bootstrap-project-context
Bootstrap a new AI-agent session by reading the repository operating docs and rebuilding project understanding from source. Use when Codex needs to start a new conversation, get up to speed on an unfamiliar repo, read AGENTS.md and README.md completely before acting, investigate the codebase to understand the project's purpose and architecture, or turn a rough onboarding prompt into an execution-ready repo-orientation prompt.
What this skill does
# Bootstrap Project Context Start a session by absorbing the repo's explicit instructions first, then confirm the real system shape from source. Load [references/prompt-template.md](references/prompt-template.md) when the user wants a reusable bootstrap prompt or when you need a strong starting template before beginning repo discovery. ## Modes Choose the lightest mode that fits the request. - `Prompt-only`: refine the user's rough bootstrap prompt and return the upgraded prompt. - `Repo bootstrap`: read the operating docs, investigate the repo, and deliver an onboarding summary. - `Prompt + bootstrap`: return the upgraded prompt and use the same standards while building context. ## Workflow ### 1. Read the explicit repo contract first Read these files completely when they exist: - `AGENTS.md` - `README.md` Treat them as mandatory orientation, not optional background. If the repo exposes a read-only status or onboarding scout, you may use it as a supplement after the mandatory docs pass, never as a substitute for reading the docs. ### 2. Build a source-first map of the repository Understand the codebase from the implementation, not from naming alone. Inspect the most informative source artifacts first, such as: - package manifests, build files, task runners, and lockfiles - top-level app or service directories - primary entrypoints and framework bootstraps - configuration files, environment examples, and schemas - tests that reveal supported behavior - architecture docs and design notes when present Aim to identify: - what the project is for - who or what uses it - the major subsystems and how they relate - the main execution paths, data flows, and external integrations - the development and verification commands that matter ### 3. Trace the technical architecture with enough depth Go broad before going deep. At minimum, determine: - the primary language, framework, and runtime model - the main module boundaries - where requests, jobs, or user actions enter the system - where state lives - how the project is configured, built, and tested Read representative files from each important area. Do not pretend to understand the architecture from one or two files, but do not exhaustively read the whole repo when a targeted map is enough. ### 4. Return a practical onboarding synthesis Summarize the repo in a way that helps the next turn start strong. Include: - project purpose - architecture summary - major components and responsibilities - important commands and workflows - notable conventions or operating constraints from `AGENTS.md` - open questions or areas that still need deeper inspection - the best next files or directories to read for the user's likely goal ### 5. Verify the orientation pass Before finishing, check that your summary is grounded in files you actually inspected. Make sure you did not: - skip `AGENTS.md` or `README.md` - confuse docs intent with real implementation behavior - describe architecture that you did not verify from source - miss an obvious top-level subsystem, runtime, or integration ## Prompt Upgrade Rules When the user gives a rough bootstrap prompt, keep the intent but add the missing execution structure: - require a complete read of `AGENTS.md` and `README.md` before code investigation - require source-first codebase investigation after the docs pass - require identifying project purpose, architecture, components, workflows, and commands - require a concise onboarding synthesis instead of vague claims of understanding - require explicit mention of remaining unknowns and recommended next reads when appropriate Prefer the template in [references/prompt-template.md](references/prompt-template.md) over improvising from scratch. ## Red Flags Stop and correct the approach if any of these appear: - skimming `AGENTS.md` or `README.md` - jumping into code without first reading the repo instructions - inferring architecture from directory names alone - giving a hand-wavy summary with no file-grounded evidence - over-reading low-value files instead of building a representative system map ## Done Criteria This skill is complete when the upgraded prompt or repo-orientation pass: - begins with a full read of `AGENTS.md` and `README.md` when present - explains the project's purpose and technical architecture from inspected source - identifies the main components, workflows, and important commands - captures repo-specific conventions and open questions clearly enough for the next turn to start productively
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.