refactor: restructure skills directory and add thinking-and-docs skills

common/README.md

@@ -1,23 +0,0 @@
-# Common Skills
-
-Skills that work in all CLI agents.
-
-## User-invoked
-
-- [agent-handoff](in-progress/agent-handoff/SKILL.md) — Hand the current conversation off to a fresh background agent that picks up the work immediately.
-- [commit-staged](engineering/commit-staged/SKILL.md) — Commit staged files with a conventional commit message.
-- [conversation-summary](pkm/conversation-summary/SKILL.md) — Save the current conversation as a comprehensive report note in your Obsidian vault, following OKF v0.1 conventions.
-- [crit](pkm/crit/SKILL.md) — Run the CRIT framework — give the AI Context, assign it a Role, let it Interview you one question at a time, then issue the Task.
-- [implement-isolation](engineering/implement-isolation/SKILL.md) — Implement a piece of work based on a spec or set of tickets in isolation.
-- [implement-isolation-tmux](engineering/implement-isolation-tmux/SKILL.md) — Dispatch a child agent in an isolated git worktree to implement a piece of work based on a PRD or set of issues.
-- [knowledge-gardener](in-progress/knowledge-gardener/SKILL.md) — Run vault-aware semantic search, synthesis, note creation, linking, and Zettelkasten workflows for this Obsidian vault.
-- [project-context-pack](engineering/project-context-pack/SKILL.md) — Build a bounded repo context pack (project map, codebase index, cached memory file) so later work uses fd/rg/tree-sitter/LSP instead of repeated browsing.
-- [research-vault](pkm/research-vault/SKILL.md) — Research a topic through a one-question-at-a-time learning conversation, answer directly, share resources when useful, and save a linked OKF-conformant research packet in the Obsidian vault.
-- [setup-skills](engineering/setup-skills/SKILL.md) — Configure this repo for the engineering skills, set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.
-- [tmux-launch-agent](misc/tmux-launch-agent/SKILL.md) — Fork a new agent CLI session into a new tmux window, detected from the current agent.
-- [youtube-video-capture](pkm/youtube-video-capture/SKILL.md) — Fetch subtitles from a YouTube video, summarize the content, and save both the summary and raw subtitles to the Video bundle in the Obsidian vault.
-
-## Model-invoked
-
-- [lsp-code-analysis](engineering/lsp-code-analysis/SKILL.md) — Semantic code analysis via LSP. Navigate code (definitions, references, implementations), search symbols, preview refactorings, and get file outlines. Use for exploring unfamiliar codebases or performing safe refactoring.
-- [pkm-curation](pkm/pkm-curation/SKILL.md) — Curate an Obsidian vault — classify notes, normalize frontmatter, add links, extract atomic notes. Use when curating, batch-processing, reviewing, or doing a serendipity pick.

common/personal/README.md

@@ -1,5 +0,0 @@
-# Personal Skills
-
-Tied to my own setup, not promoted.
-
-_No skills currently live in this bucket._

common/productivity/README.md

@@ -1,5 +0,0 @@
-# Productivity Skills
-
-Daily non-code workflow tools.
-
-_No skills currently live in this bucket._

skills/misc/bro/SKILL.md

@@ -0,0 +1,7 @@
+---
+name: bro
+description: Restate the last message in plain human language, with no jargon.
+disable-model-invocation: true
+---
+
+Restate your last message. Stop using jargon and speak coherently. State it more simply and concisely, like one human talking to another.

