backlog-md
Task tracking system for agents via Backlog.md CLI. Use when creating deferred issues during implementation, filing audit findings, working assigned tasks, or managing project work. Optimized for agent workflows: structured issue filing, priority/labeling system, and task completion tracking.
What this skill does
# Backlog.md Task Tracking for Agents Task tracking system optimized for agent workflows via Backlog.md CLI. ## Three Primary Use Cases ### 1. Implementer: Deferring Issues During Feature Work File P2-P4 issues discovered during implementation. Use label `remediation` for deferred review findings. - **Create with plan**: You have implementation context at filing time - **Assign priority**: See priority guide - **Add type label** (required) + **app labels** (optional) ### 2. Reviewer: Audit Findings Create structured issues from security/audit reviews. Link dependencies and assign to milestones. - **Create with plan**: You're creating from audit context - **Link dependencies** as needed - **Assign priority**: Ask user if uncertain (label `priority-review`) - **Set milestones** ### 3. Worker: Executing Assigned Tasks Read task fully, understand all fields, follow acceptance criteria and definition of done. - **Claim task**: `backlog task edit 42 -s "In Progress" -a @myself` - **Read everything**: All fields, attached files/URLs, linked documentation - **Complete systematically**: AC → Implementation Notes → Final Summary → DoD → Done ## Absolute Rules 1. **CLI only for writes**. `backlog task edit` and `backlog task create` only. Never edit files directly. 2. **Always use `--plain` flag** when reading: `backlog task 42 --plain`, `backlog task list --plain` 3. **Type label required** (bug|feature|documentation|refactor|remediation), single value only 4. **App labels optional**, can be multiple: synapse-pingora, signal-horizon-ui, signal-horizon-api, etc. 5. **Custom labels allowed** as agent-useful (priority-review, performance, security, etc.) ## Implementation Plans: When Required | Scenario | Include Plan? | Reason | |----------|---------------|--------| | Deferred review issues | ✅ YES | You have audit/review context now | | Reporting found issues | ❌ NO | Implementer will plan when they work it | | Explicitly asked to plan work | ✅ YES (detailed) | Required per instruction | | Regular task work | After claiming, before coding | Don't add at creation, add after starting | **Never update an existing plan unless explicitly instructed.** ## Priority System (See references/priority-labels.md for details) - **P0**: Critical problems, major breakage - **P1**: Legitimate bugs impacting users - **P2**: Bugs, edge cases - **P3**: Nice-to-have improvements, features we want - **P4**: Backlog, future ideas Unsure about P0-P1? Label with `priority-review` and let user decide. ## Task Completion Checklist 1. Status: "In Progress" + assign self 2. Read: All fields, attached documentation 3. Plan: Add implementation plan (if not deferred from review) 4. Work: Code implementation, mark AC as you complete each 5. Notes: Append progress notes as you go 6. Summary: Add final summary (PR-style) 7. DoD: Check all definition-of-done items 8. Done: Set status "Done" ## Essential Commands ```bash # Create issue (required: title, type label, priority) backlog task create "Title" -d "Description" -l bug -p 2 --ac "AC 1" # Work a task backlog task edit 42 -s "In Progress" -a @myself backlog task 42 --plain # Read everything backlog task edit 42 --check-ac 1 # Mark AC complete backlog task edit 42 --append-notes "Progress here" backlog task edit 42 --final-summary "PR description" backlog task edit 42 -s Done # Search and filter backlog task list -s "To Do" --plain backlog search "topic" --plain ``` See `references/cli-reference.md` for complete command reference. See `references/priority-labels.md` for priority and labeling guidelines. See `references/issue-creation-guide.md` for detailed issue creation patterns.
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.