🚀 Socket Launch Week Day 5:Introducing Repository Access Permissions and Custom Roles.Learn more →
Sign In

learnship

Package Overview
Dependencies
Maintainers
1
Versions
50
Alerts
File Explorer

Advanced tools

License
Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

learnship - npm Package Compare versions

Comparing version
2.2.1
 to
2.2.2
+1
-1
.claude-plugin/plugin.json
{
"name": "learnship",
"description": "Agentic engineering done right — 57 structured workflows, persistent memory across sessions, integrated learning partner, and impeccable UI design system. Works with Claude Code, Windsurf, Cursor, Gemini CLI, OpenCode, and Codex.",
"version": "2.2.1",
"version": "2.2.2",
"author": {

@@ -6,0 +6,0 @@ "name": "Favio Vazquez",

+1
-1
.cursor-plugin/plugin.json

@@ -5,3 +5,3 @@ {

"description": "Agentic engineering done right — 57 structured workflows, persistent memory across sessions, integrated learning partner, and impeccable UI design system.",
"version": "2.2.1",
"version": "2.2.2",
"logo": "assets/logo.png",

@@ -8,0 +8,0 @@ "author": {

+1
-1
gemini-extension.json
{
"name": "learnship",
"version": "2.2.1",
"version": "2.2.2",
"description": "Agentic engineering done right — 57 structured workflows, persistent memory across sessions, integrated learning partner, and impeccable UI design system.",

@@ -5,0 +5,0 @@ "author": "Favio Vazquez",

+14
-0
learnship/workflows/challenge.md

@@ -45,3 +45,10 @@ ---

subagent_type="learnship-challenger",
description="Product challenge",
prompt="
<agent_definition>
You are a learnship challenger running the PRODUCT lens. Your job is to stress-test whether this proposal is worth building.
Ask forcing questions that expose weak assumptions. Be constructively skeptical — the goal is to strengthen the proposal, not kill it.
Return a clear verdict: proceed / rethink / reduce-scope.
</agent_definition>
<objective>

@@ -85,3 +92,10 @@ Run the PRODUCT lens challenge on this proposal:

subagent_type="learnship-challenger",
description="Engineering challenge",
prompt="
<agent_definition>
You are a learnship challenger running the ENGINEERING lens. Your job is to stress-test whether this proposal is technically sound.
Ask forcing questions that expose complexity, fragility, and hidden costs. Be constructively skeptical.
Return a clear verdict: proceed / rethink / reduce-scope.
</agent_definition>
<objective>

@@ -88,0 +102,0 @@ Run the ENGINEERING lens challenge on this proposal:

+6
-0
learnship/workflows/compound.md

@@ -44,3 +44,9 @@ ---

subagent_type="learnship-solution-writer",
description="Classify and extract solution",
prompt="
<agent_definition>
You are a learnship solution writer. Analyze conversation history to classify problems and extract structured solution metadata.
RESEARCH ONLY — do NOT write any files. Return text data to the orchestrator.
</agent_definition>
<objective>

@@ -47,0 +53,0 @@ RESEARCH ONLY — do NOT write any files.

+8
-0
learnship/workflows/debug.md

@@ -106,3 +106,11 @@ ---

subagent_type="learnship-debugger",
description="Investigate bug",
prompt="
<agent_definition>
You are a learnship debugger. Trace from user-facing symptoms inward to find root causes.
Read-first: understand the current design before proposing changes. Find the specific file and line where behavior diverges.
Confirm the root cause with: 'If this were fixed, would the symptom go away?'
One hypothesis at a time. Change one thing, verify, then move to the next.
</agent_definition>
<objective>

@@ -109,0 +117,0 @@ Investigate the bug described in [session_file].

+8
-0
learnship/workflows/execute-phase.md

@@ -163,3 +163,11 @@ ---

subagent_type="learnship-executor",
description="Execute plan [plan_id]",
prompt="
<agent_definition>
You are a learnship executor. Execute plan tasks one at a time, commit atomically after each.
Read the plan file, follow each task's action field exactly. Verify using the verify field.
Mark tasks done. Create SUMMARY.md when complete. Update STATE.md.
Never skip tasks. Never batch commits. One task = one commit.
</agent_definition>
<objective>

@@ -166,0 +174,0 @@ Execute plan [plan_id] of phase [phase_number]-[phase_name].