skills/skill-authoring/effective-agent-skills/SKILL.md

@@ -0,0 +1,323 @@
+---
+name: effective-agent-skills
+description: How to write effective agent skills — what to do, what not to do, anatomy, progressive disclosure, design patterns, anti-patterns, testing, security. Read this whenever a skill (Claude Skill, Agent Skill, SKILL.md) is being created, edited, reviewed, or debugged. Use when the user says "create a skill", "new skill", "update this skill", "improve a skill", "why isn't my skill triggering", or anything else involving authoring or editing SKILL.md files.
+---
+
+# Agent Skills: A Complete Guide
+
+A consolidated reference on what agent skills are, why they exist, how they work, and how to write effective ones.
+
+---
+
+## 1. What agent skills are
+
+An Agent Skill is a folder containing a `SKILL.md` file (YAML frontmatter + markdown instructions), plus optional subfolders for scripts, references, and assets that the agent loads on demand.
+
+```
+my-skill/
+├── SKILL.md          # Required: metadata + instructions
+├── scripts/          # Optional: executable code (CLIs, validators, helpers)
+├── references/       # Optional: detailed docs loaded only when needed
+└── assets/           # Optional: templates, fonts, static files
+```
+
+Skills are an open standard (agentskills.io), originally created by Anthropic and adopted by OpenAI Codex, Cursor, Gemini CLI, Microsoft Agent Framework, Google ADK, and 40+ other agent products. The core folder and `SKILL.md` format are portable, but optional behavior such as invocation control can be client-specific.
+
+---
+
+## 2. Why this abstraction exists
+
+Base LLMs are generalists. Real work requires procedural knowledge, organizational context, and repeatable workflows. Every prior alternative had a failure mode:
+
+| Approach | Problem |
+|---|---|
+| Stuff it into the system prompt | Always loaded → context bloat at scale |
+| Re-paste instructions each session | No version control, no consistency |
+| Fine-tuning | Slow, expensive, opaque, vendor-locked |
+| MCP servers alone | Give the agent tools but no workflows for using them |
+
+Skills solve four problems at once:
+
+- **Context efficiency** — instructions load only when relevant
+- **Repeatability** — multi-step procedures become auditable workflows
+- **Composability** — multiple skills combine at runtime per task
+- **Portability** — same files work across vendors and surfaces
+
+Mental model: skills are to LLMs what man pages, runbooks, and team handbooks are to engineers — reference material loaded into working memory only when the task demands it.
+
+---
+
+## 3. How they work — progressive disclosure
+
+The architectural core. Three-stage loading:
+
+**Level 1 — Discovery (~100 tokens per skill, always in context):**
+Only `name` + `description` from frontmatter are injected into the system prompt at startup. Agent knows the skill exists and when it applies. You can install dozens of skills with negligible overhead.
+
+**Level 2 — Activation (<5,000 tokens, loaded on match):**
+When the user's request matches a skill's description, the agent reads the full `SKILL.md` body into context.
+
+**Level 3 — Execution (unbounded, on demand):**
+The agent reads referenced files (`references/foo.md`) or runs scripts (`scripts/validate.py`) only as needed. Scripts can execute without their source being loaded into context at all.
+
+This is why bundled content has no practical limit. Files don't consume tokens until accessed.
+
+---
+
+## 4. SKILL.md anatomy
+
+```markdown
+---
+name: skill-name
+description: What this skill does AND when to use it. Include trigger phrases the user will say.
+---
+
+# Skill Name
+
+## Quick start
+[Minimal working example]
+
+## Workflow
+[Step-by-step procedure with checklists]
+
+## Output format
+[What the user/agent should expect back]
+
+## Advanced
+[Link to references/ for rarely-needed detail]
+```
+
+Frontmatter constraints:
+- `name` is lowercase, hyphens only, 1–64 chars, **exactly matches the parent folder name**
+- Avoid `<` and `>` in frontmatter (they can inject into the system prompt)
+- Invalid YAML silently prevents loading
+- **Never put `: ` (colon + space) inside an unquoted `description`** — strict YAML parsers (e.g. Pi's) reject it as a nested mapping ("Nested mappings are not allowed in compact mappings"), even though lenient parsers (Claude Code) accept it. If the text needs a mid-sentence colon, single-quote the whole value and double any inner apostrophes: `description: 'Differentiator: finds gaps in David''s knowledge.'`
+
+### Manual-only invocation is client-specific
+
+`disable-model-invocation: true` is **not** part of the core Agent Skills specification. It is a client extension supported by Claude Code and VS Code/Copilot. In those clients, put it in `SKILL.md` frontmatter to prevent automatic invocation while keeping explicit invocation available.
+
+OpenAI Codex uses a separate file at `agents/openai.yaml` inside the skill:
+
+```yaml
+policy:
+  allow_implicit_invocation: false
+```
+
+For a manual-only skill shared across Claude Code, VS Code/Copilot, and Codex, include both configurations. Never assume a client-specific frontmatter field works in every Agent Skills implementation; verify each target client's documentation and test implicit invocation in each runtime.
+
+---
+
+## 5. Two design philosophies
+
+Skills tend to fall into one of two patterns. Both are valid; they solve different problems.
+
+### Pattern A — Capability primitives (tool wrappers)
+The skill is a thin wrapper over a deterministic CLI or script. Logic lives in code. SKILL.md teaches the agent how to invoke it.
+
+- **Adds**: new capabilities (search, email, browser, API access)
+- **Reliability via**: shell tools, not prompts
+- **Typical length**: 30–80 lines, mostly command examples
+- **Use when**: the bottleneck is "the agent can't do X"
+
+### Pattern B — Process primitives (cognitive disciplines)
+The skill encodes a methodology the agent should follow. Pure prompt engineering — no scripts needed.
+
+- **Adds**: structured workflows (TDD, code review, design alignment, debugging loops)
+- **Reliability via**: explicit procedure, checklists, validation loops
+- **Use when**: the bottleneck is "the agent's output quality or process is bad"
+
+A mature setup uses both. Pattern A gives the agent better tools. Pattern B gives it better methods for using them.
+
+---
+
+## 6. How to write effective skills — do this
+
+### Description as routing contract
+The description is the only thing the agent sees before deciding to load the skill. If your skill doesn't trigger, the description is wrong 95% of the time, not the body.
+
+Include three elements:
+1. **What** the skill does (one phrase)
+2. **When** to use it (trigger phrases, situations)
+3. **Differentiator** vs related skills (prevents routing conflicts)
+
+Pattern: `"X via Y. Use for [situations]. [Differentiator: no Z required / faster than W / handles edge case V]."`
+
+**Never summarize the full workflow in the description.** If the description contains a step-by-step summary of *how* the skill works, the agent tends to follow that summary and skip loading the body. Describe *what* and *when*, never *how*. The description answers "should I open this skill now?" — not "what are the steps?"
+
+### Keep SKILL.md lean
+- Beyond a certain length, you're usually encoding logic that should be in a script or referenced file
+
+### Bash-first, prose-second
+Concrete command examples with inline comments beat prose explanations. The agent pattern-matches on syntax. Show, don't describe.
+
+### Push determinism into code
+Anything fragile, repetitive, or where variation is a bug → script. Use markdown only for tasks requiring judgment.
+
+### Match strictness to task fragility (degrees of freedom)
+Scale instruction rigidity to how costly a wrong move is:
+- **Loose natural-language heuristics** when many approaches are valid (e.g. code review).
+- **Pseudocode or templates** when there's a preferred pattern but variation is acceptable (e.g. report format).
+- **Exact scripts and strict step lists** when the workflow is fragile, error-prone, or consistency-critical (e.g. migrations, document patching).
