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Pokemon Champions Ui Session

Community
pmwl0128
pokemon-champions-ui-session

Work on a Quark Drive Console (local Pokémon Champions UI) session — read its request, format, attached team and artifacts through the pcui CLI, run the normal UEP team-building flow with each gate's artifact submitted as it runs, and wait for the reader's answers in the panel so the local web page shows progress live. Trigger when the user asks to handle a Quark Drive local session (including an exact session id), continue one after deciding or answering in the panel, or wants their work shown in the local Console. Chinese examples include "处理 Quark Drive 本地会话 <id>", "继续处理会话 <id>", "处理当前 Pokémon UI 会话", "看看 UI 会话的决策". English examples include "work on Quark Drive local session <id>", "continue session <id>", "work on the current Pokémon UI session". Japanese examples include "Quark Driveのローカルセッション <id> を処理して", "セッション <id> を続けて". Requires the pokemon-champions-team skill (and its sibling data skills) — this skill only adds the session I/O, never replaces the UEP discipline.

Overview

Publisherpmwl0128
Repositoryquark_drive_skills
Skill namepokemon-champions-ui-session
Stars
78
Forks
4
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 pmwl0128 on GitHub. Read the source before you install it.

Installation

Install the Pokemon Champions Ui Session 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/pmwl0128/quark_drive_skills/tree/main/frontend/bridge/pcui/companion
  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/pmwl0128/quark_drive_skills.git /tmp/quark_drive_skills
mkdir -p .claude/skills
cp -r /tmp/quark_drive_skills/frontend/bridge/pcui/companion .claude/skills/pokemon-champions-ui-session
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pokemon Champions Ui Session 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 Pokemon Champions Ui Session 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 Pokemon Champions Ui Session 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.

Quark Drive Console Session

The local web panel (pcui serve, shown as Quark Drive Console) and you collaborate through ONE shared session store: the panel is the session's eyes and hands, you are the decision participant. This skill adds exactly one thing to your normal workflow — session I/O through pcui. The team-building discipline itself is unchanged: follow pokemon-champions-team's assisted workflow (UEP) exactly as you always do.

CLI

The pcui command for this machine (works from any directory): {{PCUI_CMD}} (bridge directory: {{BRIDGE_DIR}})

{{PCUI_CMD}} session show <S>                           # meta (intent, format, team) + heads + ledger
{{PCUI_CMD}} run <op> --session <S> [--file f.json] [--context ctx.json] [--args '<json>'] [--out out.json]
                                                        # run one team operator in the daemon AND
                                                        # submit its gate artifacts to the session
{{PCUI_CMD}} session wait <S> [--for decision|intake-answer] [--timeout 1800]
                                                        # block until the reader answers in the panel;
                                                        # prints that artifact (rc 3 on timeout)
{{PCUI_CMD}} artifact get --session <S> --kind <kind>    # latest artifact of that kind (verbatim)
{{PCUI_CMD}} artifact put --session <S> --kind <kind> --revision <R> --file out.json
                                                        # for what no operator produces; R is the
                                                        # session revision read BEFORE doing the work
{{PCUI_CMD}} session create --intent "<request>" [--format double] [--kind build] [--team team.json]
{{PCUI_CMD}} open --no-browser [--session <S>]          # a fresh one-time link to the panel

MCP instead of the CLI: when the quark-drive MCP tools are available ({{PCUI_CMD}} mcp; {{PCUI_CMD}} mcp --print-config shows how the user registers it), use them — same daemon, same semantics: session_list, session_show, session_create, artifact_get, artifact_put (pass the revision you read; a conflict comes back as is, so re-read and decide), run_operator (with session and output_path, exactly like run --session --out), session_wait (returns status: timeout to call again) and open_link. Everything below applies to both.

Typed deterministic tools also expose team_review, team_try, review_claims, slate_init, team_fill, team_tune_cell, team_localize, team_counters, team_ko_scan, team_speed_ladder, team_scenario and team_threshold. Use their schemas instead of guessing generic arguments. File inputs are inline JSON, never local paths. output_path on review/try saves the full report from that run; Console receives a display summary. Copy the saved full report into subsequent claim/tuning inputs. Typed tools use the same daemon session and UEP gates as run_operator.

inference_prior and inference_init/add/status/remove/example/import_web/schema expose portable calculator cases. Every call supplies the current case and returns new JSON; there is no hidden case state, automatic team write or confirmed opponent set. Prior/observations are inline objects; keep frozen source evidence and the calculator's uncertainty. These query tools do not append a session gate. A surviving example is a hypothesis, not a recommendation or factual identification.

Canceling session_wait stops that wait. Canceling a run_operator reply does not retract work already sent to the daemon: it may still complete and commit. Read session_show before retrying.

Always pass the exact session id on every command. session use is a shared, human-only CLI convenience; another assistant or terminal can change it. Do not use it or rely on environment variables persisting between agent shell calls. Give run an --out file whenever the output may be large (frame, slate-evaluate, diagnose): stdout then carries a one-line summary that lists the artifacts it submitted, and the file holds the FULL result body, directly consumable by the next gate (without an {op, rc, result} envelope). A failed output-file save may follow a successful gate commit: read the returned completion/artifact details before deciding to rerun.

