TenureOS Investment Platform

Skills Guidebook

Reusable agent skills for planning, building, reviewing, and teaching

Skills are reusable capability packs. Installed ones load automatically when a matching task comes up; recommended ones can be added with npx skills add. Each entry explains what the skill does and when to reach for it.

Download this guidebook as PDF ↓

01

Then vs Now

From ad-hoc prompts to reusable skills

Then

  • Virtually no reusable agent skills.
  • Every task started from a blank prompt.
  • Tooling knowledge lived in scattered notes and memory.
  • Even the REPI Tools Guide book had to be built by hand each time.

Now

  • Matt Pocock’s engineering skills are installed and ready.
  • Each skill carries a trigger, a workflow, and a stop condition.
  • Work starts with the right skill instead of a blank prompt.
  • The guidebook itself is kept in sync by the same skills.
02

Skills

Then virtually none, now many skills

Skills then, virtually none — now many skills

The environment now ships with a library of skills for planning, building, reviewing, teaching, and grilling designs. Reach for them by name or by intent.

PrototypeWayfinderTeach-meGrill-meTDDCode ReviewDiagnosing BugsQAResearchHandoffPlanand many others
03

Matt Pocock’s Skills

Main engineering skills installed in this environment

setup-matt-pocock-skillsCore

Scaffolds the repo conventions the other engineering skills expect: issue tracker, triage label vocabulary, and domain-doc layout. Run once per repo.

planCore

Writes an implementation plan as a markdown file under .hermes/plans/. No execution — just a clear map before work starts.

wayfinderCore

Plans work too large for one agent session. Creates a shared map of decision tickets on the issue tracker and resolves them one at a time until the path is clear.

prototypeCore

Builds a throwaway prototype to answer a design question or explore what a UI should look like before committing to real code.

tddCore

Enforces red-green-refactor: tests before code. Use for features or bug fixes where test-first discipline matters.

code-reviewCore

Reviews changes since a commit or branch against repo standards and the originating spec, running both reviews in parallel.

implementCore

Implements a spec or set of tickets end-to-end. The workhorse skill for turning a plan into working code.

diagnosing-bugsCore

Structured diagnosis loop for hard bugs and performance regressions. Use when the cause is not obvious.

qaCore

Interactive QA session: report bugs conversationally and the agent explores the codebase, then files well-formed issues.

researchCore

Investigates a question against high-trust primary sources and captures the findings as a Markdown file in the repo.

grilling / grill-meCore

Relentlessly interviews you to stress-test a plan, decision, or design before you build on it.

teachCore

Teaches a new skill or concept within this workspace, with examples tied to your actual code.

handoffCore

Compacts the current conversation into a handoff document another agent can pick up and continue.

to-spec, to-tickets, to-questionnaireCore

Turn conversation into a spec, a set of tracer-bullet tickets, or a questionnaire for someone else to fill in.

domain-modeling & ubiquitous-languageCore

Builds a domain model and canonical glossary, flagging ambiguities and proposing terms the whole team can use.

codebase-design & design-an-interfaceCore

Shared vocabulary for deep modules and parallel exploration of interface shapes before choosing one.

request-refactor-planCore

Creates a detailed, tiny-commit refactor plan via interview, then files it as an issue. Use before large restructuring.

04

/teach-me

How to implement and use a skill
  1. Load it. Say the skill name in chat, e.g. /teach TDD, or call skill_view(name='tdd') to read its full instructions.
  2. Match the trigger.Each skill opens with “Use when...” — wait for that situation, then invoke the skill by name.
  3. Follow the workflow. Skills are deterministic recipes. Let the skill ask its setup questions, confirm choices, then execute.
  4. Save learnings. If a skill taught you a stable workflow, ask the agent to save it as a new skill or patch the existing one.

Start small: pick one skill per week, use it for real work, and add the next only when the first feels automatic.