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.
npx agentmods add instructions/dotnet/skills/agents-mdgit clone --depth 1 https://github.com/dotnet/skillsWhat 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 | $0.00441 | $0.00441 |
| Opus 5 | $0.00220 | $0.00220 |
| Sonnet 5 | $0.00088 | $0.00088 |
| Haiku 4.5 | $0.00044 | $0.00044 |
Grade A, and why
skills AGENTS.md 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 2d 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.
What it actually says
Repository Instructions
This repository contains skill plugins under plugins/. Each subdirectory in plugins/ is an independent plugin (e.g., plugins/dotnet-msbuild, plugins/dotnet).
Working on skills and evals
Use the repository's own authoring skills under .agents/skills/ instead of improvising:
create-skill— scaffolding a new skill, and writing adescriptionthe runtime will route to.create-skill-test— writing or resizing aneval.yaml. Evals use the Vally schema (stimuli:/graders:/defaults:);scenarios:/assertions:no longer load.improve-skill-quality— an eval regressed, produced no verdict, or the skill did not activate. Classify the failure before editing skill content; broken fixtures, underpowered trial counts and harness errors routinely masquerade as skill regressions.
Before pushing eval changes, run python eng/eval-quality/check_eval_quality.py. It blocks eleven
structural defect classes documented in eng/eval-quality/README.md that can corrupt a real
evaluation result.
The distilled quality rules — what makes a skill beat its own baseline — live in the "Quality bar"
section of CONTRIBUTING.md.
Skill-Validator
The skill-validator is a shipping tool — its NuGet package and .tar.gz archives are built from eng/skill-validator/src/. Content referenced at runtime or bundled with the tool (docs, README, etc.) must live under src/ so it is included in the published output. Do not add references from src/ to files outside of it, except for explicitly linked packaging assets (such as the repo-root LICENSE file) referenced by the project file.
When modifying the evaluation pipeline (evaluation.yml), results JSON schema (Models.cs), or the skill-validator evaluation logic, review and update eng/skill-validator/src/docs/InvestigatingResults.md to keep the failure investigation guidance, schema documentation, and example scripts in sync.
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.
- 2d ago First seen · 28 lines · 441 tokens per session scan A b8a750837482
skills AGENTS.md is an instructions file published in the GitHub repository dotnet/skills (5,314 stars, last pushed today), licensed MIT. It adds 441 tokens to every session, about $0.0022 per session on Opus 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.
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