The Skills of Algora

[!NOTE] Status: Content ready · Media: Generated cover and original SVG illustration · Last verified: 2026-09-05
Primary capability: Packaging reusable agent expertise

[!TIP] Download this learner kit and use the extraction and setup guide. Keep the original starter untouched.

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flowchart LR
    accTitle: The Skills of Algora capability map
    accDescr: A skill is a reusable procedure, not always-on instructions or a deterministic lifecycle hook.
    T["Task arrives"] --> Q{"Skill relevant?"}
    Q -->|No| N["Do not load unrelated expertise"]
    Q -->|Yes| S["Load focused procedure"]
    S --> V["Run the existing check"]
    V --> E["Report command, result and limits"]

Legend. The relevance decision separates loading expertise from running its prescribed verification.

Explanation. A skill is a reusable procedure, not always-on instructions or a deterministic lifecycle hook.

Official references

These official sources are the authority for product behavior and availability. Recheck them when using a different surface or after a product update.

• Add skills to Copilot cloud agent
• Agent Skills specification
• VS Code Agent Skills

Story

Algora’s guilds reveal a focused spellbook only when the quest actually calls for it.

The fantasy is a memory aid; the engineering lesson requires observable, reproducible evidence.

Learning objectives

• Define a reusable skill with a specific trigger and explicit non-goals.
• Require an executed check, command, exit code and observed result in its procedure.
• Test relevant and irrelevant requests without claiming universal automatic activation.

Prerequisites

For tools and personal accounts, complete the prerequisites guide. For terminal-only study, follow the extracted-kit CLI route; VS Code-specific evidence remains separate.

Evidence boundary: The verifier checks skill content; actual discovery and invocation depend on the selected harness.

Estimated session: 45–75 minutes after prerequisites; actual duration varies. Never use production secrets or customer data.

Concept explanation

A skill packages reusable expertise an agent can load when relevant. Instructions apply automatically, prompts are manually invoked, and custom agents define roles and tools. A good skill has narrow triggers, non-goals, an ordered safe procedure, and evidence requirements. Availability and discovery behavior are surface-dependent; record the exact host used and do not claim universal activation.

Concrete use case

An evidence-report skill is useful after a code change with a test command. A request to brainstorm a name should not trigger a fake verification report just because the skill exists.

Vocabulary checkpoint

• Role: Ask, Plan, Agent, or a custom agent.
• Harness/surface: the runtime or product surface in which a role operates.
• Target: the selected session destination, such as Local, Copilot, or Cloud where exposed.
• Environment: the folder, worktree, local machine, Codespace, or remote environment.
• Instructions: automatically applied durable context.
• Prompt: a manually invoked task template.
• Skill: reusable expertise loaded when relevant.
• Custom agent: a role with instructions, tools, and optional handoffs.
• MCP: Model Context Protocol.
• Evidence: a path, diff, command result, trace, or review decision.

Ask → Plan → Agent workflow

Ask — investigate

1. Identify the real user outcome and current source of truth.
2. Inspect relevant files, instructions, tools, permissions, and existing checks.
3. Cite concrete paths or platform evidence for every important finding.
4. Record uncertainty and do not infer unavailable capabilities.

Gate: No implementation until the current state and evidence are understood.

Plan — design

1. State scope, non-goals, assumptions, risks, and trust boundaries.
2. Select the minimum role, tools, authority, and environment.
3. Define acceptance criteria, verification commands, review, and reset.
4. Mark any Preview or experimental dependency and provide a fallback.

Gate: Another learner should be able to predict completion from the plan.

Agent — execute

1. Make the smallest reversible change or produce the planned artifact.
2. Use short inspect → change → verify loops.
3. Preserve command output, exit status, diffs, traces, or platform records.
4. Stop when acceptance criteria are met; do not perform unrelated cleanup.

Review — challenge

1. Inspect the complete diff or artifact.
2. Compare each result with the plan and acceptance criteria.
3. Run the narrowest relevant existing check, broadening only when justified.
4. Record limitations and unresolved risks.
5. Score the work with rubric.md.

Guided mission

1. Prepare one isolated copy

1. Download and extract the algora-skills kit into a new work-drive directory.
2. Read KIT-START.md at its root. Open starter/ as the VS Code workspace when testing discovery, but run the verifier from the kit root.
3. Inspect starter/.github/skills/evidence-report/SKILL.md, verify.js before editing.
4. From the extracted kit root, run node verify.js.
5. Record the documented starter rejection. A missing runtime or unrelated crash is not the expected exercise result.

2. Investigate and plan

Copyable baseline command, from the extracted kit root:

node verify.js

In Ask, request a trace of the inspected files and what the verifier actually observes. Challenge any claim about live execution that is not supported by output.

Use this planning prompt:

Define when evidence-report is relevant and when it is not. Specify the procedure for running an existing check and reporting real output, including blocked or failed checks.
Do not implement yet. Identify affected files, the negative case and a safe reset.

3. Implement the reviewed slice

1. Approve only the named starter artifact and necessary focused tests.
2. Ask Agent to implement one slice; inspect proposed commands before execution.
3. Run node verify.js again from the kit root, or node ../verify.js from starter/.
4. Compare the exact result with the checkpoint below and review the complete diff.
5. Record host discovery or live activity separately when available. Do not enable extra services to manufacture a passing result.

[!IMPORTANT] Checkpoint: The file has valid metadata, a narrow use case and no instruction to invent successful results. The verifier checks skill content; actual discovery and invocation depend on the selected harness.

4. Prove a check can reject a mistake

Remove the observed-result requirement and confirm the structural check rejects it. Restore the rule and compare one in-scope and one out-of-scope request.

Finish with the adventure-specific capability evidence in the rubric, not just the presence of a file.

Intentional failure: The Skill That Answers Everything

Perform this only in a disposable environment:

Give a skill the trigger “use for all coding.” It becomes noisy or competes with unrelated guidance because the activation boundary is meaningless.

Recovery

Return to Ask, identify the violated boundary, narrow the plan, remove unnecessary authority or context, and repeat the smallest relevant verification. Document the causal lesson rather than merely stating that the attempt failed.

Independent challenge

Split one overloaded skill into two cohesive skills and justify their positive and negative trigger scenarios.

Constraints:

• Do not copy the guided mission verbatim.
• Do not add tools, permissions, or cloud resources without a stated need.
• Do not claim quality, performance, compatibility, or availability without executed evidence.
• Keep fantasy language subordinate to technical clarity.

Evidence checklist

• Source-grounded Ask findings.
• Approved plan with scope, non-goals, risks, checks, and reset.
• Agent diff or artifact limited to the declared scope.
• Verification output with command, status, or platform record.
• Intentional-failure root cause and recovery.
• Independent challenge result.
• Explicit limitations and feature-status notes.
• Completed rubric.md.

Reset instructions

1. Save your diff, command output and limitations from the disposable kit.
2. Stop only the process or learning session you started. Do not stop other projects.
3. If you initialized Git in the kit, inspect git status --short there and restore only your named exercise files from its local baseline.
4. Otherwise, extract the original ZIP into a new unused directory for another attempt; do not overwrite your current work.
5. Remove only exercise-owned configurations, worktrees or remote resources after reviewing anything worth preserving.

The curriculum source and other projects must remain unchanged. A reset of the copied kit is not a repository-wide hard reset.

Next adventure

The Agents of Stellaris