dictation
Skill4 starsChanged 6 months agoMITView on GitHub
Fix speech-to-text errors, correct technical terminology, and improve text clarity in documentation and workflows.
- Reads credentials
1 other project uses a version of this.
Adopted by
Most recently taken up by
- marmutapp/superbased-claude-code-plugintheir own version, first seen Apr 12, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- marmutapp/superbased-claude-code-plugin★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository124 other files it gives its agents.
- AGENTS.mdAGENTS.md
- .github/AGENTS.mdAGENTS.md
- .github/workflows/AGENTS.mdAGENTS.md
- .vscode/AGENTS.mdAGENTS.md
- .github/copilot-instructions.mdCopilot instructions
- agent-log-analysisSkill
- agent-mdSkill
- agent-md-writerSkill
- agent-skill-md-writerSkill
- agentskillsSkill
- agents-md-writerSkill
- ai-prompt-writerSkill
- ansibleSkill
- apache-airflow-apiSkill
- apache-airflow-dagsSkill
- astro-cliSkill
- astronomer-docsSkill
- awesome-copilotSkill
- brainstorm-agent-runsSkill
- brainstorm-github-prSkill
- brainstormSkill
- catSkill
- chrome-devtoolsSkill
- claude-docsSkill
- codeqlSkill
- code-reviewSkill
- code-tourSkill
- coding-standard-writerSkill
- context-aware-opsSkill
- copilot-cliSkill
- copilot-docsSkill
- critical-thinkingSkill
- datadog-agentSkill
- datadog-apiSkill
- datadog-mcpSkill
- datadog-monitorsSkill
- datadog-pulumiSkill
- devcontainerSkill
- direnvSkill
- dockerfileSkill
- dockerSkill
- docs-reviewSkill
- docs-writerSkill
- dot-claudeSkill
- dotfilesSkill
- dot-githubSkill
- editorconfigSkill
- fact-writerSkill
- gdpr-compliantSkill
- gh-agent-taskSkill
- gh-apiSkill
- gh-aw-compileSkill
- gh-aw-firewall-debugSkill
- gh-aw-firewallSkill
- gh-aw-newSkill
- gh-awSkill
- gh-aw-troubleshootingSkill
- gh-codespaceSkill
- gh-issueSkill
- gh-modelsSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
8 rules. About 0 are common, 7 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈7
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 16
Refine: Remove filler words, improve conciseness, and ensure technical accuracy without modifying code/examples.
- Very specific: line 21
Accurate Hyphenation: Ensure correct hyphenation for compound modifiers (e.g., pull-request, cache-memory).
- Distinctive: line 23
No Code Generation: Do not generate code examples or output step-by-step planning to the user.
- Distinctive: line 24
Maintain Intent: Do not simplify technical concepts or alter the user's intended meaning.
- Distinctive: line 28
When processing raw dictation or speech-to-text input.
- Distinctive: line 29
When refining draft documentation or workflow descriptions that contain filler words or inaccuracies.
- Distinctive: line 34
When generating new content from scratch (use documentation generation skills instead).
- Distinctive: line 133
Improve Clarity: Remove redundant phrases ("in order to" -> "to", "at this point in time" -> "now"), make text more concise, and maintain …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Skill: dictation
<!-- markdownlint-disable MD013 MD023 MD031 MD032 -->Apply dictation correction protocols to fix common speech-to-text errors, especially technical terms from the project glossary.
Core Process
- Identify: Scan the input text for common speech-to-text misrecognitions, filler words, and unclear phrasing.
- Correct: Apply project-specific technical term corrections, hyphenation rules, and engine name formatting.
- Refine: Remove filler words, improve conciseness, and ensure technical accuracy without modifying code/examples.
Core Principles
- Glossary Compliance: Strict adherence to the project technical glossary.
- Accurate Hyphenation: Ensure correct hyphenation for compound modifiers (e.g.,
pull-request,cache-memory). - Engine Capitalization: Proper spelling and casing for AI engines (e.g., GitHub Copilot, Claude, Gemini).
- No Code Generation: Do not generate code examples or output step-by-step planning to the user. You may follow the Core Process internally, but the response should primarily be the corrected text.
