author-flowglad-skill

name	author-flowglad-skill
description	Author and revise governed Flowglad Skill packages with Agent Skills instructions, OWS-native Python functions, synthetic JSON test cases, and lifecycle metadata. Use when creating or updating a reusable Flowglad capability, preparing a candidate revision, submitting through Flowglad MCP, following verification, or resolving package and OWS-ABI diagnostics.

Author Flowglad Skill

Create a durable Flowglad Skill package and follow its immutable revision lifecycle.

Authoring process

1. Gather the reusable behavior, inputs, outputs, failure conditions, and representative synthetic cases.
2. Use skills.list and skills.get when Flowglad MCP is available. Prefer a suitable active Skill over creating a duplicate.
3. For a new Skill, derive the slug from the request. For an update, resolve the exact Skill and base revision; never infer identities from names.
4. Read references/PACKAGE_CONTRACT.md completely.
5. Copy assets/starter-package, rename SKILL.template.md to exactly SKILL.md, replace every placeholder, and extend only what the capability needs.
6. For every selected Python function, read references/OWS_COMPATIBILITY.md and use the OWS code ABI. Do not add a CLI wrapper around OWS code.
7. Create or revise SKILL.md, support files, scripts/**.py, and small flowglad-ows-code-test-v1 cases under tests/**.json. Keep scratch work outside the package.
8. Run Python syntax checks and any available Flowglad OWS verifier. Do not execute the selected function as a positional-argument CLI.
9. Run this skill's bundled helper as python3 /path/to/author-flowglad-skill/scripts/build-package-payload.py PACKAGE_DIR for inline MCP preflight. For large packages or MCP byte-transport failures, add --transport staged and follow references/MCP_LIFECYCLE.md.
10. Submit only when the user explicitly asks to create, upload, publish, or update the remote Skill.
11. Poll skills.get_lifecycle_request, then skills.get until the candidate reaches its final verification state. Repair rejected authored bytes as a new immutable candidate.

Package design

• Keep metadata in meta.json and Markdown instructions without YAML frontmatter in SKILL.md.
• Prefer small deterministic functions with explicit injected inputs and JSON-compatible returns.
• Put structural constraints in the surrounding OWS schemas and semantic constraints in Python.
• Do not embed tokens, cookies, hidden credentials, or customer datasets. Use small synthetic test inputs.
• Default new Python packages to OWS-native verification with no CLI entrypoint declarations.
• Preserve stable CLI entrypoint identities only when explicitly maintaining a legacy CLI package; do not add them to new OWS-native packages.
• Never fabricate Flowglad IDs or conflate Skill verification with OWS publication.

Lifecycle integrity

• skills.create and skills.create_candidate accept inline base64 for small packages.
• Use skills.create_package_upload_session and skills.finalize_package_upload_session for large packages or transport failures; upload the exact declared bytes between those calls.
• An accepted lifecycle request or verificationDispatched: true does not prove verification success.
• Treat active, verified, rejected, and stale-base outcomes distinctly. Revisions are immutable.
• OWS publication separately validates the selected source function and exact revision.

Boundaries and handoff

Do not author the surrounding OWS workflow; use author-flowglad-ows. Do not weaken the verifier or mutate a remote Skill without explicit intent.

Return the complete package, any legacy entrypoint declarations, exact local checks, lifecycle/Skill/revision identities when submitted, final verification diagnostics, and OWS publication status as a separate state.