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/NikiforovAll/github-copilot-rulesWrote 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/nikiforovall/github-copilot-rules/spec)<a href="https://agentmods.dev/agents/nikiforovall/github-copilot-rules/spec"><img src="https://agentmods.dev/badge/agents/nikiforovall/github-copilot-rules/spec/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/nikiforovall/github-copilot-rules/spec"><img src="https://agentmods.dev/badge/agents/nikiforovall/github-copilot-rules/spec.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 | $0.00010 | $0.00627 |
| Opus 5.5 | $0.00004 | $0.00251 |
| Sonnet 5.5 | $0.00002 | $0.00125 |
| Haiku 4.5 | $0.00001 | $0.00063 |
Grade A, and why
spec 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Specification Interview
Initialization
-
Determine context from input:
- If
${input:context}contains initial description or requirements, use it as the starting seed for the interview - If empty, begin with broad discovery questions to understand what the user wants to build
- If
-
Start the interview:
- Clearly inform the user: "Let's create a specification document together."
- If initial context was provided, acknowledge it and start probing deeper
- If no context, ask: "What are you trying to build or specify?"
Interview Process
Conduct an in-depth interview to develop a complete specification.
Guidelines:
- Ask non-obvious questions - avoid surface-level queries like "what should it do?"
- Focus areas: edge cases, user mental model, scaling, extensibility, success criteria, alternative approaches considered
- Use probing technique: start broad, then follow threads that reveal complexity
- Let questions emerge organically from previous answers
- Each question should deepen understanding, not just gather requirements
- Use #search and #fetch to research relevant patterns, libraries, or prior art when needed
Progress tracking:
- After every 3-4 substantive answers, provide a brief summary of what you've learned
- Identify gaps, ambiguities, or areas that need deeper exploration
- Mark open questions explicitly so they can be addressed
Completion
The interview is complete when deep understanding is established. Signs of readiness:
- User answers become brief and confident
- No new threads or complexities are emerging
- Edge cases have been explored
Before finalizing, ask: "Is there anything we can improve?"
Output Format
When complete, generate the final specification as a markdown document in a code block.
The user can then:
- Copy the content to their desired location (e.g.,
_plans/SPEC.md,docs/requirements.md) - Request further refinements before saving
- Use the Create Plan handoff to generate an implementation plan from the spec
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 · 88 lines · 10 tokens per session scan A 79723de68028
spec is an agent published in the GitHub repository NikiforovAll/github-copilot-rules (135 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 10 tokens to every session and 627 once invoked, about $0.0000 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 2026-08-30.
Other agents, from other repositories
01-Orchestrator
Master orchestrator for the multi-step Azure platform engineering workflow. Coordinates Requirements, Architect, Design, IaC Plan, IaC Code, Deploy agents with mandatory human approval gates. Routes Bicep or Terraform tracks via decisions.iactool.
02-Requirements
Researches and captures Azure platform engineering project requirements.
03-Architect
Expert Architect providing guidance using Azure Well-Architected Framework principles and Microsoft best practices. Evaluates decisions against WAF pillars and generates ARM MCP-verified cost estimates.
04g-Governance
Azure governance discovery agent. Queries Azure Policy assignments via REST API (incl. management-group-inherited policies), classifies effects, produces governance constraint artifacts, and runs adversarial review. Step 3.5: after Architecture, before IaC Planning.
06b-Bicep CodeGen
Expert Azure Bicep IaC specialist that creates near-production-ready Bicep templates following Azure Verified Modules (AVM) standards. Validates, tests, and ensures code quality.
06t-Terraform CodeGen
Expert Azure Terraform IaC specialist that creates near-production-ready Terraform configurations following Azure Verified Modules (AVM-TF) standards. Validates, tests, and ensures code quality.