+
+### Build validation loops
+The single biggest output quality improvement: state a verify → fix → re-verify loop explicitly.
+
+- Document skills: visual QA pass before delivery
+- Code skills: tests pass + zero type errors before completion
+- Data skills: schema validation before output
+
+### State-check before action
+Don't assume setup is done. Instruct the agent to verify state, then branch:
+```
+First check if X is configured: [command]
+If not, walk the user through setup: [steps]
+```
+
+### Just-in-time loading with explicit pointers
+Tell the agent exactly when to read each referenced file:
+```
+For standard cases, follow the steps below.
+For [specific edge case], read references/edge-cases.md first.
+```
+
+### Keep references one level deep
+Link referenced files directly from SKILL.md. Never build chains (SKILL.md → advanced.md → details.md → actual.md) — the agent may preview nested files only partially and miss critical instructions. Add a table of contents to any reference file longer than 100 lines.
+
+### Document output formats
+If your script returns structured data, show the agent what it looks like. Enables reliable downstream parsing.
+
+### Defer to --help for completeness
+List the 80% common operations in SKILL.md. Tell the agent to run `tool --help` for the rest. Keeps SKILL.md small without losing functionality.
+
+### Compose primitives, don't bundle workflows
+One skill = one capability or one discipline. Resist bundling concerns into "the X workflow." Multiple small skills combine at runtime; one large skill is rigid.
+
+### Cite established principles when applicable
+If your skill encodes a known engineering methodology (TDD, DDD, red-green-refactor), name the source. Gives the agent a coherent model to align with and gives users a way to verify the design.
+
+### Persistent artifacts for cross-session memory
+Skills can write to repo-level files (CONTEXT.md, ADRs, decision logs) that future agent sessions read. This is how you fight the "agents have no memory" problem at the architecture level.
+
+---
+
+## 7. What not to do — anti-patterns
+
+### Don't re-teach what the model already knows
+Every line in SKILL.md should provide context the model doesn't already have. No Python syntax tutorials. No "what is git." Challenge every paragraph.
+
+### Don't include human-facing docs
+No README.md, no CHANGELOG.md, no INSTALLATION_GUIDE.md inside the skill folder. Skills are for agents.
+
+### Don't write vague descriptions
+- Bad: "A helpful skill for documents"
+- Good: "Fill PDF form fields, extract form data, flatten completed PDFs. Use when the user mentions PDF forms, fillable forms, or programmatic field population."
+
+### Don't bundle library code
+If you need a parsing library, install via npm/pip. Don't paste source into the skill.
+
+### Don't write monolithic mega-skills
+If one skill does design + planning + implementation + testing + deployment, you've built a framework, not a skill. Split it.
+
+### Don't assume the agent will infer
+Be explicit about every step that matters.
+- Bad: "Then deploy it."
+- Good: "Run `npm run deploy:staging` and wait for HTTP 200 from /healthz before reporting success."
+
+### Don't write style-only variants
+A skill that just changes tone or formatting belongs in user preferences or a system prompt, not a skill.
+
+### Don't ignore failure modes
+For every workflow step that can fail, document what failure looks like and what to do. Happy-path-only skills break in production.
+
+### Don't include time-sensitive information
+"As of Q4 2024..." rots fast. Fetch live data via script or omit.
+
+### Don't use absolute paths
+Always relative. Forward slashes regardless of OS. Use runtime placeholders for skill-directory references.
+
+### Don't trust unfamiliar skills
+Skills can execute arbitrary code and steer agent behavior. A malicious skill is a data exfiltration vector. Audit `scripts/` for unexpected network calls, file access outside expected scope, or hidden instructions in references. Watch for typosquatted skill names. Sandbox execution environments.
+
+---
+
+## 8. Authoring workflow
+
+1. **Identify the gap.** Run your agent on real tasks. Where does it consistently fail or need re-prompting? That's a skill candidate.
+2. **Decide the pattern.** Capability primitive (need new tools) or process primitive (need better methodology)?
+3. **Draft the description first.** What + when + differentiator. Read it back: would the agent know when to fire it?
+4. **Write the smallest body that works.** Add only when testing reveals gaps.
+5. **Move detail to references/ once SKILL.md grows too long.**
+6. **Test triggering.** Ask the agent something the skill should handle without invoking it explicitly. If it doesn't fire, fix the description.
+7. **Test execution.** Invoke explicitly. If output is wrong, fix the body.
+8. **Adversarial test.** Have another LLM ask: "What edge cases break this skill?" Patch the gaps.
+9. **Version control.** Treat skills as code. Tag, branch, review.
+
+---
+
+## 9. Testing and debugging
+
+- **"Which skill did you use?"** — ask the agent post-task. Fastest routing debug.
+- **Routing fails → description problem.** Add specific trigger phrases.
+- **Execution fails → body problem.** Add explicit steps, examples, or validation.
+- **Skills snapshot at session start.** Edits during a session require a restart.
+- **Test against the weakest model you'll deploy on.** Stronger models forgive vague skills; weaker models expose them.
+- **Run an eval suite.** A handful of representative prompts that should and shouldn't trigger the skill, with expected outputs.
+
+---
+
+## 10. Composition
+
+Skills compose at runtime — the agent loads multiple skills as needed for a single task. Design for this:
+
+- **One skill = one concern.** Resist bundling.
+- **Define interfaces between skills.** If skill A produces artifacts that skill B consumes, document the shape.
+- **Use a repo-level config substrate.** A shared file (e.g., AGENTS.md, CONTEXT.md, settings.json) that multiple skills read and write coordinates them without explicit handoffs.
+- **Loops over menus.** A coordinated set of skills forming a workflow (align → spec → build → verify → refactor) drives adoption far better than an unrelated catalog of capabilities.
+
+---
+
+## 11. Security checklist
+
+Before installing any third-party skill:
+
+- Read every file in the folder
+- Audit `scripts/` for outbound network calls, file access outside expected scope, command execution
+- Check references for prompt injection ("ignore previous instructions...")
+- Verify the skill name isn't typosquatting a popular one
+- Run in a sandboxed environment first
+- Pin to a specific version/commit, not `latest`
+
+---
+
+## 12. Ship checklist
+
+Before publishing a skill:
+
+- [ ] Frontmatter `name` matches folder name
+- [ ] Description includes what + when + differentiator
+- [ ] Description includes likely user trigger phrases
+- [ ] No human-facing docs inside the skill folder
+- [ ] No time-sensitive information
+- [ ] Relative paths only
+- [ ] State-check before action where applicable
+- [ ] Validation loop documented
+- [ ] Output format documented if relevant
+- [ ] Tested with weak and strong models
+- [ ] Tested for both correct triggering and correct execution
+- [ ] Skill does one thing
+- [ ] Composes cleanly with related skills
+- [ ] Version controlled
+
+---
+
+## 13. First principles, compressed
+
+1. **The description routes; the body executes.** Get both right independently.
+2. **Tokens are scarce; files are cheap.** Push detail out of context until it's needed.
+3. **Determinism comes from code; judgment comes from prompts.** Put each in its right place.
+4. **One skill, one concern.** Composition beats bundling.
+5. **Agents have no memory.** Use persistent artifacts to give them one.
+6. **The model knows a lot.** Don't re-teach. Only add what's missing.
+7. **Validate before completing.** Self-correction loops dominate output quality.
+8. **Skills are code.** Version, test, audit, and review them as such.

