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Multi Deliverable Tracking

OrganizationPopular
HKUDS
multi-deliverable-tracking

Track and complete all required deliverables before finishing a task

Overview

PublisherHKUDS
RepositoryOpenSpace
Skill namemulti-deliverable-tracking
Stars
7.8K
Forks
923
Bundled files
Instructions only
LicenseMIT
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by HKUDS on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/multi-deliverable-tracking
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/HKUDS/OpenSpace.git /tmp/OpenSpace
mkdir -p .claude/skills
cp -r /tmp/OpenSpace/benchmarks/gdpval/skills/multi-deliverable-tracking .claude/skills/multi-deliverable-tracking
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multi Deliverable Tracking 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 Multi Deliverable Tracking 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 Multi Deliverable Tracking 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.

Multi-Deliverable Task Completion

Purpose

Ensure ALL required outputs are completed before concluding a task, preventing premature task termination when multiple deliverables are required. This skill includes mandatory enforcement steps that MUST be followed.

Core Workflow

Step 1: Identify All Deliverables Upfront

CRITICAL: You MUST complete this step before producing ANY deliverable or taking any other action.

At task start, explicitly enumerate EVERY required output:

  • Parse task requirements carefully for action verbs: "create", "generate", "produce", "deliver", "write", "send"
  • Count distinct deliverables (files, reports, emails, code modules, analyses, etc.)
  • Document this list visibly in working notes
  • Note any dependencies between deliverables

MANDATORY OUTPUT FORMAT: Before any other work, output this exact format:

DELIVERABLES_IDENTIFIED:
1. [filename/description] - [brief description]
2. [filename/description] - [brief description]
...
TOTAL_DELIVERABLES: N

Example identification:

Required Deliverables:
1. [ ] Email to Juvoxa CEO
2. [ ] Follow-up analysis report
Total: 2 deliverables

Step 2: Create a Completion Tracker

CRITICAL: This tracker MUST be created and visible throughout task execution.

Maintain a visible checklist throughout task execution:

DELIVERABLE TRACKER
===================
[ ] Deliverable 1: [brief description]
[ ] Deliverable 2: [brief description]
[ ] Deliverable 3: [brief description]

Completed: 0/3

Update this tracker AFTER each deliverable is fully completed (not just started).

Step 2.5: Filesystem Verification [MANDATORY BEFORE COMPLETION]

Before considering any deliverable complete, verify it exists:

  • For file deliverables: Use list_dir or run_shell (ls/cat) to confirm the file exists
  • For output deliverables: Verify the output was actually produced in the response stream
  • Document verification with: [✓] filename verified in filesystem

Step 3: Work Through Deliverables Systematically

  • Complete each deliverable fully before marking it done
  • Update the tracker immediately after completion
  • If a deliverable fails, note the failure but continue to others if possible
  • Return to failed items after completing others

Step 4: Final Verification Before Stopping

Before concluding the task, run this mandatory checklist: CRITICAL ENFORCEMENT: You are BLOCKED from outputting until this verification passes.

FINAL VERIFICATION (REQUIRED BEFORE <COMPLETE>)
==================
1. Review original task requirements
2. Count total deliverables identified: ___
3. Count deliverables marked complete: ___
4. Do counts match? YES/NO
5. If NO: Identify missing deliverables and complete them
6. If YES: Verify each deliverable exists and meets quality standards
7. Filesystem verification (REQUIRED):
   - [ ] file1.ext: VERIFIED (list_dir/run_shell confirmed)
   - [ ] file2.ext: VERIFIED (list_dir/run_shell confirmed)

**BLOCKING RULE:** If counts do not match OR any file cannot be verified in the filesystem, you MUST:
1. NOT output <COMPLETE>
2. Identify the missing/unverified deliverables
3. Complete and verify them
4. Re-run this verification checklist

**Only output <COMPLETE> when all deliverables are verified present in the filesystem or output stream.**

Only stop when all deliverables are verified complete.

Step 5: Self-Check Questions

Before marking task complete, ask:

  • "Did I create EVERY output the task requested?"
  • "Are there any deliverables I started but didn't finish?"
  • "Would someone reviewing my work see all required outputs?"
  • "Have I verified EACH deliverable exists in the filesystem using list_dir or run_shell?"
  • "Is my DELIVERABLES_IDENTIFIED output present and accurate?"

Common Pitfalls to Avoid

❌ Stopping after first deliverable - Just because one output is done doesn't mean the task is complete ❌ Stopping after first deliverable - Just because one output is done doesn't mean the task is complete

❌ Outputting without filesystem verification - You must verify files exist before completion

❌ Skipping DELIVERABLES_IDENTIFIED output - This mandatory output must appear before any work begins

❌ Assuming implicit completion - Don't assume related outputs are "part of" a main deliverable unless explicitly stated

❌ Losing track mid-task - The tracker must stay visible and updated throughout

❌ Counting drafts as complete - Only mark done when the deliverable meets quality standards

Code Example: Tracker Template

python
# Deliverable tracking template
deliverables = {
    "email_to_ceo": {"status": "pending", "file": "ceo_email.txt"},
    "analysis_report": {"status": "pending", "file": "analysis.pdf"},
    "summary_doc": {"status": "pending", "file": "summary.docx"},
}

def mark_complete(name):
    deliverables[name]["status"] = "complete"
    print(f"✓ {name} completed")
    print(f"Progress: {sum(1 for d in deliverables.values() if d['status'] == 'complete')}/{len(deliverables)}")

def all_complete():
    return all(d["status"] == "complete" for d in deliverables.values())

When to Apply This Skill

Use this pattern whenever a task involves:

  • Multiple files to create
  • Several analyses or reports
  • Multiple communications (emails, messages)
  • Code with multiple modules/components
  • Any task with enumerated requirements (1), 2), 3)...)

Remember

The task is NOT complete until ALL deliverables are complete. One done ≠ all done.

Frequently asked questions

What does the Multi Deliverable Tracking AI skill do?

Track and complete all required deliverables before finishing a task

Why use Multi Deliverable Tracking on TypingMind?

Because you install it once and use it with any model. Multi Deliverable Tracking 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 Multi Deliverable Tracking in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/multi-deliverable-tracking. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Multi Deliverable Tracking?

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 Multi Deliverable Tracking?

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

Is the Multi Deliverable Tracking AI skill free?

Yes. It is published on GitHub by HKUDS under the MIT license. 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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