- Maintain Intent: Do not simplify technical concepts or alter the user's intended meaning.
When to Use
- When processing raw dictation or speech-to-text input.
- When refining draft documentation or workflow descriptions that contain filler words or inaccuracies.
When Not to Use
- For refactoring actual source code, where technical syntax must remain untouched by NLP heuristics.
- When generating new content from scratch (use documentation generation skills instead).
- For translating text between distinct human languages.
Common Pitfalls
- Over-Correction: Attempting to "fix" valid code snippets or shell commands that happen to contain words resembling dictation errors (e.g., changing a literal
"get actions"variable name to"GitHub Actions"). - Losing Tone: Stripping out conversational nuance entirely when editing a community-facing discussion, making it sound robotic.
- Ignoring Project Context: Applying global acronyms (like expanding
API) when the project glossary specifically demands a different domain-specific expansion.
Quick Start
User: "The get actions work flow needs safe outputs for the cache memory"
Agent: Corrects to "The GitHub Actions workflow needs safe-outputs for the cache-memory"Vocabulary & Corrections
GitHub and Git Terms
- "get hub" -> GitHub
- "git lab" -> GitLab
- "get actions" -> GitHub Actions
- "pull request" -> pull-request (as compound modifier)
- "issue ops" -> IssueOps
- "label ops" -> LabelOps
- "chat ops" -> ChatOps
- "multi repo ops" -> MultiRepoOps
- "project ops" -> ProjectOps
- "data ops" -> DataOps
- "dispatch ops" -> DispatchOps
- "daily ops" -> DailyOps
Workflow Configuration
- "front matter" -> frontmatter
- "safe outputs" -> safe-outputs
- "safe inputs" -> safe-inputs
- "lock file" -> .lock.yml or lockfile
- "tool sets" -> toolsets
- "M.C.P." or "M C P" -> MCP
- "repo memory" -> repo-memory
- "cache memory" -> cache-memory
- "work flow" -> workflow
- "timeout minutes" -> timeout-minutes
- "runs on" -> runs-on
- "min integrity" -> min-integrity
- "mcp gateway" -> mcp-gateway
- "mcp scripts" -> mcp-scripts
- "staged mode" -> staged-mode
- "token weights" -> token-weights
- "effective tokens" -> effective-tokens
AI Engines
- "co-pilot" -> GitHub Copilot
- "code x" -> Codex
- "cloud" -> Claude
- "gem ini" -> Gemini
- "serena" -> Serena
Commands and Operations
- "G.H. A.W." -> gh-aw or
gh aw - "re-compile" -> recompile
- "work flow dispatch" -> workflow_dispatch
- "action lint" -> actionlint
- "ziz more" -> zizmor
- "poo teen" -> poutine
- "queue M.D." -> qmd
File Formats and Extensions
- "dot M.D." -> .md
- "dot Y.A.M.L." or "dot Y M L" -> .yaml or .yml
- "dot lock dot Y M L" -> .lock.yml
- "jason" -> JSON
- "wasm" -> WebAssembly or wasm
Technical Patterns
- "A.P.I." -> API
- "U.R.L." -> URL
- "H.T.T.P." -> HTTP
- "H.T.T.P.S." -> HTTPS
- "S.H.A." -> SHA
- "C.I." -> CI
- "G.H." -> GH
- "Y.A.M.L." -> YAML
- "O.I.D.C." -> OIDC
- "S.A.R.I.F." -> SARIF
Environment Variables
Capitalize fully: GITHUB_TOKEN, GH_TOKEN, COPILOT_GITHUB_TOKEN, GH_AW_GITHUB_TOKEN, ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY, CLAUDE_CODE_OAUTH_TOKEN, CODEX_API_KEY
Clean Up and Improve Text
- Remove Filler Words: humm, um, uh, uhh, umm, you know, like, basically, actually, literally, kind of, sort of, I mean, I think, right?, okay?, so yeah, well.
- Improve Clarity: Remove redundant phrases ("in order to" -> "to", "at this point in time" -> "now"), make text more concise, and maintain technical accuracy.
What to Avoid
- Generating code examples.
- Providing step-by-step planning.
- Simplifying technical concepts.
References
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

