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Agent install bundle + self-test for PLATE-built products

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
35/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Active
Tech stack
python
Domain
developer-experience

Research direction

The issue names template_payload, .agentic/marketing.yml, and the docs as entry points; start by reading those and the related issues #1159 and #1164. The proposal also calls for a worked product example and a stdlib unittest. Done means templates, a config flag, one example, bundle documentation, and a test that verifies the self-test runs; the issue says the stub needs refinement first.

Written by the indexing model from the issue text.

Description

area:product Feature need:refinement

Part of #1159.

Outcome

For each PLATE product that an AI agent might use, generate an agent install bundle from one source of truth, plus a self-test an agent can run in seconds. The bundle makes the product installable and verifiably working in the agent harnesses people actually use.

Problem

Agent harnesses learn about a tool from short text and pick it mostly by how well that text matches the task, plus list position and training-data familiarity (see the research note in #1160). Octopus OS (repo, MIT, macOS research preview) declares each capability as JSON in a registry/ folder (tool.json, interface.json, life.json, application.json) and adds a short plain-text context artifact that teaches the model the tool's contract, with an optional verifier. Claude Code, Codex and Cursor use skills and MCP configs whose short descriptions load on demand. Being easy to install and honestly, checkably working is the legitimate lever.

Proposal

Per product, generate:

  • A harness-neutral manifest: name, what it does, limits as specific numbers, machine-readable pricing, auth and setup steps, and an honest "when NOT to use" section.
  • Adapters per harness: an MCP config, a skill file, and Octopus-style registry JSON.
  • A short, accurate context artifact that states the contract and its limits.
  • A self-test or fixture with a clear pass or fail result that runs in seconds.

Guardrails

  • Accurate and verifiable claims only. Every claim traces to SPEC.md or an issue.
  • No hidden text, no prompt injection aimed at models, no fake reviews or endorsements, and no puffery.
  • Bundle text is reviewed under claim_review_policy in .agentic/marketing.yml.

Done when

  • Template(s) exist in template_payload.
  • A config flag exists in .agentic/marketing.yml.
  • One worked example exists for a PLATE product.
  • Docs describe the bundle.
  • A stdlib unittest proves the self-test runs.

Hooks

Related: #1162 (llms.txt and JSON-LD), #1165 (deploy defaults by deployment type), #1169 (scoring), #1160 (research), #1164 (marketing.yml config).

Notes

  • Adapter formats can change (Octopus docs promise no schema migrations), so keep the manifest as the source of truth and generate adapters from it.
  • Stub: refine before work starts.
Dominant language
Python
Stars
1
Forks
0
Avg merge
2d 5h
Merged PRs (30d)
43

Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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