accelint-qrspi-apply
Implement QRSPI-planned OpenSpec changes with intelligent parallelization. Use when the user wants to apply a QRSPI change, implement tasks with parallelization, or says "apply this QRSPI change", "implement with parallelization", "run the parallel slices". This skill is specifically designed for changes created via accelint-qrspi that include "Parallelization Strategy" sections in tasks.md. It orchestrates parallel sub-agent execution for independent task slices using OpenSpec CLI workflows. Make sure to use this skill when the user mentions applying QRSPI changes, running parallel implementation, or working on changes with vertical slices.
What this skill does
# Accelint QRSPI Apply
Implement OpenSpec changes with intelligent parallelization. This skill orchestrates parallel sub-agent execution based on dependency analysis in the OpenSpec task file, validates implementation, and manages the complete apply workflow.
## What This Skill Does
**Automates**: The implementation phase of spec-driven development with parallel execution
**Scope**: Task implementation → Validation → Archive readiness
**Output**: Fully implemented change ready for archival
**Does NOT**: Create plans, modify specs, or automatically archive (suggests archival when ready)
## Prerequisites
- OpenSpec CLI installed and initialized
- OpenSpec change created via `accelint-qrspi` skill (includes "Parallelization Strategy" in tasks.md)
- Sub-agent support (for parallel execution)
- The expanded OpenSpec workflows (`explore`, `new`, `continue`) enabled
**Important**: This skill is specifically designed for QRSPI-planned changes. Standard OpenSpec changes without parallelization strategies should use the regular `/opsx:apply` command directly.
## Workflow Overview
```
┌─────────────────────────────────────────────────────────────────┐
│ Phase Action Output │
├─────────────────────────────────────────────────────────────────┤
│ Parse Extract parallelization Dependency graph │
│ Dependencies Identify blocking tasks Execution plan │
│ Execute Run slices (parallel/serial) Implemented code │
│ Verify Run opsx:verify Verification rpt │
│ Report Show results + next steps Archive or fix │
└─────────────────────────────────────────────────────────────────┘
```
## Phase Breakdown
### Phase 0: Preflight and Change Selection
**Steps**:
1. If a change name is provided in the skill arguments, use it
2. Otherwise, try to infer from conversation context (recent mentions of change names)
3. If ambiguous or missing:
```bash
openspec list --json
```
Parse the JSON and use **AskUserQuestion** to let the user select the change
4. Announce: "Applying change: `<name>`" and how to override (e.g., re-invoke with different name)
5. Check that tasks.md exists:
```bash
openspec status --change "<name>" --json
```
If `state: "blocked"` (missing tasks), exit with: "Tasks artifact is missing. Run `/opsx:continue` to generate tasks before applying."
### Phase 1: Parse Tasks and Parallelization Strategy
**Goal**: Extract task structure and identify parallel vs sequential execution opportunities. Detect if work has already started and resume from the correct level.
**Steps**:
1. Read the tasks.md file from `openspec/changes/<change-name>/tasks.md`
2. **Validate checklist format** (CRITICAL for progress tracking):
- Check that tasks use markdown checklist format: `- [ ] task` or `- [x] task`
- If tasks use numbered lists (1. 2. 3.) or plain bullets (- without [ ]):
```
❌ Invalid tasks.md format
This skill requires tasks in markdown checklist format (`- [ ] task`) for
progress tracking and resumption detection.
Found format: [numbered lists / plain bullets / other]
Please regenerate tasks.md using the accelint-qrspi-propose skill or convert
manually to checklist format before applying.
```
- Exit if format is invalid — do not proceed with invalid task format
3. **Check for partial completion** (resumption detection):
- Count completed tasks (marked `- [x]`) vs total tasks
- Parse which slices have all their tasks marked complete
- If any slices are complete, announce: "Detected partial completion. Resuming from Slice N."
- Adjust the execution plan to skip completed slices
4. Look for the "Parallelization Strategy" section (usually at the end of the file)
5. Parse the strategy to build a dependency graph:
**Example strategy:**
```md
## Parallelization Strategy
- **Slice 1** must complete first (establishes infrastructure)
- **Slice 2** and **Slice 3** can run in parallel after Slice 1
- **Slice 2** (implementation cleanup) is independent of **Slice 3** (docs/verification)
- Final integration: merge both slices, run full pre-commit checklist
```
**Parsed dependency graph:**
```
Level 0 (must run first):
- Slice 1
Level 1 (can run in parallel after Level 0):
- Slice 2
- Slice 3
Level 2 (after all previous):
- Final integration
```
6. If no "Parallelization Strategy" section exists:
- Assume all tasks must run sequentially (safe default)
- Inform user: "No parallelization strategy found. Running tasks sequentially."
7. Build an execution plan showing:
- Which slices run in which order
- Which slices can run in parallel (and which are already complete)
- Total estimated parallelization speedup
- Starting point (Level 0 or resuming from Level N)
**Output**: Dependency graph, execution plan, and resumption point if applicable
### Phase 2: Execute Tasks (Sequential + Parallel)
**Goal**: Implement tasks following the dependency graph, spawning parallel sub-agents where possible.
**Sequential execution** (when tasks have dependencies):
For each level in the dependency graph (starting from level 0):
1. If the level has only one slice:
- Spawn a single sub-agent with this prompt:
```
/opsx:apply <change-name>
CRITICAL: You MUST use the /opsx:apply command to implement tasks.
DO NOT implement tasks directly yourself. The /opsx:apply workflow will
load context and guide implementation.
IMPORTANT: This is Slice N of a parallelized QRSPI implementation.
Context: This slice must complete before other slices can proceed.
Other slices will start after you finish.
Instructions:
- Work ONLY on tasks in Slice N: [list slice N tasks/sections]
- Do NOT implement tasks from other slices (Slices X, Y, Z will be handled separately)
- Follow the normal OpenSpec apply workflow:
* OpenSpec will load context files (proposal, design, specs, tasks)
* Implement the tasks assigned to Slice N
* Mark tasks complete as you go: `- [ ]` → `- [x]`
* Test your changes if tests are specified in the tasks
- Report completion with summary of changes made
Focus exclusively on Slice N. Leave other slice tasks unchecked.
```
2. Wait for completion before proceeding to the next level
**Parallel execution** (when multiple slices are independent):
For each level with multiple independent slices:
1. Spawn all sub-agents in parallel in a single turn (one per slice):
```
/opsx:apply <change-name>
CRITICAL: You MUST use the /opsx:apply command to implement tasks.
DO NOT implement tasks directly yourself. The /opsx:apply workflow will
load context and guide implementation.
IMPORTANT: This is Slice N of a parallelized QRSPI implementation.
Context: This slice is independent and runs in parallel with Slices X, Y.
Other agents are working on those slices simultaneously.
Instructions:
- Work ONLY on tasks in Slice N: [list slice N tasks/sections]
- Do NOT implement tasks from other slices - they are being handled in parallel
- Follow the normal OpenSpec apply workflow:
* OpenSpec will load context files (proposal, design, specs, tasks)
* Implement the tasks assigned to Slice N
* Mark tasks complete as you go: `- [ ]` → `- [x]`
* Test your changes if tests are specified in the tasks
- Report completion with summary of changes made
Focus exclusively on Slice N. Leave other slice tasks unchecked.
Your work is independent and should not block or depend on other slices.
```
2. Track completion as each sub-agent finishes
3. When all slices in the level are done, **pause and offer context management**:
```
✅ Level N complete
Completed slices:
- Slice X: [summary]
- Slice Y: [summary]
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.