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Get Started Free →Suggest relevant GitHub Copilot Custom Agents files from the awesome-copilot repository based on current repository context and chat history, avoiding duplicates with existing custom agents in this repository, and identifying outdated agents that need updates.
.claude/skills/suggest-awesome-github-copilot-agents/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-20 | ✗→✓ | ▲ Improved | 81% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 53% | 0% |
| case-01 | ✗→✗ | = Same ✗ | 510% | 0% |
| case-02 | ✗→✗ | = Same ✗ | 273% | 0% |
| case-03 | ✗→✗ | = Same ✗ | 33% | 0% |
Analyze current repository context and suggest relevant Custom Agents files from the GitHub awesome-copilot repository that are not already available in this repository. Custom Agent files are located in the agents folder of the awesome-copilot repository.
fetch tool..github/agents/ folderhttps://raw.githubusercontent.com/github/awesome-copilot/main/agents/<filename>)AWAIT user request to proceed with installation or updates of specific custom agents. DO NOT INSTALL OR UPDATE UNLESS DIRECTED TO DO SO.
.github/agents/ folder#fetch tool to download assets, but may use curl using #runInTerminal tool to ensure all content is retrieved#todos tool to track progress🔍 Repository Patterns:
🗨️ Chat History Context:
Display analysis results in structured table comparing awesome-copilot custom agents with existing repository custom agents:
| Awesome-Copilot Custom Agent | Description | Already Installed | Similar Local Custom Agent | Suggestion Rationale | | ------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------- | ---------------------------------- | ------------------------------------------------------------- | | amplitude-experiment-implementation.agent.md | This custom agent uses Amplitude's MCP tools to deploy new experiments inside of Amplitude, enabling seamless variant testing capabilities and rollout of product features | ❌ No | None | Would enhance experimentation capabilities within the product | | launchdarkly-flag-cleanup.agent.md | Feature flag cleanup agent for LaunchDarkly | ✅ Yes | launchdarkly-flag-cleanup.agent.md | Already covered by existing LaunchDarkly custom agents | | principal-software-engineer.agent.md | Provide principal-level software engineering guidance with focus on engineering excellence, technical leadership, and pragmatic implementation. | ⚠️ Outdated | principal-software-engineer.agent.md | Tools configuration differs: remote uses 'web/fetch' vs local 'fetch' - Update recommended |
*.agent.md files in .github/agents/ directorydescriptionhttps://raw.githubusercontent.com/github/awesome-copilot/main/agents/<filename>fetch toolgithubRepo tool to get content from awesome-copilot repository agents folder.github/agents/ directoryWhen outdated agents are identified:
.github/agents/ directory| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 6,062 | 7,432 | +23% | 1 | 1 | 0% | 360 | 2,197 | +510% | 0 | 0 | — |
case-02 | fail→fail | 7,239 | 7,051 | -3% | 1 | 1 | 0% | 505 | 1,884 | +273% | 0 | 0 | — |
case-03 | fail→fail | 7,502 | 22,879 | +205% | 1 | 1 | 0% | 1,387 | 1,840 | +33% | 0 | 0 | — |
case-04 | fail→fail | 7,258 | 7,023 | -3% | 1 | 1 | 0% | 1,061 | 2,117 | +100% | 0 | 0 | — |
case-05 | fail→fail | 9,044 | 6,454 | -29% | 1 | 1 | 0% | 893 | 1,946 | +118% | 0 | 0 | — |
case-06 | fail→fail | 1,692 | 6,392 | +278% | 1 | 1 | 0% | 278 | 1,710 | +515% | 0 | 0 | — |
case-07 | fail→fail | 7,299 | 8,747 | +20% | 1 | 1 | 0% | 1,352 | 1,840 | +36% | 0 | 0 | — |
case-08 | fail→fail | 5,561 | 9,336 | +68% | 1 | 1 | 0% | 968 | 2,279 | +135% | 0 | 0 | — |
case-09 | fail→fail | 9,723 | 13,951 | +43% | 1 | 1 | 0% | 1,660 | 1,944 | +17% | 0 | 0 | — |
case-10 | fail→fail | 4,828 | 1,581 | -67% | 1 | 1 | 0% | 930 | 1,806 | +94% | 0 | 0 | — |
case-11 | fail→fail | 33,095 | 6,429 | -81% | 1 | 1 | 0% | 6,175 | 2,063 | -67% | 0 | 0 | — |
case-12 | fail→fail | 10,720 | 6,657 | -38% | 1 | 1 | 0% | 1,976 | 1,992 | +1% | 0 | 0 | — |
case-13 | fail→fail | 8,073 | 3,230 | -60% | 1 | 1 | 0% | 1,343 | 2,070 | +54% | 0 | 0 | — |
case-14 | fail→fail | 12,579 | 2,733 | -78% | 1 | 1 | 0% | 2,286 | 2,000 | -13% | 0 | 0 | — |
case-15 | fail→fail | 18,574 | 10,078 | -46% | 1 | 1 | 0% | 2,489 | 2,156 | -13% | 0 | 0 | — |
case-16 | fail→fail | 11,784 | 7,330 | -38% | 1 | 1 | 0% | 1,590 | 2,635 | +66% | 0 | 0 | — |
case-17 | fail→fail | 12,625 | 13,006 | +3% | 1 | 1 | 0% | 2,342 | 4,062 | +73% | 0 | 0 | — |
case-18 | fail→fail | 11,685 | 12,330 | +6% | 1 | 1 | 0% | 2,053 | 2,529 | +23% | 0 | 0 | — |
case-19 | fail→fail | 5,739 | 26,422 | +360% | 1 | 1 | 0% | 1,094 | 5,133 | +369% | 0 | 0 | — |
case-20 | fail→pass | 6,762 | 2,916 | -57% | 1 | 1 | 0% | 1,145 | 2,076 | +81% | 0 | 0 | — |
case-21 | fail→fail | 12,772 | 6,735 | -47% | 1 | 1 | 0% | 2,132 | 2,025 | -5% | 0 | 0 | — |
case-22 | fail→pass | 6,787 | 2,023 | -70% | 1 | 1 | 0% | 1,174 | 1,795 | +53% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 7 counted toward the lift figure. The other 15 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +9 percentage points is the difference between those two pass rates over the 7 comparable cases. 5 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.