Gate artifacts: run --session submits them

Run every UEP gate operator through run <op> --session <S>. The daemon then submits, in ledger order:

opsubmitted kinds
context-audit (with --context)context (the context it read), audit
frameframe
slate-evaluateslate — the panel gets the summary view; YOUR output stays full
checkpointcheckpoint
answer-audit (with --file draft.json)draft (the draft it read), answer-audit
intake with "next": trueintake (the batch the panel shows as a form)
tunetune-result
diagnose (with --file team.json)team (the team it read), diagnose — the panel shows the report
review / diagnose --focusreview display summary; preserve full result for evidence
trytrial display summary; original team stays unchanged
review-claimsreview-claims recomputation result
slate-initslate-input candidate transport; does not certify delivery

A consecutive rerun of the same gate adds nothing. An intervening draft, answer or other gate makes it a new ledger application even when the result bytes match. Use artifact put only for content no operator produced. Everything you submit is shown live; the panel also shows which operator is running.

Workflow

  1. Find the session: a Console handoff names an exact session id — use that id, not the newest, and include it explicitly on every call. session show <S> gives meta.intent (the request typed in the panel), heads (latest artifact per kind) and the ledger. New requests may include meta.format, meta.requestKind, meta.team (the canonical attached team snapshot), meta.teamName and meta.environment. Read these before intake; use the attached builds for rework/diagnosis/tuning, preserving their completeness and the user's constraints. Old intent-only sessions remain valid. The task kind is a hint, not permission to skip the normal UEP gates or silently change the team.
  2. Read what exists: artifact get every kind in heads that matters — context, decision (the reader's checkpoint answer: chooseCandidate + candidateIndex into your slate's teams, requestChanges + note, or proceed) and intake-answer (below). A handoff that says the reader decided or answered in the panel means: read that artifact first and continue from it. A handoff that says review failed means: read answer-audit, fix what it names, and run answer-audit again.
  3. Work the UEP flow normally (context-audit → grounding → frame → assemble → slate-evaluate → checkpoint → answer-audit), with each gate operator run as run <op> --session <S>. Batch related lookups (ranking/landscape/repset/validate) into one session spec instead of many shell-outs; fall back to a raw team.py only when the daemon is down, then artifact put the gate outputs.
  4. Ask through the panel when the user is there: run intake --next as run intake --session <S> --args '{"next": true, "format": "<f>", "answered": [...]}' --context ctx.json, say in one line that the questions are on the panel, then session wait <S> --for intake-answer. The reader may still answer in conversation instead; either is a user answer. An intake-answer is {answers: [{id, option?, text?, skipped?}], note?}: apply each chosen option's maps_to exactly as for a numbered reply, echo a typed text back before committing it, and add every answered id to --answered for the next batch.
  5. Checkpoint pause: when the checkpoint output says pause:true (and the context did not encode direct_final/skip_checkpoint), tell the user to decide in the panel (give the open --no-browser --session <S> link if they may not have it open), then session wait <S> --for decision. Hosts that can run a command in the background (Claude Code) should do so and continue when it completes; otherwise run it in the foreground. On a timeout (rc 3), stop the turn; next time, read decision first and continue from it.
  6. Draft prose: narrate in the USER'S language, but name every ENTITY by English canonical. In tradeoffs, assumptions[].note, confidence_notes, convergence_rationale, context_summary: species, moves, items and abilities are written EXACTLY as they appear in operator output ("Whimsicott", "Last Respects") — never hand-translated. The panel localizes canonical names with dex authority at render time.
  7. Artifact size: the daemon rejects a body over ~2 MiB on artifact put. Only slate risks that, and run slate-evaluate --session <S> already submits the summary view. Keep the FULL slate output for your own reasoning and for answer-audit (its claim recompute re-hashes the untrimmed matchup_risk) — pass the full output file as answer-audit's slate_output.
  8. Tuning: SP adjustment intent becomes build-context.benchmarks[] and run tune --session <S> --context ctx.json --file team.json per the team skill; the panel renders its cards. Sessions from older panels may carry a tune-request ({note, member?, member_set?, team?, form_benchmarks?}); translate its note the same way and keep any form_benchmarks the user already ran.
  9. Conflicts: a gate commits all its artifacts against the revision it read before running. A 409 means the inputs are stale or another run is active: re-read the session and reader's answers, decide what is still applicable, then rerun. Never blindly repeat an old write. Both MCP artifact_put and CLI artifact put --revision <R> require the revision read before doing the work. On conflict, review the newer artifacts and revise your output as needed; do not merely fetch a newer revision and retry the old output. The CLI never chooses a baseline.

When the user started in conversation rather than the panel and wants to follow along there, create the session yourself (session create --intent … [--team team.json]), keep its id explicit, and hand the user the open --no-browser --session <S> link.

Frequently asked questions

What does the Pokemon Champions Ui Session AI skill do?

Work on a Quark Drive Console (local Pokémon Champions UI) session — read its request, format, attached team and artifacts through the pcui CLI, run the normal UEP team-building flow with each gate's artifact submitted as it runs, and wait for the reader's answers in the panel so the local web page shows progress live. Trigger when the user asks to handle a Quark Drive local session (including an exact session id), continue one after deciding or answering in the panel, or wants their work shown in the local Console. Chinese examples include "处理 Quark Drive 本地会话 <id>", "继续处理会话 <id>", "处理当前 P...

Why use Pokemon Champions Ui Session on TypingMind?

Because you install it once and use it with any model. Pokemon Champions Ui Session 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 Pokemon Champions Ui Session in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pmwl0128/quark_drive_skills/tree/main/frontend/bridge/pcui/companion. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pokemon Champions Ui Session?

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 Pokemon Champions Ui Session?

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

Is the Pokemon Champions Ui Session AI skill free?

It is published on GitHub by pmwl0128. 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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