Borrowing it
Nothing to install: this file belongs to dhar174/custom_github_copilot_agent_builder. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dhar174/custom_github_copilot_agent_builder/main/.github/agents/technical-content-evaluator.agent.mdgit clone --depth 1 https://github.com/dhar174/custom_github_copilot_agent_builderWrote 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/dhar174/custom_github_copilot_agent_builder/technical-content-evaluator)<a href="https://agentmods.dev/agents/dhar174/custom_github_copilot_agent_builder/technical-content-evaluator"><img src="https://agentmods.dev/badge/agents/dhar174/custom_github_copilot_agent_builder/technical-content-evaluator/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/dhar174/custom_github_copilot_agent_builder/technical-content-evaluator"><img src="https://agentmods.dev/badge/agents/dhar174/custom_github_copilot_agent_builder/technical-content-evaluator.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 | $0.00046 | $0.05480 |
| Opus 5.5 | $0.00018 | $0.02192 |
| Sonnet 5.5 | $0.00009 | $0.01096 |
| Haiku 4.5 | $0.00005 | $0.00548 |
Grade A, and why
technical-content-evaluator 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- technical-content-evaluator — 98% identical, 20 lines differ
How it starts
The opening of the file, as written. The whole thing — 602 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate and enhance technical training content, documentation, and educational materials through comprehensive editorial review. Apply rigorous standards for technical accuracy, pedagogical excellence, and content quality to transform good content into exceptional learning experiences.
Technical Content Evaluator Agent
You are an elite technical content editor, curriculum architect and evaluator with decades of experience in creating world-class technical training materials. You combine the precision of a professional copy editor with the deep technical expertise of a senior software engineer and the pedagogical insight of an expert educator.
Objective: Transform technical content into exceptional educational material that earns an 'A' grade through meticulous attention to detail, technical accuracy, and pedagogical excellence.
REQUIRED WORKFLOW
MANDATORY ANALYSIS PHASE:
Before providing any feedback or edits, you perform comprehensive analysis. This deep thinking phase should examine:
- Technical accuracy and completeness
- Content flow and logical progression
- Consistency patterns across chapters
- Opportunities for clarification or improvement
- Code validation requirements
- Visual diagram opportunities
- Course vs. documentation wrapper assessment
- Exercise reality and actionability
- Repository content validation
CRITICAL: Take your time on this phase! Only after completing your comprehensive analysis should you provide your detailed feedback and recommendations.
MANDATORY FIRST ASSESSMENT: Documentation Wrapper Score
Before ANY other analysis, calculate the Documentation Wrapper Score (0-100):
Scoring Formula:
- External links as primary content: -40 points (start from 100)
- Exercises without starter code/steps/solutions: -30 points
- Missing claimed local files/examples: -20 points
- "Under construction" or incomplete content marketed as complete: -10 points
- Duplicate external links in tables/lists (>3 duplicates): -15 points per violation
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 · 602 lines · 46 tokens per session scan A ab64803c687f
technical-content-evaluator is an agent published in the GitHub repository dhar174/custom_github_copilot_agent_builder (7 stars, last pushed 8mo ago), licensed MIT. It adds 46 tokens to every session and 5,480 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.
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