Summarise Ecosystem Results logo

Summarise Ecosystem Results

Organization
modem-dev
summarise-ecosystem-results

Use when a user says "summarise ecosystem results", "summarize this ty ecosystem report", "what changed in this ecosystem run?", or asks to summarise or summarize ty ecosystem results for a Ruff PR from a PR number, PR URL, GitHub ecosystem-results comment, or detailed HTML report.

Overview

Publishermodem-dev
Repositoryossrules
Skill namesummarise-ecosystem-results
Stars
65
Forks
2
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 modem-dev on GitHub. Read the source before you install it.

Installation

Install the Summarise Ecosystem Results 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/modem-dev/ossrules/tree/main/public/files/ruff/.agents/skills/summarise-ecosystem-results
  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/modem-dev/ossrules.git /tmp/ossrules
mkdir -p .claude/skills
cp -r /tmp/ossrules/public/files/ruff/.agents/skills/summarise-ecosystem-results .claude/skills/summarise-ecosystem-results
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Summarise Ecosystem Results 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 Summarise Ecosystem Results 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 Summarise Ecosystem Results 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.

Summarise Ecosystem Results

Priorities

  1. Reproduce every retained source-attributable behavior with the exact environment used by the Actions run.
  2. For every distinct source-attributable behavior change, produce the smallest provenance-preserving reproducer obtainable through the complete advanced-minimization workflow.
  3. Eliminate every third-party import unless identified ty behavior depends on that library's identity or third-party search-path classification, and eliminate every unnecessary standard-library import. Retain an import only after verifying that neither removing it nor inlining its definitions preserves the underlying behavior.
  4. Lead the report with new or meaningfully changed project failures, including intermittent severe failures, then cover stable diagnostic changes and fully minimized examples.
  5. Keep execution, audit, and traceability bookkeeping out of the report.

GitHub CLI Telemetry

Prefix every direct or indirect gh invocation with GH_TELEMETRY=false, including GH_TELEMETRY=false uv run --script scripts/collect_ty_ecosystem_run_metadata.py .... Require the same of subagents. Codex tool calls may start separate shells, so an export in an earlier call is insufficient.

Deliverable

Create PR_<number>_ECOSYSTEM_SUMMARY.md at the repository root by adapting assets/report-template.md. The finished artifact must be GitHub-flavored Markdown suitable for a GitHub comment, with each prose paragraph and list item on one source line.

Use the template's structure and omissions as the report contract. Remove all placeholders and HTML comments. Link external source locations with permalinks such as [project file.py:123](permalink); never emit raw URLs.

If summarising an ecosystem report is the only thing you're asked to do in a Codex App thread, you should rename that thread to "PR ecosystem summary".

Reporting Policy

  • Focus on new or meaningfully changed behavior relative to the merge base. Evaluate individual diagnostics and failure outcomes, not a project's overall flaky or persistent status.
  • Omit flaky diagnostic changes, unchanged failures, and frequency fluctuations that leave the observed outcomes unchanged.
  • Report new, fixed, or meaningfully changed panics, crashes, overflows, and timeouts, including merge-base and PR run frequencies when intermittent behavior is involved.

