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Manage Ml Backlog

Organization
probabl-ai
manage-ml-backlog

Record experiment outcomes and triage idea files. A promoted idea moves from the Ideas table into a Backlog row. Record an experiment outcome in History and triage idea files. Summarize open, discarded, and aside ideas in the JOURNAL Ideas table. Move a row into Backlog when it is promoted, and keep the idea file. Also supports the model-entry selection mode: show real B<N> rows supplied by the deterministic CLI and consume one into a proposal. Trigger after audit, when a run finishes, when the user asks what to try next or to triage idea files, or when model-ml-pipeline routes its Backlog choice here. This is cadence, not a methodology owner.

Overview

Publisherprobabl-ai
Repositoryskore-skills
Skill namemanage-ml-backlog
Stars
144
Forks
9
Bundled files
1
LicenseBSD-3-Clause
Links
Skill sourceRepositoryHomepage
  • 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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by probabl-ai on GitHub. Read the source before you install it.

Installation

Install the Manage Ml Backlog 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/probabl-ai/skore-skills/tree/main/skills/manage-ml-backlog
  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/probabl-ai/skore-skills.git /tmp/skore-skills
mkdir -p .claude/skills
cp -r /tmp/skore-skills/skills/manage-ml-backlog .claude/skills/manage-ml-backlog
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Manage Ml Backlog 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 Manage Ml Backlog 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 Manage Ml Backlog 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.

Manage ML Backlog

Replace iterate-as-cadence. Do not own setup, exploratory data analysis, build, smoke, evaluate, or audit methodology.

Human-facing prose

Details: setup-workspace references/human_facing_prose.md. JOURNAL rows, design-note Status / Results, and # comments describe this experiment's outcome — not the skills framework, the CLI, or the command that produced an output. Questions and replies use the same data-science language — not skill ids, G-* names, or the wrapper CLI. <!-- results-embed: … --> is a site marker. Authoring hints stay in this skill. style is ruff only.

The CLI writes JOURNAL with sections in order: Status, Data understanding, Modeling decisions, History, Ideas, Backlog. History, Ideas, and Backlog start as header-only tables. Column contracts stay here (Stem, Intent, Status, Headline result, Report, Design note; Question, Status, Experiment, Source; #, Item, Source). Do not put those contracts back into HTML comments in the file.

Model-entry selection mode

When model-ml-pipeline calls with the backlog array from python -m skore_skills model choices:

  1. When the user has not named a B<N>, present exactly those rows in their returned order and AskUserQuestion for one pick. One row, or a message that already names a B<N>, is the pick: do not ask again. Carry each row's Item and Source as the option context, and say in 2–4 lines what the pick authorizes (a design note to approve) and what it does not (no model code yet). A file link is an addition, never the context. Do not rescan into a different menu and do not add an idea.
  2. Turn the selected row's Item + Source into a Proposal. Ask only for missing shaping facts; do not invent a Method from a one-line item. Do not ask Yes / No on the proposal.
  3. Return that proposal to model-ml-pipeline. Do not ask to confirm it. After the model stage creates and populates the design note, remove only the selected Backlog row and add the planned History row. Preserve every other stable B index.

This mode does not require a report/audit digest and does not run the outcome-recording procedure below. Empty Backlog is a routing error: return to model-ml-pipeline; do not fabricate B1.

Record-outcome mode

This mode is only a dispatch from model-ml-pipeline, evaluate-ml-pipeline, or audit-ml-pipeline at that caller's end of turn. A user saying the run finished, or "record it", is a direct Procedure turn: record the outcome, then End of turn (site build --if-stale when site policy is true, then git end-turn --stage backlog). Do not treat that user request as this mode.

When model-ml-pipeline, evaluate-ml-pipeline, or audit-ml-pipeline calls at end of turn with the normalized G-REPORT-LOCATOR, optional headline, and G-AUDIT-FINDING. This is the only path that records an outcome without a full backlog turn. Audit may have been skipped; the locator remains required and the finding becomes n/a — audit not run. If the caller omitted the locator, run python -m skore_skills loop locator --stem <stem> and paste JSON locator. If it omitted the finding, run python -m skore_skills audit finding --stem <stem> (stop → n/a — audit not run / n/a — audit digest unavailable). Do not rephrase either string.

