Step: Implement /learn command definition

Author: apiad

.gemini/commands/learn.toml

@@ -0,0 +1,35 @@
+description = "Explore and master a new library or topic, generating a permanent project skill."
+
+prompt = """
+You are an expert learning agent. Follow this robust 6-phase workflow to explore and master the following topic: {{args}}
+
+### Phase 1: Environment Audit
+1. **Identify Topic:** Determine the specific library, framework, or concept to be learned.
+2. **Check Presence:** Use shell commands (e.g., `pip show`, `npm list`, `go list`) to check if it's already installed.
+3. **Install if Missing:** If it's a library and not installed, ask the user for permission or use standard package managers to install it.
+
+### Phase 2: Research & Mapping
+1. **Research Core Concepts:** Use `google_web_search` and `web_fetch` to identify the core API surface, common patterns, and architecture.
+2. **Survey Local Source:** Briefly survey local source files to identify existing usage or potential integration points.
+3. **Build Learning Map:** Create a structured "Learning Map" outlining what needs to be explored, including core features, edge cases, and common "gotchas."
+
+### Phase 3: User Approval (Checkpoint)
+1. **Approval:** Use `ask_user` to present the "Learning Map" to the user for approval or modification. Do not proceed until approved.
+
+### Phase 4: Grounded Experimentation
+1. **Invoke Generalist:** Use the `generalist` subagent to write and run small, independent test scripts (in Python, JS, etc.) to verify API behavior, performance, and specific use cases identified in the Learning Map.
+2. **Deep Dive:** Perform deep-dives into complex areas or potential "gotchas" through iterative experimentation.
+
+### Phase 5: Skill Codification
+1. **Consult CLI Help:** Invoke the `cli_help` agent to understand the exact internal structure for a project skill.
+2. **Create Skill:** Create a new skill in `.gemini/skills/<skill-name>/SKILL.md`.
+3. **Populate Reference:** Add a comprehensive reference, idiomatic coding examples, and "gotchas" discovered during experimentation.
+4. **Reference Files:** If relevant, create additional `reference-*.md` subfiles for specialized use cases.
+
+### Phase 6: Asset Management & Cleanup
+1. **Store Assets:** Move successful, high-value experiment scripts to the skill's assets folder (if appropriate) or link them in the documentation.
+2. **Cleanup:** Delete temporary experiment files and artifacts to keep the workspace clean.
+3. **Report:** Provide a final summary of what was learned and where the new skill is located.
+
+Do not stop until the skill is fully codified and the workspace is clean.
+"""

TASKS.md

@@ -18,7 +18,7 @@ Put done tasks into the Archive.
 
 ## Active Tasks
 
-- [ ] Implement the new `/learn` command and specialized learning agent to automate technology exploration and skill codification. (See plan: plans/implement-learn-command.md)
+- [/] In Progress (@gemini-cli) Implement the new `/learn` command and specialized learning agent to automate technology exploration and skill codification. (See plan: plans/implement-learn-command.md)
 
 ---
 

journal/2026-03-20.md

@@ -15,3 +15,4 @@
 [2026-03-20T10:19:25] - feat(docs): update install.sh to generate generic boilerplate for scaffolding files
 [2026-03-20T10:26:46] - chore(release): version 0.18.0
 [2026-03-20T12:01:05] - Plan and task created for /learn command implementation.
+[2026-03-20T12:03:54] - Implemented /learn command definition and verified with tests.

makefile

@@ -4,6 +4,7 @@ all: test lint
 
 test:
 	@echo "Running tests..."
+	python3 -m unittest discover tests
 
 docs-serve:
 	@mkdocs serve

tests/__init__.py

@@ -0,0 +1,22 @@
+import os
+import tomllib
+import unittest
+
+class TestCommands(unittest.TestCase):
+    def test_learn_command_exists(self):
+        path = ".gemini/commands/learn.toml"
+        self.assertTrue(os.path.exists(path), f"{path} does not exist")
+        
+    def test_learn_command_structure(self):
+        path = ".gemini/commands/learn.toml"
+        if not os.path.exists(path):
+            self.skipTest("learn.toml does not exist yet")
+        with open(path, "rb") as f:
+            data = tomllib.load(f)
+            self.assertIn("description", data)
+            self.assertIn("prompt", data)
+            self.assertGreater(len(data["description"]), 0)
+            self.assertGreater(len(data["prompt"]), 0)
+
+if __name__ == "__main__":
+    unittest.main()