Deliverable Selfloop

Community

WILLOSCAR

deliverable-selfloop

Score an existing reader-facing deliverable and emit a deterministic PASS/FAIL scorecard plus bounded repair plan; use after synthesis, not to rewrite content.

Overview

Publisher	WILLOSCAR
Repository	`research-units-pipeline-skills`
Skill name	`deliverable-selfloop`
Stars	513
Forks	39
Bundled files	3
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Deliverable Selfloop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/deliverable-selfloop
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/deliverable-selfloop

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/deliverable-selfloop .claude/skills/deliverable-selfloop

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deliverable Selfloop in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deliverable Selfloop on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deliverable Selfloop is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Deliverable Self-Loop

Runs the final quality gate for a reader-facing deliverable and always writes a PASS/FAIL report.

Inputs

Primary input depends on the active pipeline contract:

brief deliverable
paper-review deliverable
evidence-review deliverable
idea memo bundle
tutorial deliverable

Output

output/DELIVERABLE_SELFLOOP_TODO.md
for research-brief: output/BRIEF_SCORECARD.md and output/BRIEF_SCORECARD.json
for paper-review: output/REVIEW_SCORECARD.md and output/REVIEW_SCORECARD.json
for idea-brainstorm: output/IDEA_SCORECARD.md and output/IDEA_SCORECARD.json
for evidence-review: output/EVIDENCE_SCORECARD.md and output/EVIDENCE_SCORECARD.json

Dispatch rule

The gate should dispatch by pipeline contract first:

quality_contract.deliverable_kind

Only fall back to legacy profile-name checks when contract metadata is missing.

Script boundary

scripts/run.py should:

detect the active deliverable contract
run the matching evaluator
always write a report

It should not mutate the deliverable itself.

Acceptance

report exists
report contains - Status: PASS or - Status: FAIL
PASS only when the active deliverable satisfies its minimum section / artifact contract
research-brief additionally requires valid core-set pointers and the configured score threshold
paper-review additionally requires the configured score threshold and all critical rubric dimensions
idea-brainstorm additionally requires traceable anchors, actionable lead directions, and the configured score threshold
evidence-review additionally requires clause-linked screening, complete extraction rows, synthesis pointers, and the configured score threshold

Non-goals

rewriting the deliverable
choosing upstream fixes beyond pointing to the missing contract items

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deliverable Selfloop AI skill do?

Score an existing reader-facing deliverable and emit a deterministic PASS/FAIL scorecard plus bounded repair plan; use after synthesis, not to rewrite content.

Why use Deliverable Selfloop on TypingMind?

Because you install it once and use it with any model. Deliverable Selfloop is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deliverable Selfloop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/deliverable-selfloop. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Deliverable Selfloop?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deliverable Selfloop?

As many as you like. As long as a model supports skills, you can use Deliverable Selfloop with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deliverable Selfloop AI skill free?

It is published on GitHub by WILLOSCAR. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Deliverable Self-Loop

Inputs

Output

Dispatch rule

Script boundary

Acceptance

Non-goals

Bundled files

Frequently asked questions

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