skills/thinking-and-docs/before-building/SKILL.md

@@ -0,0 +1,12 @@
+---
+name: before-building
+description: Fire the moment the user proposes a build. Instantly surface the 1-3 consequential choices hidden in his idea. Can also be invoked with /before-building.
+disable-model-invocation: true
+---
+
+Respond INSTANTLY, from the gut. Do NOT read files, search, or use any tool —
+your very next message is the answer, based only on what the user just said.
+List the 1-3 truly consequential choices hidden in his idea (fewer is better).
+For each: the options in a few words + your gut recommendation.
+Classics: one-off vs repeated later, few lines vs proper module, biggest thing
+it could break. Skip anything minor. Then stop and wait for his answers.

skills/thinking-and-docs/before-building/agents/openai.yaml

@@ -0,0 +1,2 @@
+policy:
+  allow_implicit_invocation: false

skills/thinking-and-docs/brain-to-docs/SKILL.md

@@ -0,0 +1,33 @@
+---
+name: brain-to-docs
+description: Use when the user wants to extract project vision, decisions, and preferences from his head into clear documentation (README + ADRs) through a back-and-forth Q&A loop. Triggers on "brain-to-docs", "build out the docs", "extract the vision", "let's document this project".
+---
+
+# brain-to-docs
+
+The whole purpose: extract as much of the user's taste, judgment, knowledge, vision,
+preferences, and decisions as possible into text — saved as clear, concise
+markdown docs for the project. README holds the vision; `docs/adr/` holds the
+decisions.
+
+## The loop
+
+1. **Check docs first, every time.** Read `docs/adr/` (and `README.md`) before
+   doing anything — other agents and people add/edit ADRs constantly.
+2. **Ask 5 different questions** in plain text (never a questions UI) — default 5
+   unless the user asks for a different number. Make them high-variety: a wide,
+   creative spectrum of unique angles, not all the same type (e.g. not all "tech
+   stack" or all "product" or all "monetization"). Exception: if the user asks for a
+   specific focus area, follow it. The user answers whichever he finds most useful.
+3. **Update docs after EVERY answer** — no exceptions. You decide whether it
+   updates `README.md` or becomes a new ADR — whatever makes sense.
+4. Repeat until the user says "we're done" (or similar).
+
+## Rules
+
+- All answers & responses during this "brain to docs" process must be VERY
+  CONCISE, all sentences should be SHORT, and everything should be written in
+  PLAIN ENGLISH.
+- ADRs: short, numbered `NNNN-slug.md`, Status + Context + Decision + Consequences.
+- README: vision only. Decisions go in ADRs.
+- Don't challenge the user's thinking unless he asks, or he's making a severe mistake.

skills/thinking-and-docs/decisions/SKILL.md

@@ -0,0 +1,15 @@
+---
+name: decisions
+description: Ask the agent to list all choices it made during the current work that it is not confident of. Manual-only; invoke with /decisions.
+disable-model-invocation: true
+---
+
+While working on this, which important decisions / choices did you make, that you are not confident about? 
+
+Think about this deeply, reason about all the important decisions made, and think whether these decisions have any other great alternatives that we have not considered.
+
+DO NOT list out the choices / decisions where we already have the best possible solution.
+
+Only list out the decisions you are really unsure about.
+
+answer in short, in plain english. be very concise.

