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Uncertainty Management

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UnpaidAttention
uncertainty-management

Classify what you actually know vs. what you're reconstructing from memory, verify before acting on unverified specifics, and communicate uncertainty without hedging everything. Trigger this whenever you are about to write a call to a library API, CLI flag, config key, or endpoint you have not confirmed in this session; when answering questions about version-specific or fast-moving library behavior; when quoting numbers, versions, or citations; or when you notice the thought "it's usually something like this". Do NOT trigger for ambiguity about what the USER wants (use task-planning's ask-vs-proceed rule) — this skill covers factual/technical uncertainty only.

Overview

PublisherUnpaidAttention
Repositoryfable5-methodology
Skill nameuncertainty-management
Stars
94
Forks
21
Bundled files
Instructions only
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 UnpaidAttention on GitHub. Read the source before you install it.

Installation

Install the Uncertainty Management 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/UnpaidAttention/fable5-methodology/tree/main/skills/uncertainty-management
  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/UnpaidAttention/fable5-methodology.git /tmp/fable5-methodology
mkdir -p .claude/skills
cp -r /tmp/fable5-methodology/skills/uncertainty-management .claude/skills/uncertainty-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Uncertainty Management 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 Uncertainty Management 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 Uncertainty Management 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.

Uncertainty Management

Confabulated specifics — invented parameter names, flags, version behaviors — are worse than admitted ignorance, because they fail late and erode all trust in everything else you said. The countermeasure is mechanical classification, not vibes.

Step 1: Classify every factual claim into one of three bins

  • Verified: you read it in this session — in this codebase, in fetched docs, in command output, or by running it. → Act on it; state it as fact.
  • Confident recall: stable, universally replicated knowledge (Python list semantics, HTTP status codes, SQL join behavior, POSIX basics). → Act on it; no hedge.
  • Plausible reconstruction: exact API signatures, parameter names, CLI flags, config keys, default values, version-specific behavior, anything in a library that moves fast or is younger than your training data. → Do NOT act on it as-is. Verify (Step 2) or flag (Step 3).

The tell for bin 3: the detail is specific (an exact name, an exact flag) and your source is "it's usually something like this". Specificity without a source is where hallucinated APIs come from. When in doubt between bins 2 and 3, treat as 3 — the check costs seconds.

Step 2: The verification ladder (cheapest rung first)

  1. This codebase: grep for existing usage of the API. The repo is ground truth for how it's called here — version, style, and wrappers included.
  2. Installed source: read the signature in node_modules / site-packages / vendor / the crate source; or run python -c "help(x)", tool --help, man tool.
  3. A 10-second experiment: run the one-liner in a REPL or scratch script. Executing beats reading — it verifies the whole stack, not just the signature.
  4. Current docs: fetch documentation (docs MCP / official site / web search) when the library is newer than your knowledge or the behavior is version-sensitive. Check the installed version in the lockfile FIRST so you read the right docs.
  5. No rung available (offline, no env): write the code anyway, but mark it — Step 3.

Step 3: Communicating uncertainty without drowning the signal

  1. Hedge ONLY load-bearing uncertainty. A response that hedges everything hides the one hedge that matters. Verified and confident-recall facts are stated plainly.
  2. Format for a real unknown — best answer + confidence + concrete check:

    "bulk_create should batch these into one query — verify the generated SQL with echo=True; this differs across ORM versions."

  3. Never invent a citation, version number, benchmark figure, or API name to make an answer look complete. "I don't know the exact flag — check tool --help" is a correct and complete answer.
  4. In code you couldn't verify, leave the marker at the call site: # UNVERIFIED: confirm param name against installed vX.Y — so the reader knows exactly where the risk sits.

Step 4: Assumption bookkeeping

When you proceed on an assumption, record it AT THE MOMENT you make it (note file, todo, or message), and restate ALL assumptions in one compact block in your final message ("Assumptions: X; Y; Z"). An assumption mentioned once at step 2 of 14 is invisible by delivery time — the final block is what the user actually reads.

Checklist — run before delivering any code that touches a library

  • Every imported function/method I call: seen in this repo, in installed source, in fetched docs, or in a REPL this session?
  • Every CLI flag and config key: confirmed against --help or docs for the INSTALLED version?
  • Every number I quoted (limits, defaults, versions, prices): has a source, or is flagged?
  • Anything remaining unverified: explicitly marked at the call site AND in the report?

Worked example — weak vs. correct

Task: "Use the stripe library to create a subscription with a 14-day trial."

Weak: write stripe.Subscription.create(customer=cid, trial_days=14, plan=pid) from memory. (trial_days and plan are plausible reconstructions — one is renamed, the other deprecated; it fails at runtime, in production, on a payment path.)

Correct: (1) grep the repo — two existing Subscription.create calls use items=[...] and the wrapper billing/client.py; (2) check installed version in the lockfile; (3) confirm the trial parameter in that version's source/docs → trial_period_days. Deliver using the repo's own wrapper, and state: "Used trial_period_days per stripe-python vN.N (verified against installed package)."

Done when

Every bin-3 claim in the deliverable has either climbed the verification ladder to "verified" or carries an explicit UNVERIFIED marker with the exact check the reader should run; the final message contains the consolidated assumptions block; and nothing in your output states a reconstructed specific as fact.

Frequently asked questions

What does the Uncertainty Management AI skill do?

Classify what you actually know vs. what you're reconstructing from memory, verify before acting on unverified specifics, and communicate uncertainty without hedging everything. Trigger this whenever you are about to write a call to a library API, CLI flag, config key, or endpoint you have not confirmed in this session; when answering questions about version-specific or fast-moving library behavior; when quoting numbers, versions, or citations; or when you notice the thought "it's usually something like this". Do NOT trigger for ambiguity about what the USER wants (use task-planning's ask-v...

Why use Uncertainty Management on TypingMind?

Because you install it once and use it with any model. Uncertainty Management 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 Uncertainty Management in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/UnpaidAttention/fable5-methodology/tree/main/skills/uncertainty-management. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Uncertainty Management?

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 Uncertainty Management?

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

Is the Uncertainty Management AI skill free?

It is published on GitHub by UnpaidAttention. 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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