Run Procedure steps 1-3 and nothing else:

  1. Step 1 — python -m skore_skills status; require an approved stem.
  2. Step 2 — read journal/JOURNAL.md; scaffold the index if it is missing.
  3. Step 3 — update the matching History row and design-note Status block from the digest or user-supplied headline when available. Paste G-REPORT-LOCATOR and G-AUDIT-FINDING verbatim into their separate Status lines. Do not change State or Approved by user on. Also refresh the journal/JOURNAL.md Status rows Last experiment and Last result. Insert or replace ## Results between Status and Notebooks from digest text, not HTML. ### Metrics is a heading only.

Then return to the caller. Do not read journal/ideas/, do not edit the Ideas table, and do not open the idea-triage menu — the caller did not ask what to try next.

Do not dispatch audit-ml-pipeline in this mode; the digest is already in hand and dispatching would bounce back here. Do not run this skill's End of turn either: the caller owns convert / site / git end-turn and the User-facing close. This mode writes History; it does not replace the caller's chat close.

The Procedure guards still bind. Never mark done while smoke is red, and never invent a metric — no digest and no user-supplied value means use n/a, not a guess. Never construct a missing backend URL. Record n/a — backend did not expose a locator in both markdown destinations when the digest has no authoritative locator.

Procedure

  1. Run python -m skore_skills status. When recording a done outcome, run python -m skore_skills design consent --stem <stem>. ask / stop → do not mark done. Also require green smoke evidence and a normalized report locator. An audit digest is optional. G-AUDIT-FINDING is required as one of: the value returned by audit, n/a — audit not run, or n/a — audit digest unavailable.
  2. Read journal/JOURNAL.md History and Backlog. If the index is missing, run python -m skore_skills scaffold --journal. Do not write or paste the file. The CLI writes sections in order: Status, Data understanding, Modeling decisions, History, Ideas, and Backlog. If that command cannot run this turn, name it and stop. After the file exists, edit the existing History and Backlog tables (columns: Stem, Intent, Status, Headline result, Report, Design note; and #, Item, Source). Ideas columns are Question, Status, Experiment, Source; triage owns that table. A planned History row uses n/a in Report. Stable B<N> indices. Do not renumber on removal. Record-outcome does not edit Ideas.
  3. If recording a run: copy the headline metric from the audit digest or the user's value. Do not invent numbers. With no digest and no user headline, skip the headline in one line and leave the History status unchanged. Do not write done with headline n/a. Update the matching History row (planned → done only if smoke passed and a headline result exists). Headline metric remains the source for History and Last result; never substitute G-AUDIT-FINDING for performance. Copy the digest's persisted-report locator into the History Report cell and the design note's Persisted report Status line. Paste that string verbatim; do not paraphrase it as "normalized" or rewrite the Hub URL. If the digest has none, write n/a — backend did not expose a locator in both places; do not derive or guess a URL. Copy G-AUDIT-FINDING verbatim into the design note's Audit findings line. Audit skipped → n/a — audit not run; missing/errored digest → n/a — audit digest unavailable. The Status update copies Headline result, Persisted report, and Audit findings. Do not change State or Approved by user on. Then insert or replace ## Results in the design note, between ## Status and ## Notebooks. Summarize from the audit digest — its cell outputs carry repr(report), ## Checks summary, and ## Metrics summary as text. With no audit this turn, fall back to scratch/results/<stem>/report.txt, which evaluate writes. Write ### Report overview from the report text, then ### Checks when that section exists. ### Metrics is a heading only when ## Metrics summary exists: do not transcribe the metric table or its values. The site embeds scratch/results/<stem>/metrics.html under that heading. History and Last result still get the single headline metric. Evaluation-only (audit skipped): Report overview only — do not invent Checks or Metrics subsections. After Metrics, add one ### subsection per extra Display cell the audit appended, using a human title and a <!-- results-embed: <slug> --> comment with the accessor name as <slug> so site build can inject the viewer. Report overview, Checks, and each extra subsection are 2–4 sentences from that cell's output. Do not copy G-AUDIT-FINDING, do not parse *.html, and do not paste iframes (site build injects those). Do not add <!-- results-embed: audit -->. The audit notebook viewer is audit/<stem>.nb.html, which site build places under ## Notebooks. If no subsection has a source, skip the Results section.
  4. Idea triage, separate from record-outcome. Read journal/ideas/*.md and the Ideas table. If ## Ideas is missing, insert that header-only table between History and Backlog. Do not rewrite the other sections. A file's Triage line is open, promoted, discarded, or aside. A missing line is open. An open file whose Question and Source already match one Backlog row is set to promoted without asking, its Ideas row is removed, and no duplicate row is appended. Match both fields so two user ideas do not collapse. An open file with no Ideas row gets one: Question as plain text, Status open, Experiment from the file, Source copied verbatim. Ask the other open files: promote / discard / set aside. Write that value on the Triage line and keep the file. Promote removes that Ideas row and appends a stable B<N> row (Item from Question, Source copied verbatim) and sets promoted. Discard sets discarded on the file and the Ideas row and adds no Backlog row. Set aside sets aside on the file and the Ideas row and adds no Backlog row. A promoted idea is absent from Ideas. promoted, discarded, and aside leave the default queue; a later pass does not ask about them. Do not create a design note here. Do not delete an idea file. Changing Triage does not remove or renumber a Backlog row. An empty folder is a one-line skip: there are no idea files to triage, and it does not fabricate B1. When no file is open and tagged files remain, say in one line how many are promoted, discarded, and set aside, and offer to revisit. Do not retag until the user picks a file. On revisit, the same three choices apply. Promoting then appends B<N> only when that Question and Source are not already a row, and removes the Ideas row. The only other follow-up is offering to shape an idea or search the literature when those skills are installed. Do not load either skill, and do not start a search or a shaping menu, until the user picks one. Missing skill → one-line skip; do not invent that skill's search or shaping steps. After the user picks, that skill writes the idea file and returns here; triage the new file in this same mode. When the user picks an existing B<N> to draft, return that row to model-ml-pipeline, which can create its design-note shell with python -m skore_skills scaffold --journal --stem <NN_short_name>. Do not draft that template in this backlog turn.

