Deliverable Intake

Community

scdenney

deliverable-intake

Turns raw captures (dictated braindumps in a deliverable's inbox/, or recent macwhspr dictation-log entries) into proposed planning-wiki changes, shown as one unified diff. Classifies each statement as a decision, evidence claim, question, task, or dropped item, traces it to its capture paragraph, tags unconfirmed names, numbers, and citations UNRESOLVED, and writes decisions only with the author's assent. Edits only the planning wiki, never source files. Use when asked to intake, absorb notes, or process the inbox, or after dictating notes from a talk, meeting, or reading session.

Overview

Publisher	scdenney
Repository	`open-science-skills`
Skill name	`deliverable-intake`
Stars	69
Forks	3
Bundled files	Instructions only
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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by scdenney on GitHub. Read the source before you install it.

Installation

Install the Deliverable Intake 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/scdenney/open-science-skills/tree/main/plugin/skills/deliverable-intake
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/scdenney/open-science-skills/tree/main/plugin/skills/deliverable-intake

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/scdenney/open-science-skills.git /tmp/open-science-skills
mkdir -p .claude/skills
cp -r /tmp/open-science-skills/plugin/skills/deliverable-intake .claude/skills/deliverable-intake

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

Use it in TypingMind

Enable Deliverable Intake 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 Intake 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 Intake 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 intake

Full guide: docs/deliverable-pipeline.md in the repository.

The author thinks out loud; the wiki stays authoritative. This skill is the seam between the two. It reads captures verbatim, proposes where each statement belongs, and applies nothing the author has not seen.

Inputs

The nearest deliverable.yml upward from cwd (or the path given). Read it, then planning_dir/00-README.md and every file that index names. That is the current state of the wiki; the proposal is a diff against it.
inbox/*.md that do not end in .done.md, oldest first. A capture is read verbatim and never edited.
--from-log N: show the last N entries of ~/.config/macwhspr/cleanup_log.jsonl ({ts, raw, cleaned} pairs) as a numbered list with the first line of each, and ask which to import. An imported entry is written to inbox/YYYY-MM-DD-HHMM-log.md with the cleaned text (the raw text appended under a  marker) before anything else happens. Nothing is imported without being named.

Classify, then trace

Walk the capture paragraph by paragraph. Each statement gets exactly one class:

Class	Goes to	Needs assent?
decision	the wiki file that owns the topic (`00-README.md` "Decisions recorded here", or the numbered file)	yes, always
evidence claim	the wiki file for the section it supports, marked with the capture reference	no, but a claim naming a source that is not in `sources_of_truth` or the bib is written as `UNRESOLVED: <claim>`; when the manifest names a `knowledge_base`, the report lists each such source as a `process-source` candidate (drop the PDF in `sources/unprocessed/`) so intake feeds the knowledge base rather than bypassing it
question	`08-open-questions.md`, open list	no
task	`08-open-questions.md` under a `Tasks` heading; one due before the next session is also named in the report, so the closure step carries it into HANDOFF	no
dropped	nowhere; listed in the report with the reason (duplicate, already resolved, off-topic)	no

Every proposed line carries a trace: (inbox/2026-09-10-debrief.md ¶3). A capture paragraph that yields no proposal is listed as dropped with its reason, so the author can see the capture was read whole.

Names, dates, numbers, and citations that the capture states but the wiki cannot confirm stay as dictated and are tagged UNRESOLVED. Do not correct a dictated name against a guess; do not invent a citation to match a claim; do not resolve a number from memory.

Propose

Produce one unified diff across the wiki files (new files included) plus the dropped list. Show it. Then put the decisions to the author as a numbered list, each with the sentence that would be written and where; use AskUserQuestion when there are three or fewer, a numbered list otherwise.

Apply

Once the author has seen the diff, apply the non-decision hunks and each decision they approved, then rename the capture to *.done.md. Do not edit any existing HANDOFF.md entry, today's included; the intake is recorded by the closure step (/oss:finished) as Intake: <capture> -> <n> decisions, <n> claims, <n> questions, <n> tasks, <n> dropped, and if the session ends without closure the renamed capture and the wiki diff are the record. Record which capture paragraphs were promoted (a  line at the end of the .done.md file) so a partially accepted capture is never promoted twice. On refusal of any decision, leave that line out and keep the capture unrenamed until the author says otherwise.

Report

Five lines: captures read; counts by class; UNRESOLVED items (each with what would resolve it); decisions applied; what to run next (/oss:deliverable-lint if the wiki changed materially, otherwise nothing).

Notes

One worker, no subagents; a cheap model suffices.
A reaction to a lint report is a capture like any other: dictate it into inbox/, then intake it; findings the author accepts become tasks, findings they reject go under a Rejected findings heading in 08-open-questions.md (the authoritative wiki, not NOTES.md) with the finding id and one line of reason, so the next lint does not raise them again.
Register vocabulary as it appears: a proper noun the dictation mangled twice belongs in ~/.config/macwhspr/vocab.md (offer, never append silently).

Frequently asked questions

What does the Deliverable Intake AI skill do?

Why use Deliverable Intake on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/scdenney/open-science-skills/tree/main/plugin/skills/deliverable-intake. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deliverable Intake?

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 Intake?

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

Is the Deliverable Intake AI skill free?

It is published on GitHub by scdenney. 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 intake

Inputs

Classify, then trace

Propose

Apply

Report

Notes

Frequently asked questions

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