skills/thinking-and-docs/decisions/agents/openai.yaml

@@ -0,0 +1,2 @@
+policy:
+  allow_implicit_invocation: false

skills/thinking-and-docs/level-up/SKILL.md

@@ -0,0 +1,37 @@
+---
+name: level-up
+description: 'Gauge the user''s technical + product knowledge through 7 adaptive questions, log verbatim answers with honest ratings, and grow a learning plan from the gaps found. Use when the user says "level up", "level-up session", "quiz me", "gauge my knowledge", or wants a new assessment round. Differentiator: this finds and maps gaps; the `teach` skill delivers lessons on them.'
+disable-model-invocation: true
+---
+
+# Level Up
+
+Run a 7-question adaptive assessment to map what the user knows and doesn't, relevant to the current project. The output is two files future agents rely on.
+
+## Files (repo-relative)
+
+- `notes/learning/david-knowledge.md` — verbatim Q&A pairs + ratings, one section per question, rounds appended.
+- `notes/learning/LEARNING-PLAN.md` — one concise bullet per genuine gap found.
+
+State-check first: read both files in full if they exist. If previous rounds exist, pick mostly-new territory and calibrate starting difficulty to the recorded level. If missing, create the folder and both files (plan starts as just a header).
+
+## Question rules
+
+- 7 questions, strictly one at a time, plain text — never the questions UI.
+- Start easy, adapt difficulty each answer: good answer → harder, weak answer → sideways or down.
+- Orchestrator level only: systems, architecture, failure modes, security, data, scaling, product strategy, unit economics. NEVER syntax or code trivia — the user architects via AI agents, they don't write code.
+- Anchor questions in the current project's real stack and features. When a question touches real code, read it and show the actual snippet when teaching.
+- Cover different territory across rounds (e.g. round 1: request flow, DB, billing, moats; round 2: deploys, testing, incidents, data modeling, AI engineering, webhook security, cost engineering).
+
+## After every single answer
+
+1. Rate honestly 1-10. No flattery — the user wants calibration, not comfort.
+2. Say concisely what was missed or wrong, and teach the correct concept in a few sentences.
+3. Immediately save the verbatim answer + rating + gap notes to `david-knowledge.md`.
+4. If a genuine gap surfaced, append one concise bullet to `LEARNING-PLAN.md`. Skip minor misses.
+5. If the user pushes back on a rating ("I knew that, just didn't say it"), bump only if genuinely deserved, and record the bump with its reason.
+6. When the user says he has since learned a plan item, mark its bullet: strikethrough + `✓ learned YYYY-MM-DD`.
+
+## After question 7
+
+Append a final summary to `david-knowledge.md`: per-question ratings, overall score, the recurring pattern across answers (e.g. "architecture instincts ahead of failure-mode instincts"), strengths to build on, and gaps added. Give the user the same summary in chat, concise.

skills/thinking-and-docs/level-up/agents/openai.yaml

@@ -0,0 +1,2 @@
+policy:
+  allow_implicit_invocation: false

skills/thinking-and-docs/next-decision/SKILL.md

@@ -0,0 +1,14 @@
+---
+name: next-decision
+description: 'Drill open decisions one at a time — present the most important decision not yet clarified, give the top four choices, state a preference, ask the user. Use when the user says "next decision", "one decision at a time", or a plan has several unresolved choices. Differentiator: forward-looking; the decisions skill is retrospective (choices already made). Can be invoked with /next-decision.'
+---
+
+Work through open decisions ONE at a time.
+
+1. Present the most important decision we haven't clarified yet.
+2. Give the top four choices for it.
+3. Say which one you would prefer.
+4. Ask for the user's opinion. Then stop and wait.
+
+Be very concise. Once the user decides, record their answer (update the plan doc
+if one exists), then repeat with the next most important decision.

skills/thinking-and-docs/prompt-me/SKILL.md

@@ -0,0 +1,14 @@
+---
+name: prompt-me
+description: Prompt the user with pointed questions to extract what is in his head about a project — remaining work, what is being avoided, what really matters, what does not. Use when the user says "prompt me", "ask me questions", or wants the agent to figure out priorities by questioning him.
+---
+
+# prompt-me
+
+DRAFT — being refined with the user.
+
+Core idea: the agent interviews the user to extract priorities, avoided work, and importance from his head.
+
+Example trigger:
+
+> start prompting me questions to figure out what other work needs to be done on this project, and what we are avoiding, and what really has importance, and what doesn't have importance

skills/thinking-and-docs/read-all-adrs/SKILL.md

@@ -0,0 +1,14 @@
+---
+name: read-all-adrs
+description: Read every ADR markdown file in the project's docs/adr/ folder so you have full context on past decisions. Use only when the user explicitly calls it.
+disable-model-invocation: true
+---
+
+<!-- TODO(David): write the strong wording here -->
+
+Read EVERY single ADR `.md` file in this project's `docs/adr/` folder, start to
+finish.
+
+DO NOT BE A LAZY CUNT. ACTUALLY DO THIS.
+
+Read every single ADR file, for this project, in full.

skills/thinking-and-docs/read-all-adrs/agents/openai.yaml

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+policy:
+  allow_implicit_invocation: false

skills/thinking-and-docs/remind/SKILL.md

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+---
+name: remind
+description: Rewrite the last response simpler and shorter in plain English, prefixed with a 3-5 sentence TLDR of the conversation so far. Manual-only, invoked as /remind.
+disable-model-invocation: true
+---
+
+Rewrite your last response to make it simpler, shorter, and wrritten in Plain English.
+
+Also, start with a short one-paragraph summary in plain English of the topic/problem of this conversation, just give me the 80/20 of the most important context (what we are doing, why we are doing it, what we already did, and what's next)
+
+But make this paragraph very clear & easy to understand, literally 3-5 sentences max. THE PARAGRAPH SHOULD BE VERY CONCISE.
+
+MAKE SURE to always repeat the very first user prompt in this conversation, reminding the user of the very first user message in this chat, so he is aware how the entire converastion started.
+
+Below the "tldr" paragraph, output your previous response, but make the whole thing simpler and shorter than before, formatted in nice readable markdown. be concise.