+12
-0
learnship/workflows/ideate.md

@@ -96,3 +96,9 @@ ---

subagent_type="learnship-researcher",
description="Quick research pass",
prompt="
<agent_definition>
You are a learnship researcher doing a quick, focused research pass.
Your training data is stale — use WebSearch to verify current state. Be concise.
</agent_definition>
<objective>

@@ -163,3 +169,9 @@ Quick research pass on: [specific question].

subagent_type="learnship-ideation-agent",
description="Ideate: [FRAME] lens",
prompt="
<agent_definition>
You are a learnship ideation agent. Generate improvement ideas grounded in the actual codebase — no abstract advice.
Every idea must cite specific files, patterns, or evidence. Be creative but practical.
</agent_definition>
<objective>

@@ -166,0 +178,0 @@ Generate 6-8 improvement ideas for this project using the [FRAME] lens.

+206
-25
learnship/workflows/new-project.md

@@ -471,47 +471,228 @@ ---

Spawn a dedicated researcher agent with the project context:
Display spawning indicator:
```
◆ Spawning 4 researchers in parallel...
→ Stack research
→ Features research
→ Architecture research
→ Pitfalls research
```
Spawn 4 parallel researcher agents — one per research dimension. Each agent writes ONE file.
```
Task(
subagent_type="learnship-researcher",
description="Stack research",
prompt="
<agent_definition>
You are a learnship researcher. Your training data is 6-18 months stale — treat it as hypothesis, not fact.
Verify before asserting. Flag uncertainty with confidence levels (HIGH/MEDIUM/LOW). Be prescriptive: 'Use X because Y' not 'Options are X, Y, Z.'
Tool priority: 1. WebSearch (ecosystem discovery — always include current year), 2. WebFetch (official docs), 3. Codebase scan.
</agent_definition>
<objective>
Research the domain ecosystem for a new project, then write 5 research files into .planning/research/.
Research the standard tech stack for [project domain]. Write .planning/research/STACK.md.
You MUST run WebSearch queries BEFORE writing the file. Do NOT write from training data alone.
</objective>
IMPORTANT: You MUST do online research BEFORE writing any files. Your training data is stale — verify everything.
<research_steps>
1. Read .planning/PROJECT.md to understand the project domain and goals
2. Run 2-3 WebSearch queries: '[domain] recommended tech stack [current year]', '[domain] best libraries [current year]'
3. WebFetch official docs for any key libraries discovered
4. Write .planning/research/STACK.md with confidence levels and source citations
</research_steps>
Phase 1 — INVESTIGATE (do this first):
<files_to_read>
- .planning/PROJECT.md (project context and goals)
</files_to_read>
<downstream_consumer>
Your STACK.md feeds into roadmap creation. Be prescriptive:
- Specific libraries with versions
- Clear rationale for each choice
- What NOT to use and why
</downstream_consumer>
<quality_gate>
- [ ] Versions are current (verified via WebSearch/WebFetch, not training data)
- [ ] Rationale explains WHY, not just WHAT
- [ ] Confidence levels assigned to each recommendation
</quality_gate>
<output>
Write to: .planning/research/STACK.md
Required sections: ## Recommended Stack, ## Alternatives Considered, ## What NOT to Use, ## Versions
</output>
"
)
Task(
subagent_type="learnship-researcher",
description="Features research",
prompt="
<agent_definition>
You are a learnship researcher. Your training data is 6-18 months stale — treat it as hypothesis, not fact.
Verify before asserting. Flag uncertainty with confidence levels (HIGH/MEDIUM/LOW). Be prescriptive: 'Use X because Y' not 'Options are X, Y, Z.'
Tool priority: 1. WebSearch (ecosystem discovery — always include current year), 2. WebFetch (official docs), 3. Codebase scan.
</agent_definition>
<objective>
Research what features [project domain] products typically have. Write .planning/research/FEATURES.md.
You MUST run WebSearch queries BEFORE writing the file. Do NOT write from training data alone.
</objective>
<research_steps>
1. Read .planning/PROJECT.md to understand the project domain and goals
2. Run at least 5 WebSearch queries to discover: standard tech stacks, recommended libraries, architecture patterns, common pitfalls, and best practices for this domain. Include the current year in queries.
3. Use WebFetch to read official documentation for any key libraries or frameworks discovered
4. Read the research persona at @./agents/researcher.md for research principles
2. Run 2-3 WebSearch queries: '[domain] features table stakes [current year]', '[domain] product features comparison'
3. WebFetch any relevant product comparison pages or feature lists
4. Write .planning/research/FEATURES.md with confidence levels and source citations
</research_steps>
Phase 2 — WRITE FILES (only after investigating):
Read each template from @./templates/research-project/ for the expected structure, then write each file based on your actual research findings (not just training data). Include confidence levels (HIGH/MEDIUM/LOW) and cite sources.
<files_to_read>
- .planning/PROJECT.md (project context and goals)
</files_to_read>
Files to write:
1. STACK.md — Must have: ## Recommended Stack, ## Alternatives Considered, ## What NOT to Use, ## Versions
2. FEATURES.md — Must have: ## Table Stakes, ## Differentiators, ## Anti-Features
3. ARCHITECTURE.md — Must have: ## Component Boundaries, ## Data Flow, ## Build Order, ## Integration Points
4. PITFALLS.md — Must have: ## Common Mistakes, ## Warning Signs, ## Prevention Strategies
5. SUMMARY.md — Must have: ## Recommended Stack, ## Table Stakes Features, ## Key Architecture Decisions, ## Top Pitfalls
<downstream_consumer>
Your FEATURES.md feeds into requirements definition. Categorize clearly:
