Generative AI for Beginners .NET is a hands-on course that teaches .NET developers to build applications using generative AI models and related tools. Its lessons use practical samples covering scenarios such as chat, audio transcription, agents, and local AI. The catalogue entries are add-ons associated with the course repository.
Borrowing it
Nothing to install: this file belongs to microsoft/Generative-AI-for-beginners-dotnet. 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/microsoft/Generative-AI-for-beginners-dotnet/main/.github/skills/iterative-retrieval/SKILL.mdgit clone --depth 1 https://github.com/microsoft/Generative-AI-for-beginners-dotnetWrote 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/skills/microsoft/generative-ai-for-beginners-dotnet/iterative-retrieval)<a href="https://agentmods.dev/skills/microsoft/generative-ai-for-beginners-dotnet/iterative-retrieval"><img src="https://agentmods.dev/badge/skills/microsoft/generative-ai-for-beginners-dotnet/iterative-retrieval.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.01385 |
| Opus 5 | $0.00017 | $0.00692 |
| Sonnet 5 | $0.00007 | $0.00277 |
| Haiku 4.5 | $0.00003 | $0.00138 |
Grade A, and why
iterative-retrieval 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 9d 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.
This is a copy
100% identical to iterative-retrieval — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iterative Retrieval Skill
Squad agents frequently spawn sub-agents to complete scoped work. Without structure, these handoffs become vague, cycles multiply, and outputs land without being checked. The Iterative Retrieval Pattern caps cycles at 3, mandates WHY context in every spawn, and requires the coordinator to validate agent output before closing an issue.
Spawn Prompt Template
Every agent spawn must include the following four sections. Copy and fill in the template:
## Task
{What you need done — concrete and bounded}
## WHY this matters
{The motivation and context. What system or user goal does this serve? What breaks if skipped?}
## Success criteria
{How you will know the output is correct. Be explicit — list acceptance criteria, not vibes.}
Example:
- [ ] File X exists and contains Y
- [ ] No regressions in existing tests
- [ ] PR is open targeting main with description matching the issue
## Escalation path
{What the agent should do if uncertain or stuck. "Stop and ask me" is valid.}
Example:
- If requirements are ambiguous → stop, comment on the issue, set label status:needs-decision
- If blocked by a dependency → label status:blocked, explain in a comment
- If 3 cycles exhausted without resolution → write a summary to inbox and surface to coordinator
3-Cycle Protocol
| Cycle | Description | Exit condition |
|---|---|---|
| 1 | Initial attempt | Done → coordinator validates. Incomplete → surface delta. |
| 2 | Targeted retry with specific corrections | Done → coordinator validates. Incomplete → one more. |
| 3 | Final attempt with all context from cycles 1–2 | Done or escalate — no cycle 4. |
Rules
- After each cycle, the coordinator evaluates the output against the success criteria before accepting it or spawning the next cycle.
- Objective context forward: each subsequent spawn includes a summary of what was tried and what is still missing — not just a repeat of the original task.
- Cycle 3 exhausted → escalate: write a summary to
.squad/decisions/inbox/, label the issuestatus:needs-decision, and notify the user.
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.
- 9d ago First seen · 166 lines · 34 tokens per session scan A a1ac94cbaed7
iterative-retrieval is a skill published in the GitHub repository microsoft/Generative-AI-for-beginners-dotnet (3,052 stars, last pushed 8d ago), licensed MIT. It adds 34 tokens to every session and 1,385 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to iterative-retrieval, differing in 0 lines, and is treated as a copy.
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