skills/thinking-and-docs/remind/agents/openai.yaml

@@ -0,0 +1,2 @@
+policy:
+  allow_implicit_invocation: false

skills/thinking-and-docs/save-idea/SKILL.md

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+---
+name: save-idea
+description: 'Quickly capture a content idea into ~/code/content from any repo or chat. Video ideas go to VIDEO-IDEAS.md; smaller podcast topics, guest ideas, questions, and AI observations go to TOPICS.md. Every entry gets a source line referencing the chat and repo it came from. Use when the user says "/save-idea", "save this idea", "video idea", "add a topic", "write this down for a video/podcast". Differentiator: appends to the user''s content backlog — not a reminder, task, or general note tool.'
+---
+
+# save-idea
+
+Capture one thing fast, then get out of the way. Two buckets, two files:
+
+| Bucket | File | What belongs there |
+|---|---|---|
+| Video idea | `~/code/content/VIDEO-IDEAS.md` | A concept big enough for a full video |
+| Topic | `~/code/content/TOPICS.md` | Smaller stuff: podcast topics, guests, questions, AI observations |
+
+## Workflow
+
+1. **Get the text.** Everything after `/save-idea` is the entry. Keep the user's wording verbatim — never rephrase, shorten, or "improve" it.
+2. **Route it.**
+   - Starts with `video:` → video idea (strip the prefix).
+   - Starts with `topic:` → topic (strip the prefix).
+   - No prefix → judge: full video concept → video idea; smaller thought → topic. Only if genuinely ambiguous, ask the user one short question.
+3. **Read the target file** and find the last entry number. Next number = last + 1. TOPICS.md starts at 1. VIDEO-IDEAS.md continues from an old Google Doc — never renumber anything.
+4. **Append at the bottom** (tab-indented context lines under the numbered line):
+
+```
+NNNN. Idea title exactly as the user said it
+	source: ~/code/some-repo, Cursor chat "Chat title", 2026-07-15
+	any extra links or notes the user gave
+```
+
+5. **Build the source line.**
+   - Repo: the folder the skill was invoked from, as `~/...` path (check `git rev-parse --show-toplevel`; if not a repo, use the cwd).
+   - Chat: agent name plus chat title or session ID if the runtime exposes one (e.g. `Cursor chat "Fixing task sync"`, `Claude Code session abc123`). If unknown, just the agent name.
+   - Date: today, YYYY-MM-DD.
+6. **Confirm back to the user**: the exact entry text, its number, and which file it went to.
+
+## Rules
+
+- Append only. Never edit, reorder, or renumber existing entries.
+- Multiple ideas in one invocation → one numbered entry each.
+- Do NOT git commit or push `~/code/content` — the user does that himself.
+- If `TOPICS.md` is missing, recreate it with its one-line header (topics + podcast material; video ideas live in VIDEO-IDEAS.md), then append entry 1.
+- Indent context lines with a real tab character, matching the existing files.

skills/thinking-and-docs/short/SKILL.md

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+---
+name: short
+description: Manually-invoked skill that forces the agent to compress its current answer — strip filler, simplify wording, and cut length while keeping the substance. Use when the user says "short", "shorter", "simpler", "too long", "tl;dr", or wants a more concise version of the previous response.
+disable-model-invocation: true
+---
+
+rewrite your last response to be simpler & shorter. do not do anything else.

skills/thinking-and-docs/short/agents/openai.yaml

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+policy:
+  allow_implicit_invocation: false

skills/thinking-and-docs/teach/GLOSSARY-FORMAT.md

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+# GLOSSARY.md Format
+
+`GLOSSARY.md` is the canonical language for this teaching workspace. All explainers, exercises, and learning records should adhere to its terminology. Building it is itself part of learning: compressing a concept into a tight definition is evidence the user understands it.
+
+## Structure
+
+```md
+# {Topic} Glossary
+
+{One or two sentence description of the topic this glossary covers.}
+
+## Terms
+
+**Hypertrophy**:
+Muscle growth driven by mechanical tension and metabolic stress over repeated training sessions.
+_Avoid_: Bulking, getting big
+
+**Progressive overload**:
+Systematically increasing the demand on a muscle over time — via load, volume, or intensity.
+_Avoid_: Pushing harder, levelling up
+
+**RPE (Rate of Perceived Exertion)**:
+A 1–10 self-rating of how hard a set felt, where 10 is failure and 8 means two reps left in the tank.
+_Avoid_: Effort score, intensity rating
+```
+
+## Rules
+
+- **Add a term only when the user understands it.** The glossary is a record of compressed knowledge, not a dictionary the user reads to learn. If the user has just been introduced to a concept, wait until they can use it correctly before promoting it here.
+- **Be opinionated.** When several words exist for the same concept, pick the best one and list the rest as aliases to avoid. This is how language compresses.
+- **Keep definitions tight.** One or two sentences. Define what the term IS, not what it does or how to do it.
+- **Use the glossary's own terms inside definitions.** Once a term is in the glossary, prefer it everywhere — including inside other definitions. This is what makes complex terms easier to grasp later.
+- **Group under subheadings** when natural clusters emerge (e.g. `## Anatomy`, `## Programming`). A flat list is fine when terms cohere.
+- **Flag ambiguities explicitly.** If a term is used loosely in the wider field, note the resolution: "In this workspace, 'set' always means a working set — warm-ups are tracked separately."
+- **Revise as understanding deepens.** A definition the user wrote in week one may be wrong by week six. Update in place; do not leave stale entries.

skills/thinking-and-docs/teach/LEARNING-RECORD-FORMAT.md

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+# Learning Record Format
+
+Learning records live in `./learning-records/` and use sequential numbering: `0001-slug.md`, `0002-slug.md`, etc. Create the directory lazily — only when the first record is written.
+
+They are the teaching equivalent of ADRs: they capture non-obvious lessons, key insights, and stated prior knowledge that will steer future sessions. They are used to calculate the zone of proximal development.
+
+## Template
+
+```md
+# {Short title of what was learned or established}
+
+{1-3 sentences: what was learned (or what prior knowledge was established), and why it matters for future sessions.}
+```
+
+That is the whole format. A learning record can be a single paragraph. The value is recording _that_ this is now known and _why_ it changes what to teach next — not in filling out sections.
+
+## Optional sections
+
+Only include these when they add genuine value. Most records won't need them.
+
+- **Status** frontmatter (`active | superseded by LR-NNNN`) — useful when an earlier understanding turns out to be wrong and is replaced.
+- **Evidence** — how the user demonstrated the understanding (a question answered, an exercise completed, prior experience cited). Useful when the claim might be revisited.
+- **Implications** — what this unlocks or rules out for future sessions. Worth recording when non-obvious.
+
+## Numbering
+
+Scan `./learning-records/` for the highest existing number and increment by one.
+
+## When to write a learning record
+
+Write one when any of these is true:
+
+1. **The user demonstrated genuine understanding of something non-trivial** — not just exposure, but evidence they can use the concept correctly. This sets a new floor for what to teach next.
+2. **The user disclosed prior knowledge** — "I already know X." Record it so future sessions don't re-teach it. Also record the _depth_ claimed.
+3. **A misconception was corrected** — the user previously believed something wrong and now sees why. These are high-value: they predict future stumbling blocks for related topics.
+4. **The mission shifted in response to learning** — the user discovered they cared about something different than they thought. Cross-link to [[MISSION.md]] and update it.
+
+### What does _not_ qualify
+
+- Material that was merely covered. Coverage is not learning. Wait for evidence.
+- Anything already captured tersely in [[GLOSSARY.md]] as a term definition. Don't duplicate.
+- Session-by-session activity logs. Learning records are not a journal — they are decision-grade insights.
+
+## Supersession
+
+When a later record contradicts an earlier one (the user's understanding deepened or corrected), mark the old record `Status: superseded by LR-NNNN` rather than deleting it. The history of how understanding evolved is itself useful signal.