Workflow

  1. Freeze the evidence. Preserve any report URL or ecosystem-results comment explicitly supplied by the user before identifying the PR. For PR-only input, find its ecosystem-results comment and linked detailed report. Capture the matching Actions run and attempt as described in references/evidence-acquisition.md; never replace a supplied report with the PR's current report. Recover exact-run metadata promptly, then prepare both exact-revision profiling binaries and the shared configuration before assigning subagent work. Ignore later comment edits, PR updates, and workflow runs. Prefer the selected attempt's validated full-report/diff.json as the authoritative structured change inventory, retain its matching frozen HTML report, and use the comment for orientation when available. Fall back to the frozen HTML report if the JSON report is unavailable.
  2. Identify changed outcomes. Inspect the structured diff for added, removed, and modified projects; stable diagnostic additions, removals, and rewrites; project failures; and intermittent exit-status changes. Preserve diagnostic levels, duplicate occurrences, source permalinks, project strictness, panic evidence, and observed run frequencies. Exclude flaky diagnostics and frequency-only noise without excluding stable diagnostics or changed severe failures from flaky projects. Use the matching HTML report for visual context, or as the primary evidence when structured JSON cannot be obtained safely.
  3. Reproduce from scratch. Ignore retained memories and previous local artifacts. Load the minimizing-ty-ecosystem-changes skill, collect exact-run metadata once, and reproduce every retained, source-attributable diagnostic or panic before explaining or minimizing it. Reproduce intermittent severe failure changes with the reported merge-base and PR run counts. Verify retained outcomes without recoverable source against their captured statuses, stderr, panic evidence, and run frequencies.
  4. Minimize to completion with provenance. For each distinct source-attributable behavior change, follow the complete advanced-minimization workflow until an exhaustive pass finds no further reduction. Derive the reproducer from a cited ecosystem entry through a verified reduction chain; never replace that entry with an independently invented example demonstrating superficially similar behavior. Before accepting a reproducer, attempt to remove every import, inline every third-party definition, and inline relevant standard-library definitions. Retain a third-party import only when identified ty behavior depends on that library's identity or third-party search-path classification and neither removing the import nor inlining its definitions preserves the underlying behavior. If a genuine external blocker prevents completion, report that blocker to the user and identify the task as incomplete. Do not silently substitute an unminimized excerpt or present a partially minimized report as finished.
  5. Group by cause. Group entries only when the same base-to-PR behavior, underlying trigger, explanation, and reproducer account for every entry. Identical diagnostic text or displayed @Todo types do not establish equivalence.
  6. Find existing ty issues. When a diagnostic change exposes a pre-existing shortcoming in ty, search the astral-sh/ty issue tracker for the precise underlying behavior. Link matching issues directly from the relevant report section; do not mistake incorrect or incomplete third-party annotations for ty shortcomings.
  7. Write and verify. Fill the report template and verify that every source-attributable behavior change has a fully minimized, provenance-preserving reproducer. Check every change number, link, diagnostic, retained import, reproducer's source provenance, and causal fingerprint when required. Verify that every retained third-party import is essential to identified ty behavior that depends on that library's identity or third-party search-path classification, that no avoidable standard-library import remains, and that no source-attributable section contains an unminimized excerpt. Then run GH_TELEMETRY=false uv run --only-group dev --locked prek run --files PR_<number>_ECOSYSTEM_SUMMARY.md. Present the Markdown file as the finished product only after these checks pass.

Parallel execution

This skill explicitly requests subagents when the report contains multiple affected projects or independently investigable entries.

Once the exact-run metadata, both profiling binaries, and shared configuration are ready, spawn as many subagents as the available concurrency budget and independent work allow, reserving one slot for the primary agent. Keep available slots occupied by assigning further work as subagents finish.

Assign disjoint projects or explicit report entries. Apparent similarity may guide scheduling, but does not establish causal equivalence. Follow all existing requirements for exhaustive reproduction, verified reduction chains, exhaustive minimization, and grouping by verified cause.

The primary agent owns the frozen evidence, shared profiling binaries, configuration, coordination, and final report. Follow references/subagent-handoff.md for handoff and shared-artifact requirements.

If multiple independent assignments exist but no subagents are spawned, record the specific reason.

Frequently asked questions

What does the Summarise Ecosystem Results AI skill do?

Use when a user says "summarise ecosystem results", "summarize this ty ecosystem report", "what changed in this ecosystem run?", or asks to summarise or summarize ty ecosystem results for a Ruff PR from a PR number, PR URL, GitHub ecosystem-results comment, or detailed HTML report.

Why use Summarise Ecosystem Results on TypingMind?

Because you install it once and use it with any model. Summarise Ecosystem Results 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 Summarise Ecosystem Results in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/modem-dev/ossrules/tree/main/public/files/ruff/.agents/skills/summarise-ecosystem-results. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Summarise Ecosystem Results?

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 Summarise Ecosystem Results?

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

Is the Summarise Ecosystem Results AI skill free?

Yes. It is published on GitHub by modem-dev 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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