- Table stakes (must have or users leave)
- Differentiators (competitive advantage)
- Anti-features (things to deliberately NOT build)
</downstream_consumer>
After writing all 5 files, run the verification command to confirm all files exist with required sections.
Return: confirmation that all 5 files pass verification, plus a summary of what you found.
<quality_gate>
- [ ] Categories are clear (table stakes vs differentiators vs anti-features)
- [ ] Complexity noted for each feature
- [ ] Dependencies between features identified
</quality_gate>
<output>
Write to: .planning/research/FEATURES.md
Required sections: ## Table Stakes, ## Differentiators, ## Anti-Features
</output>
"
)
Task(
subagent_type="learnship-researcher",
description="Architecture research",
prompt="
<agent_definition>
You are a learnship researcher. Your training data is 6-18 months stale — treat it as hypothesis, not fact.
Verify before asserting. Flag uncertainty with confidence levels (HIGH/MEDIUM/LOW). Be prescriptive: 'Use X because Y' not 'Options are X, Y, Z.'
Tool priority: 1. WebSearch (ecosystem discovery — always include current year), 2. WebFetch (official docs), 3. Codebase scan.
</agent_definition>
<objective>
Research how [project domain] systems are typically structured. Write .planning/research/ARCHITECTURE.md.
You MUST run WebSearch queries BEFORE writing the file. Do NOT write from training data alone.
</objective>
<research_steps>
1. Read .planning/PROJECT.md to understand the project domain and goals
2. Run 2-3 WebSearch queries: '[domain] architecture patterns', '[domain] system design components'
3. WebFetch architectural guides or documentation for the chosen stack
4. Write .planning/research/ARCHITECTURE.md with confidence levels and source citations
</research_steps>
<files_to_read>
- .planning/PROJECT.md (project description and goals)
- @./agents/researcher.md (research persona — read for research principles and tool strategy)
- @./templates/research-project/STACK.md (template for STACK.md)
- @./templates/research-project/FEATURES.md (template for FEATURES.md)
- @./templates/research-project/ARCHITECTURE.md (template for ARCHITECTURE.md)
- @./templates/research-project/PITFALLS.md (template for PITFALLS.md)
- @./templates/research-project/SUMMARY.md (template for SUMMARY.md)
- .planning/PROJECT.md (project context and goals)
</files_to_read>
<downstream_consumer>
Your ARCHITECTURE.md informs phase structure in roadmap. Include:
- Component boundaries (what talks to what)
- Data flow (how information moves)
- Suggested build order (dependencies between components)
</downstream_consumer>
<quality_gate>
- [ ] Components clearly defined with boundaries
- [ ] Data flow direction explicit
- [ ] Build order implications noted
</quality_gate>
<output>
Write to: .planning/research/ARCHITECTURE.md
Required sections: ## Component Boundaries, ## Data Flow, ## Build Order, ## Integration Points
</output>
"
)
Task(
subagent_type="learnship-researcher",
description="Pitfalls research",
prompt="
<agent_definition>
You are a learnship researcher. Your training data is 6-18 months stale — treat it as hypothesis, not fact.
Verify before asserting. Flag uncertainty with confidence levels (HIGH/MEDIUM/LOW). Be prescriptive: 'Use X because Y' not 'Options are X, Y, Z.'
Tool priority: 1. WebSearch (ecosystem discovery — always include current year), 2. WebFetch (official docs), 3. Codebase scan.
</agent_definition>
<objective>
Research what [project domain] projects commonly get wrong. Write .planning/research/PITFALLS.md.
You MUST run WebSearch queries BEFORE writing the file. Do NOT write from training data alone.
</objective>
<research_steps>
1. Read .planning/PROJECT.md to understand the project domain and goals
2. Run 2-3 WebSearch queries: '[domain] common mistakes gotchas', '[domain] pitfalls beginners'
3. WebFetch any detailed postmortems or lessons-learned articles
4. Write .planning/research/PITFALLS.md with confidence levels and source citations
</research_steps>
<files_to_read>
- .planning/PROJECT.md (project context and goals)
</files_to_read>
<downstream_consumer>
Your PITFALLS.md prevents mistakes in roadmap/planning. For each pitfall:
- Warning signs (how to detect early)
- Prevention strategy (how to avoid)
- Which phase should address it
</downstream_consumer>
<quality_gate>
- [ ] Pitfalls are specific to this domain (not generic advice)
- [ ] Prevention strategies are actionable
- [ ] Phase mapping included where relevant
</quality_gate>
<output>
Write to: .planning/research/PITFALLS.md
Required sections: ## Common Mistakes, ## Warning Signs, ## Prevention Strategies
</output>
"
)
```
Wait for the agent to complete, then proceed to Step 5c (verification) to confirm files were written correctly.
After all 4 agents complete, spawn a synthesizer to create SUMMARY.md from the other 4 files:
```
Task(
subagent_type="learnship-researcher",
description="Synthesize research",
prompt="
<objective>
Synthesize the 4 research files into a single SUMMARY.md.
Read all 4 files, extract the key findings, and write a cohesive summary.
</objective>
<files_to_read>
- .planning/research/STACK.md
- .planning/research/FEATURES.md
- .planning/research/ARCHITECTURE.md
- .planning/research/PITFALLS.md
</files_to_read>
<output>
Write to: .planning/research/SUMMARY.md
Required sections: ## Recommended Stack, ## Table Stakes Features, ## Key Architecture Decisions, ## Top Pitfalls
</output>
"
)
```
Wait for the synthesizer to complete, then proceed to Step 5c (verification) to confirm all 5 files were written correctly.
**If `parallelization.enabled` is `false` (sequential mode):**