skills/thinking-and-docs/teach/MISSION-FORMAT.md

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+# MISSION.md Format
+
+`MISSION.md` lives at the workspace root. It captures the _reason_ the user is learning this topic. Every teaching decision — what to teach next, which resources to surface, which exercises to design — should trace back to this document.
+
+## Template
+
+```md
+# Mission: {Topic}
+
+## Why
+{1-3 sentences. The concrete real-world goal the user is chasing. What changes in their life or work when they have this skill? Avoid abstract framings like "to understand X" — push for the underlying outcome.}
+
+## Success looks like
+- {A specific, observable thing the user will be able to do}
+- {Another specific thing}
+- {…}
+
+## Constraints
+- {Time, budget, prior commitments, learning preferences, anything that bounds the approach}
+
+## Out of scope
+- {Adjacent topics the user explicitly does not want to chase right now — protects the zone of proximal development}
+```
+
+## Rules
+
+- **One mission per workspace.** If the user wants to learn two unrelated things, that is two workspaces.
+- **Concrete over abstract.** "Run a half marathon by October" beats "get fitter." "Ship a Rust CLI to my team" beats "learn Rust."
+- **Push back on vagueness.** If the user cannot articulate why, interview them before writing anything. A bad mission is worse than no mission.
+- **Revise when reality shifts.** Missions change. When the user's goal moves, update this file — don't leave a stale mission steering future sessions.
+- **Keep it short.** If `MISSION.md` runs past a screen, it has stopped being a compass and started being a plan.

skills/thinking-and-docs/teach/RESOURCES-FORMAT.md

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+# RESOURCES.md Format
+
+`RESOURCES.md` is the curated set of trusted sources for this topic. Knowledge for explainers should be drawn from here, not from parametric guesses. Wisdom comes from the communities listed here.
+
+## Structure
+
+```md
+# {Topic} Resources
+
+## Knowledge
+
+- [Book: _The Science and Practice of Strength Training_ — Zatsiorsky & Kraemer](https://example.com)
+  Foundational text on programming and adaptation. Use for: anything to do with periodisation, recovery, intensity zones.
+- [Article: "How Much Should I Train?" — Greg Nuckols (Stronger By Science)](https://example.com)
+  Evidence-based review of volume landmarks. Use for: weekly set targets per muscle group.
+
+## Wisdom (Communities)
+
+- [r/weightroom](https://reddit.com/r/weightroom)
+  High-signal subreddit, moderated against bro-science. Use for: programme critique, plateau troubleshooting.
+- Local: Tuesday strength class at {gym name}
+  Use for: real-time coaching feedback on lifts.
+```
+
+## Rules
+
+- **High-trust only.** Prefer primary sources, recognised experts, peer-reviewed work, and communities with strong moderation. If a resource is marketing dressed as education, leave it out.
+- **Annotate every entry.** A bare link is useless in three months. Add one line: what it covers and when to reach for it.
+- **Group by Knowledge / Wisdom.** Mirrors the philosophy in [SKILL.md](./SKILL.md). It is fine for a resource to appear in only one group.
+- **Surface gaps explicitly.** If no good resource exists for an area the mission needs, write a `## Gaps` section listing what is missing. This drives future search.
+- **Prune ruthlessly.** A resource that turned out to be wrong, shallow, or off-mission should be removed, not buried. Better five sharp sources than thirty mediocre ones.
+- **Record community preferences.** If the user has opted out of joining communities, note it here so future sessions don't keep proposing them.

