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 skills/xobotyi/cc-foundry/discoverynpx skills add xobotyi/cc-foundry --skill discoverygit clone --depth 1 https://github.com/xobotyi/cc-foundryWrote 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/xobotyi/cc-foundry/discovery)<a href="https://agentmods.dev/skills/xobotyi/cc-foundry/discovery"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/discovery.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 | $0.00035 | $0.02153 |
| Opus 5 | $0.00017 | $0.01077 |
| Sonnet 5 | $0.00007 | $0.00431 |
| Haiku 4.5 | $0.00003 | $0.00215 |
Grade A, and why
discovery 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 yesterday.
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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Discovery
The user's domain expertise, lived experience, and value judgments are inputs you cannot generate independently. Discovery exists to externalize them into a form downstream stages can act on. Extract, don't contribute — never suggest solutions, fill in gaps, or accept vague answers.
Two-phase process:
Phase 1 (Intent Capture): Listen first. Understand what the user wants without pushback. Confirm shared understanding before any challenge.
Phase 2 (Stress-Test): After alignment on what they want, challenge assumptions, find gaps, pressure reasoning. If the user cannot defend a point, that point is not ready for design.
Glossary
The project's glossary (docs/glossary.md if present) is loaded before discovery starts. It is the project's vocabulary
contract — canonical terms with their definitions and forbidden aliases. Loading it lets the agent ask questions in the
project's language without inventing meanings.
Loading the glossary is not codebase or architecture exploration. Architecture-shaped knowledge (CLAUDE.md, ADRs, prior alignment or frame documents, source code) stays out of discovery — it would let the agent pattern-match user intent to existing structures and stop asking real questions. Glossary is vocabulary; it shapes language, not assumptions.
First iteration exception. If no glossary exists, proceed glossary-blind. The glossary is created during alignment.
Hypothesis, not law. When user statements contradict glossary definitions, surface the contradiction — but treat the user as the source of truth. Glossary updates are alignment's job; discovery only flags the mismatch.
Dimensions
Cover these through questioning. Track coverage broadly — not a checklist to complete, but a map of territory to explore.
- Motivation — Why are we doing this? For problems: what is broken, who experiences it, how do they experience it today? For features: what value does this create? Is the motivation validated or assumed?
- Goals — What does success look like, concretely? How would you measure it? What changes when this is done?
- Scope — What's in, what's out? Why those boundaries? What's the smallest version that delivers value?
- Constraints — What can't change? What's non-negotiable? Budget, timeline, technology, compatibility?
- Actors — Who uses this? Who maintains it? Who is affected by it? Are their needs aligned or conflicting?
- Risks — What could go wrong? What's the worst-case failure mode? What assumptions are you making that could be false?
- Prior art — Has this been tried before? Why did it fail or not exist yet? What can be learned from existing solutions?
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.
- yesterday First seen · 178 lines · 35 tokens per session scan A 6d9b0bfe559e
discovery is a skill published in the GitHub repository xobotyi/cc-foundry (20 stars, last pushed 2d ago), licensed MIT. It adds 35 tokens to every session and 2,153 once invoked, about $0.0002 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 2026-09-04.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…
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…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
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.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…