/agent-skills-platform

name: agent-skills-platform
description: >-
  Create cross-platform agent skills from workflow descriptions. Activates when
  users ask to create an agent, automate a repetitive workflow, create a custom
  skill, or need advanced agent creation. Triggers on phrases like create agent
  for, automate workflow, create skill for, every day I have to, daily I need to,
  turn process into agent, need to automate, create a cross-platform skill,
  validate this skill, export this skill, migrate this skill, audit this skill,
  is this skill safe, vet a skill before installing, what does this skill access.
  Supports single skills, multi-agent suites, transcript processing,
  template-based creation, interactive configuration, cross-platform export,
  spec validation, and security auditing of third-party skills before install.
license: MIT
activation: /agent-skills-platform
# Compatibility alias: /agent-skill-creator
metadata:
  author: Francy J G Lisboa
  version: 6.1.0
  created: 2025-10-18
  last_reviewed: 2026-08-11
  review_interval_days: 180
  dependencies:
    - name: GitHub repository transport
      url: https://github.com/FrancyJGLisboa/agent-skills-platform
      type: service
    - name: GitHub raw bootstrap transport
      url: https://raw.githubusercontent.com/FrancyJGLisboa/agent-skills-platform/main/scripts/bootstrap.sh
      type: service
provenance:
  maintainer: Francy J G Lisboa
  version: 6.1.0
  created: 2025-10-18
  source_references:
    - https://github.com/FrancyJGLisboa/agent-skills-platform
    - https://agentskills.io
compatibility: >-
  Works on all platforms supporting the Agent Skills Open Standard (SKILL.md):
  Claude Code, GitHub Copilot CLI, VS Code Copilot, Cursor, Windsurf, Cline,
  OpenAI Codex CLI, Gemini CLI, and more — 17 platforms total.

/agent-skills-platform — Turn Existing Work Into a Reusable Skill

The user provides whatever already represents their work — a description, document, link, script, screenshot, transcript, or partial example. Turn that evidence into a complete, production-ready, cross-platform agent skill. The user should not need to write a specification, understand the skill format, choose an architecture, or review implementation details.

Recurring work contains tacit knowledge that people recognize more easily than they can document upfront. Infer that knowledge from the supplied material, confirm the result in plain language, build autonomously, and give the user a concrete output they can judge and correct.

The User Journey

Use this guided-light path by default. Expose the five technical phases only when the user asks how the factory works or requests interactive control.

1. **Understand** — read the evidence and summarize the question, trigger, supported decision, required evidence, and measurable success condition alongside the workflow, input, and output. Ask for one confirmation or correction. 2. **Build** — create the skill autonomously. Report progress in user language; do not ask the user to select APIs, architecture, filenames, or eval mechanics unless a choice changes the real-world outcome. 3. **Check** — run validation, pipeline, security, and eval gates. A clean security scan means no known pattern matched; it is not proof of safety. 4. **Try** — auto-install the skill and exercise it once on representative input in a safe local or dry-run environment. Show the output and ask whether it matches the user's work.

The skill is successfully created only after the representative run succeeds. If a safe run needs credentials, unavailable data, or permission for a consequential side effect, use the `verification-blocked` handoff below instead of claiming success.

At creation start, run `python3 scripts/success_ledger.py new-run`, retain that ID through verification, and record the local lifecycle events described in `references/product-success.md`. Recording stores no workflow content and must never block creation; respect `ASC_SUCCESS_LEDGER=off`.

First-run destination routing

Before the structured interview, ask exactly one routing question when the user has not already made the destination clear:

> Is this skill just for you, or will teammates install or reuse the skill itself?

• **Just for me** — create, verify, and install a private skill. Do not ask the user

to create a marketplace or invent owners and approval state.

• **My team** — ask whether a governed GitHub or GitLab marketplace already exists.

If it exists, read its published governance configuration before generation and bind the generated skill to its exact ownership and approval requirements. If it does not exist, route the marketplace operator to create it before the team skill is generated; the workflow expert does not run marketplace commands.

Teammates receiving a report, queue, or other output does not by itself make this a team skill; route to a marketplace only when teammates will install or reuse the skill. This is destination routing, not a technical interview. Do not explain registries, release tags, or marketplace internals unless the user chooses team use.

During the structured interview, confirm each discovered decision in plain language and ask only the next highest-value question. Never present a fixed question count or progress fraction: the number of questions depends on the workflow and its risk.

Trigger

User invokes `/agent-skills-platform` followed by their input:

/agent-skills-platform Every week I pull sales data, clean it, and generate a report
/agent-skills-platform https://wiki.internal/deploy-runbook
/agent-skills-platform See src/invoice_processor.py — turn it into a reusable skill
/agent-skills-platform Here's our API docs: https://api.internal/docs — make a skill for querying inventory
/agent-skills-platform Based on compliance-checklist.pdf, create a skill for SOX audits
/agent-skills-platform --mcp-audit https://github.com/vendor/mcp-server — we pay for this data,