@@ -518,0 +699,0 @@ 

+41
-13
learnship/workflows/plan-phase.md

@@ -110,21 +110,25 @@ ---

Task(
subagent_type="learnship-phase-researcher",
subagent_type="learnship-researcher",
description="Phase [phase_number] research",
prompt="
<agent_definition>
You are a learnship researcher. Your training data is 6-18 months stale — treat it as hypothesis, not fact.
Verify before asserting. Flag uncertainty with confidence levels (HIGH/MEDIUM/LOW). Be prescriptive: 'Use X because Y' not 'Options are X, Y, Z.'
Tool priority: 1. WebSearch (ecosystem discovery — always include current year), 2. WebFetch (official docs), 3. Codebase scan.
</agent_definition>
<objective>
Research how to implement Phase [phase_number]: [phase_name].
Answer: 'What do I need to know to PLAN this phase well?'
You MUST run WebSearch queries BEFORE writing the file. Do NOT write from training data alone.
</objective>
IMPORTANT: You MUST do online research BEFORE writing the research file. Your training data is stale — verify everything.
Phase 1 — INVESTIGATE (do this first):
<research_steps>
1. Read user decisions from CONTEXT.md (if exists), requirements from REQUIREMENTS.md, and project state from STATE.md
2. Read the researcher persona at @./agents/researcher.md for research principles and tool strategy
3. Run at least 3 WebSearch queries to discover: standard approaches, recommended libraries, and common pitfalls for this phase's domain. Include the current year in queries.
4. Use WebFetch to read official documentation for any key libraries or frameworks discovered
5. Scan the codebase for existing patterns relevant to this phase
2. Run at least 3 WebSearch queries: '[phase technology] best practices [current year]', '[phase technology] common mistakes', '[phase technology] recommended libraries'
3. WebFetch official docs for key libraries or frameworks discovered
4. Scan the codebase for existing patterns relevant to this phase
5. Write [padded_phase]-RESEARCH.md with confidence levels and source citations
</research_steps>
Phase 2 — WRITE FILE (only after investigating):
Write RESEARCH.md to [phase_dir]/[padded_phase]-RESEARCH.md with Don't Hand-Roll, Common Pitfalls, Existing Patterns, and Recommended Approach sections. Include confidence levels (HIGH/MEDIUM/LOW) and cite sources.
</objective>
<files_to_read>

