Visual Studio Code is a code editor that supports editing, navigating, understanding, debugging, and extending software projects. Developers use it for the edit-build-debug cycle, and the catalogue add-ons provide skills, instructions, and agents for working within the editor.
Borrowing it
Nothing to install: this file belongs to microsoft/vscode. 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/vscode/main/.github/skills/feedback-learning/SKILL.mdgit clone --depth 1 https://github.com/microsoft/vscodeWrote 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/vscode/feedback-learning)<a href="https://agentmods.dev/skills/microsoft/vscode/feedback-learning"><img src="https://agentmods.dev/badge/skills/microsoft/vscode/feedback-learning/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/vscode/feedback-learning"><img src="https://agentmods.dev/badge/skills/microsoft/vscode/feedback-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 14 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
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 | $0.00021 | $0.00794 |
| Opus 5.5 | $0.00008 | $0.00318 |
| Sonnet 5.5 | $0.00004 | $0.00159 |
| Haiku 4.5 | $0.00002 | $0.00079 |
Grade A, and why
feedback-learning 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 10d 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.
Feedback learning
Use this skill when a user explicitly corrects an implementation or design approach, rejects a pattern, or asks the agent to learn from feedback, except when the user invokes the literal learn! trigger.
Literal learn! requests are governed exclusively by .github/instructions/learnings.instructions.md and are outside this skill's scope. Do not replace or reinterpret that instruction.
Goal
Preserve reusable knowledge in the smallest authoritative artifact. A correction is not automatically a permanent rule.
Workflow
-
Identify the correction
- What was wrong?
- Why was it wrong?
- What approach did the user prefer?
- Which paths or subsystem does it affect?
-
Search before writing
- Check applicable instructions, skills, design documents, tests, and scoped learning files.
- Update an existing rule instead of adding a duplicate.
-
Classify the feedback
Feedback kind Destination Task-specific preference or one-off adjustment Do not persist Concrete behavior that must not regress Regression test Stable subsystem architecture or product invariant Relevant design/specification document Universal repository rule Applicable instruction file Reusable but not yet mature or proven guidance .github/learnings/<area>.mdTool-driven workflow Relevant skill -
Generalize carefully
- Preserve the principle, not the incident chronology.
- Do not include temporary symbol names, line numbers, or implementation details unless they define the durable contract.
- Do not turn a single rejected implementation into a universal prohibition without broader evidence.
-
Record once
- Design documents and tests are authoritative.
- A learning inbox entry is temporary. When promoted, remove the inbox entry in the same change.
- Never copy the same rule into a skill, instruction, and design document.
- Before adding an inbox entry, compact the target file using the maintenance rules below.
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.
- 10d ago Changed · -5 lines fea38fe93efa
- 1mo ago First seen · 93 lines · 21 tokens per session scan A 49d47a02f549
feedback-learning is a skill published in the GitHub repository microsoft/vscode (194,219 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 794 once invoked, about $0.0001 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 skills, from other repositories
cyrene-plan-mode
A planning guide for Cyrene’s Plan Mode, used before code or file changes are made. It helps turn a broad request into an agreed implementation plan based on the project’s actual code and constraints.
cyrene-plugin-dev
A guide for developing runtime plugins for Cyrene, including plugins that add tools, windows, events, live context, or connections to services. Cyrene is the host application that runs these plugins.
cyrene-exam-paper
A method for creating formal exams from a learning plan, then keeping the questions, answers, grading, and review separate. It maps learned topics to skills and suitable question types.
cyrene-obsidian-workspace
A set of rules for an agent working with an Obsidian vault, a folder of linked Markdown notes. It defines where materials, notes, exercises, templates, and progress files belong and how they may be edited.
cyrene-work-hygiene
A Work-mode rulebook for organizing projects that create, arrange, or deliver several files.
cyrene-diagram
A design guide for making static SVG learning cards: small diagrams or reference cards rendered as images. It explains when to use a card instead of Mermaid, a text-based diagram format, and sets layout, color, size, and safety rules.