example

$npx mdskill add doodledood/manifest-dev/example

Analyzes project structure and tech stack to provide a concise overview

  • Summarizes projects when asked to explore or understand codebases
  • Reads manifest files and inspects directory structures
  • Identifies key technologies, frameworks, and project components
  • Delivers structured output with project name, tech stack, structure, and summary

SKILL.md

.github/skills/exampleView on GitHub ↗
---
name: example
description: 'Analyzes the current project structure and tech stack. Use when asked to explore, understand, or summarize a project. Trigger terms: project overview, analyze codebase, what is this project.'
argument-hint: '<input>'
---

# Example Skill

Analyze the current project: $ARGUMENTS

---

**Steps**:
1. Read package.json or similar manifest
2. Identify main technologies and frameworks
3. List key directories and their purposes
4. Summarize in 2-3 sentences

**Output**:
- **Project Name**: From manifest file
- **Tech Stack**: Languages, frameworks, libraries
- **Structure**: Key directories and contents
- **Summary**: Brief description of what project does

If no manifest exists, infer from file extensions and directory structure. Keep summary concise and actionable.

More from doodledood/manifest-dev

SkillDescription
autoEnd-to-end autonomous execution: figure-out → define → do, chained without manual approval gates. Use when you want to define and execute without intervention during planning, when the user asks for autonomous or end-to-end work, says just build it, or asks to tend or babysit a PR.
auto-optimize-promptIteratively auto-optimize a prompt until no issues remain. Uses prompt-reviewer in a loop, asks user for ambiguities, applies fixes via prompt-engineering skill. Runs until converged.
compress-promptCompresses prompts/skills into minimal goal-focused instructions. Trusts the model, drops what it already knows, maximizes action space. Use when asked to compress, condense, or minimize a prompt.
defineManifest builder. Turns shared understanding into a verifiable Manifest with Deliverables, Acceptance Criteria, Global Invariants, and Approach. Use when planning features, scoping refactors, debugging complex issues, or when the user asks to define, scope, plan, spec out, make a manifest, or break down a task.
doneCompletion marker for the /do workflow. Outputs a plain-prose summary of what was built. Called by /do after every Acceptance Criterion and Global Invariant verifies PASS, when the manifest is complete, all criteria pass, or the workflow needs to wrap up with a completion summary.
figure-outFigure things out together — any topic, problem, or idea. Presses relentlessly until shared understanding is reached. Use when you need to understand before acting, when figuring it out is the goal, or when the user asks to think through a decision, dig deeper, press an assumption, investigate why something is happening, or work through a problem.
figure-out-teamDrive a multi-party deliberation in a Slack channel or thread. The agent is an involved orchestrator — presses rigorously, brings evidence, names trade-offs, surfaces disagreements, advances when answers cohere; owner-by-Slack-handle overrules. Use when the people involved cannot all sit in one chat, when deliberation has to happen in Slack, or when the user asks to figure out with the team, press a group asynchronously, or get the team aligned.
harden-task-fileHarden /define task guidance files for one-shot quality. Iterates: orthogonality gap analysis, user-approved additions, prompt review, fix, converge. Use when a task file needs comprehensive coverage or "harden task file".
learn-from-sessionAnalyze Claude Code sessions to learn what went right/wrong and suggest high-confidence improvements to skills. Use when asked to analyze a session, learn from a session, or review workflow effectiveness.
optimize-prompt-token-efficiencyIteratively optimizes prompts for token efficiency by maximizing information density - reduces verbosity, removes redundancy, tightens phrasing while preserving semantic content. Use when asked to compress, shorten, reduce tokens, tighten, maximize density, increase information density, or make a prompt more concise.