in seqra/opentaint) into .claude/skills/create-test-project in your project. Claude Code loads it when a task matches its description.

How do I install Create Test Project in Codex?

Run `npx skills add seqra/opentaint --skill create-test-project -a codex`. Or copy the skill folder (skills/create-test-project in seqra/opentaint) into .agents/skills/create-test-project in your project. Codex loads it when a task matches its description.

Can I use Create Test Project in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add seqra/opentaint --skill create-test-project -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/create-test-project, .gemini/skills/create-test-project, .github/skills/create-test-project and .opencode/skills/create-test-project in your project.

What does Create Test Project need to run?

SKILL.md names no scripts, command-line tools or credentials: Create Test Project is instructions for the agent only.

Does Create Test Project access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Create Test Project safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Create Test Project use?

Create Test Project is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Create Test Project use?

About 2.3k tokens (SKILL.md is roughly 9.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.9k tokens, read only when the agent opens those files.

What are the alternatives to Create Test Project?

Skills that share tags, products or a category with Create Test Project: Semgrep (vigolium/piolium, 138 stars), C To Ast (Narwhal-Lab/MagicSkills, 316 stars), Semgrep Security Scan (trailofbits/skills, 7.4k stars) and LLM Sast Scanner (SunWeb3Sec/llm-sast-scanner, 286 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Create Test Project?

seqra (a GitHub organization) maintains it in seqra/opentaint, which has 162 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 7, 2026.

Source: seqra/opentaint on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.