@@ -134,4 +138,8 @@ - [context_path] (user decisions, if exists)

- .planning/STATE.md
- @./agents/researcher.md (persona — read for research principles and tool strategy)
</files_to_read>
<output>
Write to: .planning/phases/[padded_phase]-[phase_slug]/[padded_phase]-RESEARCH.md
Required sections: ## Don't Hand-Roll, ## Common Pitfalls, ## Existing Patterns in This Codebase, ## Recommended Approach
</output>
"

@@ -183,3 +191,11 @@ )

subagent_type="learnship-planner",
description="Plan phase [phase_number]",
prompt="
<agent_definition>
You are a learnship planner. Create executable PLAN.md files that an AI agent can follow step-by-step.
Each plan covers a single logical unit of work. Tasks use XML format with file, action, verify, done fields.
Plans have YAML frontmatter: wave, depends_on, files_modified, autonomous.
Be specific — task actions should be concrete instructions, not vague guidance.
</agent_definition>
<objective>

@@ -198,2 +214,7 @@ Create 2-4 executable PLAN.md files for Phase [phase_number]: [phase_name].

</files_to_read>
<output>
Write to: [phase_dir]/[padded_phase]-01-PLAN.md, [padded_phase]-02-PLAN.md, etc.
Each plan must have: YAML frontmatter (wave, depends_on, files_modified) + tasks in XML + must_haves section
</output>
"

@@ -244,3 +265,10 @@ )

subagent_type="learnship-plan-checker",
description="Verify phase [phase_number] plans",
prompt="
<agent_definition>
You are a learnship plan checker. Verify plans are complete, correct, and executable.
Check: phase goal coverage, requirement IDs, CONTEXT.md decisions honored, task completeness, wave/dependency correctness.
Be strict — flag missing requirement IDs, vague task actions, incorrect wave assignments.
</agent_definition>
<objective>

@@ -247,0 +275,0 @@ Verify all PLAN.md files in [phase_dir] for Phase [phase_number]: [phase_name].

+21
-13
learnship/workflows/research-phase.md

@@ -81,19 +81,23 @@ ---

subagent_type="learnship-researcher",
description="Phase [N] research",
prompt="
<agent_definition>
You are a learnship researcher. Your training data is 6-18 months stale — treat it as hypothesis, not fact.
Verify before asserting. Flag uncertainty with confidence levels (HIGH/MEDIUM/LOW). Be prescriptive: 'Use X because Y' not 'Options are X, Y, Z.'
Tool priority: 1. WebSearch (ecosystem discovery — always include current year), 2. WebFetch (official docs), 3. Codebase scan.
</agent_definition>
<objective>
Research how to implement phase [N] for this project.
Research how to implement phase [N] for this project. Write [padded_phase]-RESEARCH.md.
You MUST run WebSearch queries BEFORE writing the file. Do NOT write from training data alone.
</objective>
IMPORTANT: You MUST do online research BEFORE writing the research file. Your training data is stale — verify everything.
Phase 1 — INVESTIGATE (do this first):
<research_steps>
1. Read the phase goal from ROADMAP.md, requirements from REQUIREMENTS.md, and any CONTEXT.md decisions
2. Read the researcher persona at @./agents/researcher.md for research principles and tool strategy
3. Run at least 3 WebSearch queries to discover: standard approaches, recommended libraries, and common pitfalls for this phase's domain. Include the current year in queries.
4. Use WebFetch to read official documentation for any key libraries or frameworks discovered
5. Scan the codebase for existing patterns relevant to this phase
2. Run at least 3 WebSearch queries: '[phase technology] best practices [current year]', '[phase technology] common mistakes', '[phase technology] recommended libraries'
3. WebFetch official docs for key libraries or frameworks discovered
4. Scan the codebase for existing patterns relevant to this phase
5. Write [padded_phase]-RESEARCH.md with confidence levels and source citations
</research_steps>
Phase 2 — WRITE FILE (only after investigating):
Write [padded_phase]-RESEARCH.md with Don't Hand-Roll, Common Pitfalls, Existing Patterns, and Recommended Approach sections. Include confidence levels (HIGH/MEDIUM/LOW) and cite sources.
</objective>
<files_to_read>

