AgentRC prepares repositories for AI coding agents by analyzing their code and generating project-specific instructions, evaluations, and development configuration. It is used through a CLI, VS Code extension, or CI pipeline to measure AI readiness, keep context useful, and detect drift; the catalogue entries support these repository-preparation workflows.
Borrowing it
Nothing to install: this file belongs to microsoft/agentrc. 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/agentrc/main/.github/skills/agentic-workflows/SKILL.mdgit clone --depth 1 https://github.com/microsoft/agentrcWrote 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/agentrc/agentic-workflows)<a href="https://agentmods.dev/skills/microsoft/agentrc/agentic-workflows"><img src="https://agentmods.dev/badge/skills/microsoft/agentrc/agentic-workflows/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/skills/microsoft/agentrc/agentic-workflows"><img src="https://agentmods.dev/badge/skills/microsoft/agentrc/agentic-workflows.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 | $0.00020 | $0.01333 |
| Opus 5.5 | $0.00008 | $0.00533 |
| Sonnet 5.5 | $0.00004 | $0.00267 |
| Haiku 4.5 | $0.00002 | $0.00133 |
Grade A, and why
agentic-workflows 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.
This is a copy
98% identical to agentic-workflows — 11 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Workflows Router
Use this skill when a user asks to design, create, update, debug, or upgrade GitHub Agentic Workflows in this repository.
This skill is a dispatcher: identify the task type, load the matching workflow prompt/skill file, and follow it directly. Keep responses concise and ask a clarifying question if the correct prompt is unclear.
Repository overlay (optional):
- If
.github/aw/instructions.mdexists, load it with@.github/aw/instructions.mdafter loading the matched prompt/skill. - Precedence: repository overlay instructions override upstream defaults when they conflict.
Read only the files you need:
Load these files from github/gh-aw (they are not available locally).
.github/aw/action-container-substitutions.md.github/aw/agent-runtime-instructions.md.github/aw/agentic-chat.md.github/aw/agentic-workflows-mcp.md.github/aw/asciicharts.md.github/aw/campaign.md.github/aw/charts-trending.md.github/aw/charts.md.github/aw/cli-commands.md.github/aw/compat.md.github/aw/configure-agentic-engine.md.github/aw/context.md.github/aw/create-agentic-workflow-trigger-details.md.github/aw/create-agentic-workflow.md.github/aw/create-shared-agentic-workflow.md.github/aw/debug-agentic-workflow.md.github/aw/dependabot.md.github/aw/deployment-status.md.github/aw/designer-mappings.md.github/aw/designer.md.github/aw/drive-memory.md.github/aw/enclaves.md.github/aw/evals.md.github/aw/experiments.md.github/aw/github-agentic-workflows.md.github/aw/github-mcp-server-pagination.md.github/aw/github-mcp-server-tools.md.github/aw/github-mcp-server.md.github/aw/instructions.md.github/aw/intent.md.github/aw/jobs.md.github/aw/linter-workflows.md.github/aw/llms.md.github/aw/loop.md.github/aw/lsp.md.github/aw/maintainer.md.github/aw/mcp-clis.md.github/aw/memory-stateful-patterns.md.github/aw/memory.md.github/aw/messages.md.github/aw/multi-agent-research.md.github/aw/network.md.github/aw/optimize-agentic-workflow.md.github/aw/patterns.md.github/aw/playwright.md.github/aw/pr-reviewer.md.github/aw/release-workflow.md.github/aw/report.md.github/aw/reuse.md.github/aw/safe-outputs-automation.md.github/aw/safe-outputs-content.md.github/aw/safe-outputs-management.md.github/aw/safe-outputs-runtime.md.github/aw/safe-outputs.md.github/aw/serena-tool.md.github/aw/shared-safe-jobs.md.github/aw/skills.md.github/aw/subagents.md.github/aw/syntax-agentic.md.github/aw/syntax-core.md.github/aw/syntax-engine.md.github/aw/syntax-tools-imports.md.github/aw/syntax.md.github/aw/test-coverage.md.github/aw/test-expression.md.github/aw/token-optimization-caching-budgets.md.github/aw/token-optimization-observability.md.github/aw/token-optimization.md.github/aw/triggers.md.github/aw/update-agentic-workflow.md.github/aw/upgrade-agentic-workflows.md.github/aw/visual-regression.md.github/aw/workflow-constraints.md.github/aw/workflow-editing.md.github/aw/workflow-patterns.md
After loading the matching workflow prompt or skill, follow it directly:
- Design workflows from scratch via interview:
.github/aw/designer.md - Create new workflows:
.github/aw/create-agentic-workflow.md - Configure or add declarative engines:
.github/aw/configure-agentic-engine.md - Update existing workflows:
.github/aw/update-agentic-workflow.md - Debug, audit, or investigate workflows:
.github/aw/debug-agentic-workflow.md - Upgrade workflows and fix deprecations:
.github/aw/upgrade-agentic-workflows.md - Create shared components or MCP wrappers:
.github/aw/create-shared-agentic-workflow.md - Create report-generating workflows:
.github/aw/report.md - Fix Dependabot manifest PRs:
.github/aw/dependabot.md - Analyze coverage workflows:
.github/aw/test-coverage.md - Render compact markdown charts:
.github/aw/asciicharts.md - Map CLI commands to MCP usage:
.github/aw/cli-commands.md - Choose workflow architecture and patterns:
.github/aw/patterns.md - Optimize token usage and cost:
.github/aw/token-optimization.md - Design long-running multi-agent research workflows:
.github/aw/multi-agent-research.md - Add skills or agent plugins requested by the user (
skills:/plugins:frontmatter, never on-the-fly installs):.github/aw/skills.md
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 · 113 lines · 20 tokens per session scan A 0c0ed35d9f5b
agentic-workflows is a skill published in the GitHub repository microsoft/agentrc (1,109 stars, last pushed 2d ago), licensed MIT. It adds 20 tokens to every session and 1,333 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to agentic-workflows, differing in 11 lines, and is treated as a copy.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
component-fixtures
Use when creating or updating component fixtures for screenshot testing or their shared infrastructure, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, validation, and common pitfalls.