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Gdd Bandit Status

Community
hegemonart
gdd-bandit-status

Surface read-only per-(agent, bin, delegate) bandit posterior snapshot - alpha/beta/mean/stddev/count/last-used per arm. Phase 27.5 (v1.27.5) diagnostic. Use when investigating 'why did the bandit pick tier X for agent Y?' or when verifying posterior convergence after enabling adaptive_mode: full.

Overview

Publisherhegemonart
Repositoryget-design-done
Skill namegdd-bandit-status
Stars
3
Forks
1
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 hegemonart on GitHub. Read the source before you install it.

Installation

Install the Gdd Bandit Status 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/hegemonart/get-design-done/tree/main/scripts/skill-templates/bandit-status
  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/hegemonart/get-design-done.git /tmp/get-design-done
mkdir -p .claude/skills
cp -r /tmp/get-design-done/scripts/skill-templates/bandit-status .claude/skills/gdd-bandit-status
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gdd Bandit Status 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 Gdd Bandit Status 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 Gdd Bandit Status 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.

gdd-bandit-status

Role

You are a deterministic, read-only diagnostic skill. You do not spawn agents and do not modify the posterior. You read .design/telemetry/posterior.json (path declared by scripts/lib/bandit-router.cjs's DEFAULT_POSTERIOR_PATH), aggregate per-(agent, bin, delegate, tier) arm state, and emit a single Markdown table. Read-only per Phase 27.5 D-11 - to reset, use {{command_prefix}}bandit-reset (Phase 23.5). See ./reference/bandit-integration.md for setup, interpretation, and convergence guidance.

Invocation Contract

  • Input: none.
  • Output: a Markdown bandit-status table to stdout. The table is the entire output.

Procedure

1. Locate the posterior file

Read .design/telemetry/posterior.json. Missing → emit empty-state message:

## Bandit Posterior Snapshot

No posterior data yet — run a few pipeline cycles with `adaptive_mode: full` first.

No posterior data found at `.design/telemetry/posterior.json`.

Possible reasons:
- `adaptive_mode` is `static` or `hedge` (bandit silent — see `.design/budget.json`).
- No spawns have fired since Phase 27.5 wiring landed.
- Posterior was cleared via `{{command_prefix}}bandit-reset`.
- You are running in interactive Claude Code: the posterior is updated (learns) only on the SDK / headless `session-runner` path. In interactive `adaptive_mode: full` the bandit samples from configured priors but does not learn from in-session outcomes.

See `reference/bandit-integration.md` ("Where adaptive routing actually learns") for setup guidance.

Note: the posterior only moves (learns) on the SDK / headless session-runner path. In interactive Claude Code with adaptive_mode: full, the bandit samples from the configured priors but does not currently update them in-session. See reference/bandit-integration.md.

Skip to Section 4 (Record). Parse failure (truncated/corrupted) → emit Posterior file exists but is unparseable. Run {{command_prefix}}bandit-reset to start fresh, or restore from a backup.

2. Parse the posterior

Schema:

json
{
  "schema_version": "1.0.0",
  "generated_at": "<ISO>",
  "arms": [{ "agent": "...", "bin": "...", "tier": "...", "delegate": "...", "alpha": N, "beta": N, "last_used": "...", "count": N }]
}

The delegate field is optional - absent = Phase 23.5 legacy slice (rendered as - in the table).

3. Render the table

Compute per arm: mean = alpha / (alpha + beta) (3 decimals), stddev = sqrt(alpha*beta / ((alpha+beta)^2 * (alpha+beta+1))) (3 decimals).

Sort by (agent ASC, bin ASC, delegate ASC where '-' first, tier ASC opus<sonnet<haiku, last_used DESC). Group by agent for readability.

Emit:

## Bandit Posterior Snapshot

Per-(agent, bin, delegate, tier) posterior state. Read-only — to reset use `{{command_prefix}}bandit-reset` (Phase 23.5).

Posterior file: `.design/telemetry/posterior.json` (last updated: <generated_at>)
Total arms: <count>

| Agent | Bin | Delegate | Tier | Alpha | Beta | Mean | Stddev | Count | Last Used |
|-------|-----|----------|------|-------|------|------|--------|-------|-----------|
| ...   | ... | ...      | ...  | ...   | ...  | ...  | ...    | ...   | ...       |

> Mean = alpha / (alpha + beta). Stddev = sqrt(alpha*beta / ((alpha+beta)^2 * (alpha+beta+1))).
> Delegate '-' = Phase 23.5 legacy slice (equivalent to 'none').
> See `reference/bandit-integration.md` for interpretation.
> Read-only — use `{{command_prefix}}bandit-reset` to clear posterior state.

Precision: alpha/beta 2 decimals; mean/stddev 3 decimals; count integer; last_used truncated to minute (YYYY-MM-DDTHH:MM); null last_used renders -.

After the table, surface a per-(agent, bin) best-arm summary: for each unique pair, identify highest-mean arm (tie-broken by count DESC) - answers "why did the bandit pick tier X?" at a glance.

4. Record

Append one JSONL line to .design/skill-records.jsonl: {"skill":"gdd-bandit-status","ts":"<ISO>","arms_seen":<count>,"posterior_present":<bool>}. Skill writes ONLY to skill-records.jsonl (telemetry); never touches the posterior.

Cross-references

  • ./reference/bandit-integration.md - operator guide; interpretation patterns.
  • scripts/lib/bandit-router.cjs (Phase 23.5) - posterior shape, DEFAULT_POSTERIOR_PATH, loadPosterior().
  • scripts/lib/bandit-router/integration.cjs (27.5-01), hooks/budget-enforcer.ts (27.5-02), scripts/lib/session-runner/index.ts (27.5-03), scripts/lib/bandit-arbitrage.cjs (27.5-04), {{command_prefix}}bandit-reset (Phase 23.5) - only surface that mutates the posterior.

Frequently asked questions

What does the Gdd Bandit Status AI skill do?

Surface read-only per-(agent, bin, delegate) bandit posterior snapshot - alpha/beta/mean/stddev/count/last-used per arm. Phase 27.5 (v1.27.5) diagnostic. Use when investigating 'why did the bandit pick tier X for agent Y?' or when verifying posterior convergence after enabling adaptive_mode: full.

Why use Gdd Bandit Status on TypingMind?

Because you install it once and use it with any model. Gdd Bandit Status 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 Gdd Bandit Status in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/bandit-status. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gdd Bandit Status?

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 Gdd Bandit Status?

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

Is the Gdd Bandit Status AI skill free?

Yes. It is published on GitHub by hegemonart 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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