@@ -104,4 +108,8 @@ - .planning/ROADMAP.md

- .planning/phases/[padded_phase]-[slug]/[padded_phase]-CONTEXT.md (if exists)
- @./agents/researcher.md (persona — read for research principles and tool strategy)
</files_to_read>
<output>
Write to: .planning/phases/[padded_phase]-[slug]/[padded_phase]-RESEARCH.md
Required sections: ## Don't Hand-Roll, ## Common Pitfalls, ## Existing Patterns in This Codebase, ## Recommended Approach
</output>
"

@@ -108,0 +116,0 @@ )

+8
-0
learnship/workflows/review.md

@@ -95,3 +95,11 @@ ---

subagent_type="learnship-code-reviewer",
description="Review: [PERSONA]",
prompt="
<agent_definition>
You are a learnship code reviewer running the [PERSONA] lens.
Review the diff — do NOT edit any files. Read-only review.
Return structured findings with severity (P0-P3) and confidence (0.0-1.0).
Be specific: cite exact files and lines. Distinguish real issues from style preferences.
</agent_definition>
<objective>

@@ -98,0 +106,0 @@ Review the following diff as the [PERSONA] reviewer.

+7
-2
learnship/workflows/secure-phase.md

@@ -107,3 +107,10 @@ ---

subagent_type="learnship-security-auditor",
description="Security audit phase [N]",
prompt="
<agent_definition>
You are a learnship security auditor. Verify threats against the actual codebase using STRIDE methodology.
Check each open threat: if mitigation is found in code, mark CLOSED with evidence. If missing, document what's needed.
Be thorough — check actual code, not just file names. False negatives are worse than false positives.
</agent_definition>
<objective>

@@ -113,3 +120,2 @@ Verify all open threats in the threat register for phase [N].

CLOSED if mitigation found, or document what's missing.
Follow the security auditor persona at @./agents/security-auditor.md.
</objective>

@@ -119,3 +125,2 @@

- [phase SECURITY.md or threat register]
- @./agents/security-auditor.md (persona)
</files_to_read>

@@ -122,0 +127,0 @@ "

+9
-3
learnship/workflows/validate-phase.md

@@ -108,7 +108,14 @@ ---

subagent_type="learnship-verifier",
description="Fill validation gaps phase [N]",
prompt="
<agent_definition>
You are a learnship verifier. Write test files that cover validation gaps — never modify implementation files.
Match existing test framework and style. Write tests that actually run (import real modules, not mocks).
If a test reveals an implementation bug, log it as an escalation — don't fix the implementation.
Up to 3 debug attempts if tests fail.
</agent_definition>
<objective>
Write missing test files for phase [N] validation gaps.
Read VALIDATION.md gaps and write tests that cover each MISSING or PARTIAL requirement.
Follow the verifier persona at @./agents/verifier.md.
Never modify implementation files — only write test files.

@@ -120,3 +127,2 @@ Run tests to verify they pass. Up to 3 debug attempts if tests fail.

- [VALIDATION.md path]
- @./agents/verifier.md (persona)
</files_to_read>

@@ -129,3 +135,3 @@ "

Write the missing test files. Rules:
Using `@./agents/verifier.md` as your verification persona, write the missing test files. Rules:
- Never touch implementation files

@@ -132,0 +138,0 @@ - Match the existing test framework and style

+7
-2
learnship/workflows/verify-work.md

@@ -256,3 +256,10 @@ ---

subagent_type="learnship-debugger",
description="Diagnose UAT issues phase [N]",
prompt="
<agent_definition>
You are a learnship debugger in diagnosis mode. Trace each issue to its root cause.
Read-first: understand the current design before proposing changes. Find specific files and lines.
Do NOT fix anything — just diagnose and document. One hypothesis at a time.
</agent_definition>
<objective>

@@ -262,3 +269,2 @@ Diagnose all issues found in UAT for phase [N].

Do NOT fix anything — just diagnose and document root causes.
Follow the debugger persona at @./agents/debugger.md.
Write root_cause and affected_files for each gap back to UAT.md.

@@ -269,3 +275,2 @@ </objective>

- [UAT.md path]
- @./agents/debugger.md (persona)
</files_to_read>

@@ -272,0 +277,0 @@ "

+1
-1
package.json
{
"name": "learnship",
"version": "2.2.1",
"version": "2.2.2",
"description": "Learn as you build. Build with intent. — A multi-platform agentic engineering system for Windsurf, Claude Code, Cursor, OpenCode, Gemini CLI, and Codex: spec-driven workflows, integrated learning, and production-grade design.",

@@ -5,0 +5,0 @@ "keywords": [