Stop conditions

  • Do not design or implement the next experiment in this turn.
  • Do not dispatch setup or audit by skill id. Returning a selected row or proposal to model-ml-pipeline is required. Do not ask Yes / No on that proposal.
  • In model-entry selection mode, do not invent a Backlog row or remove it before the paired design note exists.
  • Do not invent metrics.
  • Do not derive, shorten, or merge G-AUDIT-FINDING with the headline metric. Copy each into its owned field.
  • Do not change design-note State or Approved by user on. Record-outcome copies Headline result, Persisted report, and Audit findings.
  • Do not parse scratch/results/<stem>/*.html when writing ## Results. Summarize Report overview and Checks from the digest, or from report.txt on the evaluation-only path. ### Metrics is a heading only.
  • Do not paste a JOURNAL.md body or recreate the index from memory.
  • Do not mark done while smoke is red.
  • Do not delete an idea file. Triage writes its Triage line and moves or updates its Ideas row.
  • Design approval is owned by model-ml-pipeline; this skill only returns a proposal or selected Backlog row.

End of turn

Direct turns reach here, including a user request to record a finished run. Dispatched record-outcome mode returns before this section.

If policy.site is true, export-ml-site is installed, run python -m skore_skills site build --if-stale. Do not run notebook convert. Skip in one line otherwise. If site build errors with mkdocs-material is required, load add-python-package for mkdocs-material (agent) and build once more. Do not pixi add / uv add. If that skill is missing, or the retry still fails, name the error in one line. Name a build error; do not fail the backlog turn.

Run python -m skore_skills git end-turn --stage backlog. If JSON action is invoke, load persist-ml-git only if status.skills.persist-ml-git is true and stop; that skill returns to triage. If persist is missing, name the pending staged paths and stop. Otherwise load triage-ml-task only if status.skills.triage-ml-task is true; else stop. Do not run git commit in this skill.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Manage Ml Backlog AI skill do?

Record experiment outcomes and triage idea files. A promoted idea moves from the Ideas table into a Backlog row. Record an experiment outcome in History and triage idea files. Summarize open, discarded, and aside ideas in the JOURNAL Ideas table. Move a row into Backlog when it is promoted, and keep the idea file. Also supports the model-entry selection mode: show real B<N> rows supplied by the deterministic CLI and consume one into a proposal. Trigger after audit, when a run finishes, when the user asks what to try next or to triage idea files, or when model-ml-pipeline routes its Backlog...

Why use Manage Ml Backlog on TypingMind?

Because you install it once and use it with any model. Manage Ml Backlog 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 Manage Ml Backlog in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/probabl-ai/skore-skills/tree/main/skills/manage-ml-backlog. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Manage Ml Backlog?

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 Manage Ml Backlog?

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

Is the Manage Ml Backlog AI skill free?

Yes. It is published on GitHub by probabl-ai under the BSD-3-Clause 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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