harness-creator
Build, audit, and improve lightweight harnesses for AI coding agents: AGENTS.md/CLAUDE.md, feature state, verification workflows, scope boundaries, lifecycle handoff, memory persistence, context control, tool safety, and multi-agent coordination.
What this skill does
# Harness Creator Use this skill to make a repository easier for coding agents to start, stay in scope, verify work, and resume across sessions. Keep the harness small enough that agents actually follow it. Not for model selection, prompt tuning in isolation, chat UI design, or general app architecture. ## Core Model Every useful coding-agent harness has five subsystems: | Subsystem | Minimal artifact | Purpose | |---|---|---| | Instructions | `AGENTS.md` or `CLAUDE.md` | Startup path, working rules, definition of done | | State | `feature_list.json`, `progress.md` | Current feature, status, evidence, next step | | Verification | `init.sh` or documented commands | Tests/checks the agent must run before claiming done | | Scope | Feature dependencies and done criteria | Prevents overreach and half-finished work | | Lifecycle | `session-handoff.md`, end-of-session routine | Makes the next session restartable | ## First Move 1. Inspect what already exists: instruction files, feature/state files, verification commands, docs, package manifests. 2. Ask only for missing context that cannot be inferred safely: target agent, desired file name, tolerance for structure, and whether overwriting is allowed. 3. Prefer a minimal harness first. Add memory, tool safety, multi-agent, or benchmark details only when the user's problem calls for them. ## Common Tasks ### Create a harness Use the bundled script when working on a local repository: ```bash node skills/harness-creator/scripts/create-harness.mjs --target /path/to/project ``` Options: - `--agent-file CLAUDE.md` for Claude-oriented projects. - `--package-manager npm|pnpm|yarn|bun` when detection is wrong. - `--commands "cmd one,cmd two"` for custom verification. - `--force` only after confirming overwrites are acceptable. Then explain what was created and how the user should replace placeholder feature entries. ### Audit an existing harness Run: ```bash node skills/harness-creator/scripts/validate-harness.mjs --target /path/to/project ``` Report the five subsystem scores, the lowest-scoring area, and the first 2-3 changes that would improve reliability. Treat the lowest score as a candidate bottleneck; confirm with failures, logs, or task outcomes before claiming causality. ### Produce a report Use when the user wants a shareable assessment: ```bash node skills/harness-creator/scripts/render-assessment-html.mjs --target /path/to/project node skills/harness-creator/scripts/run-benchmark.mjs --target /path/to/project --html /path/to/report.html ``` Be clear that this is a structural benchmark. Real effectiveness still needs before/after agent sessions on representative tasks. ## When to Read References Load only the reference needed for the user's problem: - Memory across sessions: [Memory Persistence](references/memory-persistence-pattern.md) - Reusable workflows as skills: [Skill Runtime](references/skill-runtime-pattern.md) - Permissions, tools, concurrency: [Tool Registry & Safety](references/tool-registry-pattern.md) - Context budget and progressive disclosure: [Context Engineering](references/context-engineering-pattern.md) - Delegation and parallel agents: [Multi-Agent Coordination](references/multi-agent-pattern.md) - Hooks, startup, long-running work: [Lifecycle & Bootstrap](references/lifecycle-bootstrap-pattern.md) - Non-obvious failure modes: [Gotchas](references/gotchas.md) ## Design Rules - Keep the root instruction file short: routing and invariants, not a full manual. - Put project facts in project docs, not in the skill. - Make verification commands explicit and runnable. - Require evidence before marking a feature done. - Use one active feature unless the harness has explicit multi-agent ownership boundaries. - Prefer append/update state files over relying on chat history. - Never hide destructive behavior in scripts; overwrites require explicit user approval. ## Deliverable Checklist For a usable minimal harness, leave the target project with: - [ ] `AGENTS.md` or `CLAUDE.md` - [ ] `feature_list.json` - [ ] `progress.md` - [ ] `init.sh` - [ ] Optional `session-handoff.md` for multi-session work - [ ] Documented verification evidence or next action If you cannot create files, provide exact file contents and commands instead.
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.