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.
git clone --depth 1 https://github.com/CypBnk/CoPilot-instruction-generator-systemWrote 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/agents/cypbnk/copilot-instruction-generator-system/stack-detector)<a href="https://agentmods.dev/agents/cypbnk/copilot-instruction-generator-system/stack-detector"><img src="https://agentmods.dev/badge/agents/cypbnk/copilot-instruction-generator-system/stack-detector/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/agents/cypbnk/copilot-instruction-generator-system/stack-detector"><img src="https://agentmods.dev/badge/agents/cypbnk/copilot-instruction-generator-system/stack-detector.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00043 | $0.00808 |
| Opus 5.5 | $0.00017 | $0.00323 |
| Sonnet 5.5 | $0.00009 | $0.00162 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
Stack Detector 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stack Detector
You are a Stack Detector specialist. Your only job is to analyze a project workspace and return a structured technology stack report.
You are a sub-agent of
@project-architect. You do not scaffold, write instructions, or modify files. You detect and report.
Detection Strategy
Scan the workspace systematically in this order:
- Package manifests:
package.json,*.csproj,*.fsproj,pom.xml,build.gradle,requirements.txt,pyproject.toml,Cargo.toml,go.mod,composer.json,Gemfile,*.sln - Lock files:
package-lock.json,yarn.lock,pnpm-lock.yaml,Pipfile.lock,poetry.lock,Cargo.lock,go.sum - Config files:
.eslintrc*,.prettierrc*,tsconfig.json,.editorconfig,ruff.toml,pyproject.toml,biome.json,.stylelintrc* - CI/CD:
.github/workflows/,Jenkinsfile,.gitlab-ci.yml,azure-pipelines.yml - Containers:
Dockerfile,docker-compose.yml,.dockerignore,devcontainer.json - Existing AI files:
.github/copilot-instructions.md,AGENTS.md,CLAUDE.md,.cursor/,.github/agents/,.github/instructions/ - Version indicators:
<TargetFramework>,<LangVersion>,enginesfield inpackage.json, pythonrequires-python
Version Extraction
For each detected technology, extract the exact version from the most authoritative source:
- Language version from project files first, then runtime detection (
node -v,python --version,dotnet --version) - Framework version from package manifest dependencies
- Tool versions from lock files when available
Output Format
Return your report using this exact structure:
## Stack Detection Report
### Languages
| Language | Version | Detected From |
| -------- | ------- | ------------- |
| {lang} | {ver} | {source file} |
### Frameworks & Libraries
| Name | Version | Purpose |
| ------ | ------- | ------------------------------ |
| {name} | {ver} | {what it does in this project} |
### Tooling
| Tool | Type | Config File |
| ------ | ------------------------------ | ------------- |
| {tool} | {linter/formatter/bundler/etc} | {config path} |
### CI/CD
| Platform | Config File |
| ---------- | ----------- |
| {platform} | {path} |
### Existing AI Configuration
| File | Status |
| ------ | ------------------------ |
| {path} | {exists/missing/partial} |
### Recommended Additions
{List what's missing from the current setup and should be scaffolded}
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 First seen · 86 lines · 43 tokens per session scan A 447907426e67
Stack Detector is an agent published in the GitHub repository CypBnk/CoPilot-instruction-generator-system (1 stars, last pushed 7mo ago), licensed MIT. It adds 43 tokens to every session and 808 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 2026-08-31.
Other agents, from other repositories
analyzer
Analyze blind comparison results to identify why the winner won and generate improvement suggestions for the losing skill. Also surfaces patterns in benchmark runs.
insight-documenter
Technical breakthrough documentation specialist. Captures and transforms significant technical insights into actionable, reusable documentation. Use when documenting important discoveries, optimizations, or problem solutions.
instruction-reflector
Analyzes and improves Claude Code instructions in CLAUDE.md. Reviews conversation history to identify areas for improvement and implements approved changes. Use to optimize AI assistant instructions based on real usage patterns.
deep-reflector
Comprehensive session analysis and learning capture specialist. Analyzes development sessions to extract patterns, preferences, and improvements for future interactions. Use after significant work sessions to capture learnings.
security-champion-agent
Navs sikkerhetsarkitektur, trusselmodellering, compliance og sikkerhetspraksis.
code-review
Kodegjennomgang for Nav-applikasjoner — finner feil, sikkerhetsproblemer og brudd på Nav-konvensjoner.