aops-core/skills/aops/SKILL.md
Core academicOps skill — institutional memory, strategic coordination, workflow routing, and framework governance. Merges butler (chief-of-staff) with framework development conventions.
npx skillsauth add nicsuzor/academicops aopsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Coordinate framework operations, maintain institutional memory in the PKB, route workflows, and enforce framework conventions.
The Personal Knowledge Base (PKB) is the single source of truth.
mcp__plugin_aops-core_pkb__get_document(id="aops-state").mcp__plugin_aops-core_pkb__append for incremental updates.list_tasks with project=<project-id> to find tasks scoped to a single project. Do not infer project membership from IDs.If corrected, encountering a process gap, or seeing a learning-tagged task:
CORE.md or SKILL.md).When failures occur, identify and fix the process gap rather than just applying a local patch. Do not declare victory until you verify success with evidence.
supervisor skill (mandatory)When you are delegating work to other agents and verifying it gets done — including running as
the main conversation orchestrator with background workers — invoke the supervisor skill;
never hand-roll supervision inline. The mandate, the "conversational orchestrator is not an
exemption" carve-out, and the full proof discipline are canonical at
[[../supervisor/SKILL.md#when-to-invoke-mandatory]].
/planner decompose first.Verify capability and constraints of framework mechanisms (plugins, model context, environment) against primary codebase files before making claims or routing tasks.
Route tasks based on scope:
| Target | Workflow | | :------------------------------------- | :------------------------------------------------------------------------- | | Add hook, skill, command, or agent | 01-design-new-component | | Fix broken framework issues | 02-debug-framework-issue | | Test optimizations / experiments | 03-experiment-design | | Check / trim bloat | 04-monitor-prevent-bloat | | Build new features | 05-feature-development | | Write/update specifications | 06-develop-specification | | Record lessons / learnings | 07-learning-log | | Unstick blocked decisions | 08-decision-briefing | | Diagnose hook/gate failures | 09-session-hook-forensics | | Process-level review (dogfooding) | 10-reflective-execution | | Verify session infrastructure | 11-self-test | | Verify hook routing | 11-self-test §3 |
$AOPS/*: Modification permitted.$ACA_DATA/*: Direct file operations forbidden; delegate writes/updates to PKB MCP tools.$ACA_DATA..bak, _new). Edit files directly, commit, and push.Follow the Plan -> Act -> Validate cycle:
rbg or pauli) only if uncertainty or blast radius requires it.When a coordinator executes a fire-and-forget dispatch with no follow-up or babysit instruction:
/dump or /end_session) rather than idling.When unable to derive a decision:
AskUserQuestion.Do not ask permission to file tasks, fix bugs, or execute safe operations (e.g., retitling, graph hygiene, canceling superseded tasks). Perform the action and report the outcome. Only ask permission for destructive or externally visible actions.
Perform safe administrative actions (e.g. repointing dependencies, status flips, hygiene) immediately in the same turn instead of documenting them as future TODOs.
Report actions and states in plain English. Avoid using internal taxonomy (DECIDE-class, Externalisation Heuristic, P#) in user-facing output.
tools
Streamlit implementation of the analyst presentation layer. Use when building or updating a Streamlit dashboard that displays pre-computed research data. This is the Streamlit-specific HOW for the tech-agnostic principles in the aops-tools analyst skill — display only, never transform.
tools
Python plotting and statistical-modelling libraries (matplotlib, seaborn, statsmodels) for the analyst presentation and statistical-methodology layers. Use when producing publication-quality figures or fitting statistical models in Python. Library-specific HOW for the tech-agnostic principles in the aops-tools analyst skill.
tools
dbt (data build tool) implementation of the analyst transformation layer. Use when a project has a dbt/ directory or you need to build, test, or document SQL transformations as version-controlled, reproducible dbt models. This is the dbt-specific HOW for the tech-agnostic principles in the aops-tools analyst skill.
development
Support academic research data analysis with technology-agnostic principles — research-data immutability, a versioned/tested/reproducible transformation layer, statistical methodology, and self-documenting research. Use this skill for any computational research project with an empirical data pipeline. The skill enforces academicOps best practices for reproducible, transparent research with a collaborative single-step workflow. Tech-specific how-to (dbt, Streamlit, Python plotting/stats) lives in the aops-extras package.