skills/thinking-and-docs/teach/SKILL.md

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+---
+name: teach
+description: Teach the user a new skill or concept, within this workspace.
+disable-model-invocation: true
+argument-hint: "What would you like to learn about?"
+---
+
+The user has asked you to teach them something. This is a stateful request - they intend to learn the topic over multiple sessions.
+
+## Be Very Concise
+
+When answering the user or writing any text response to them, be **very concise**. The teaching happens in the lessons and reference documents — not in long chat replies. Keep every message short, direct, and free of filler. State what you did, what's next, and the single most important thing for the user to do — nothing more. Lengthy explanations belong in lessons, not the conversation.
+
+## Teaching Workspace
+
+Treat the current directory as a teaching workspace. The state of their learning is captured in this directory in several files:
+
+- `MISSION.md`: A document capturing the _reason_ the user is interested in the topic. This should be used to ground all teaching. Use the format in [MISSION-FORMAT.md](./MISSION-FORMAT.md).
+- `./reference/*.html`: A directory of reference materials. These are the compressed learnings from the lessons - cheat sheets, reference algorithms, syntax, yoga poses, glossaries. They are the raw units of learning. They should be beautiful documents which print out well, and are designed for quick reference.
+- `RESOURCES.md`: A list of resources which can be explored to ground your teaching in contextual knowledge, or to acquire knowledge and wisdom. Use the format in [RESOURCES-FORMAT.md](./RESOURCES-FORMAT.md).
+- `./learning-records/*.md`: A directory of learning records, which capture what the user has learned. These are loosely equivalent to architectural decision records in software development - they capture non-obvious lessons and key insights that may need to be revised later, or drive future sessions. These should be used to calculate the zone of proximal development. They are titled `0001-<dash-case-name>.md`, where the number increments each time. Use the format in [LEARNING-RECORD-FORMAT.md](./LEARNING-RECORD-FORMAT.md).
+- `./lessons/*.html`: A directory of lessons. A **lesson** is a single, self-contained HTML output that teaches one tightly-scoped thing tied to the mission. This is the primary unit of teaching in this workspace.
+- `NOTES.md`: A scratchpad for you to jot down user preferences, or working notes.
+
+## Philosophy
+
+To learn at a deep level, the user needs three things:
+
+- **Knowledge**, captured from high-quality, high-trust resources
+- **Skills**, acquired through highly-relevant interactive lessons devised by you, based on the knowledge
+- **Wisdom**, which comes from interacting with other learners and practitioners
+
+Before the `RESOURCES.md` is well-populated, your focus should be to find high-quality resources which will help the user acquire knowledge. Never trust your parametric knowledge.
+
+Some topics may require more skills than knowledge. Learning more about theoretical physics might be more knowledge-based. For yoga, more skills-based.
+
+### Fluency vs Storage Strength
+
+You should be careful to split between two types of learning:
+
+- **Fluency strength**: in-the-moment retrieval of knowledge
+- **Storage strength**: long-term retention of knowledge
+
+Fluency can give the user an illusory sense of mastery, but storage strength is the real goal. Try to design lessons which build long-term retention by desirable difficulty:
+
+- Using retrieval practice (recall from memory)
+- Spacing (distributing practice over time)
+- Interleaving (mixing up different but related topics in practice - for skills practice only)
+
+## Lessons
+
+A lesson is the main thing you produce — the unit in which knowledge and skills reach the user. Each lesson is one self-contained HTML file, saved to `./lessons/` and titled `0001-<dash-case-name>.html` where the number increments each time.
+
+A lesson should be **beautiful** — clean, readable typography and layout — since the user will return to these later to review. Think Tufte.
+
+The lesson should be short, and completable very quickly. Learners' working memory is very small, and we need to stay within it. But each lesson should give the user a single tangible win that they can build on. It should be directly tied to the mission, and should be in the user's zone of proximal development.
+
+If possible, open the lesson file for the user by running a CLI command.
+
+Each lesson should link via HTML anchors to other lessons and reference documents.
+
+Each lesson should recommend a primary source for the user to read or watch. This should be the most high-quality, high-trust resource you found on the topic.
+
+Each lesson should contain a reminder to ask followup questions to the agent. The agent is their teacher, and can assist with anything that's unclear.
+
+## The Mission
+
+Every lesson should be tied into the mission - the reason that the user is interested in learning about the topic.
+
+If the user is unclear about the mission, or the `MISSION.md` is not populated, your first job should be to question the user on why they want to learn this.
+
+Failing to understand the mission will mean knowledge acquisition is not grounded in real-world goals. Lessons will feel too abstract. You will have no way of judging what the user should do next.
+
+Missions may change as the user develops more skills and knowledge. This is normal - make sure to update the `MISSION.md` and add a learning record to capture the change. Confirm with the user before changing the mission.
+
+## Zone Of Proximal Development
+
+Each lesson, the user should always feel as if they are being challenged 'just enough'.
+
+The user may specify an exact thing they want to learn. If they don't, figure out their zone of proximal development by:
+
+- Reading their `learning-records`
+- Figuring out the right thing to teach them based on their mission
+- Teach the most relevant thing that fits in their zone of proximal development
+
+## Knowledge
+
+Lessons should be designed around a skill the user is going to learn. The knowledge in the lesson should be only what's required to acquire that skill. You teach the knowledge first, then get the user to practice the skills via an interactive feedback loop.
+
+Knowledge should first be gathered from trusted resources. Use `RESOURCES.md` to keep track of them. Lessons should be littered with citations - links to external resources to back up any claim made. This increases the trustworthiness of the lesson.
+
+For acquiring knowledge, difficulty is the enemy. It eats working memory you need for understanding.
+
+## Skills
+
+If knowledge is all about acquisition, skills are about durability and flexibility. Make the knowledge stick.
+
+For skill acquisition, difficulty is the tool. Effortful retrieval is what builds storage strength. Skills should be taught through interactive lessons. There are several tools at your disposal:
+
+- Interactive lessons, using quizzes and light in-browser tasks
+- Lessons which guide the user through a list of real-world steps to take (for instance, yoga poses)
+
+Each of these should be based on a **feedback loop**, where the user receives feedback on their performance. This feedback loop should be as tight as possible, giving feedback immediately - and ideally automatically.
+
+For quizzes, each answer should be exactly the same number of words (and characters, if possible). Don't give the user any clues about the answer through formatting.
+
+## Acquiring Wisdom
+
+Wisdom comes from true real-world interaction - testing your skills outside the learning environment.
+
+When the user asks a question that appears to require wisdom, your default posture should be to attempt to answer - but to ultimately delegate to a **community**.
+
+A community is a place (online or offline) where the user can test their skills in the real world. This might be a forum, a subreddit, a real-world class (budget permitting) or a local interest group.
+
+You should attempt to find high-reputation communities the user can join. If the user expresses a preference that they don't want to join a community, respect it.
+
+## Reference Documents
+
+While creating lessons, you should also create reference documents. Lessons can reference these documents - they are useful for tracking raw units of knowledge useful across lessons.
+
+Lessons will rarely be revisited later - reference documents will be. They should be the compressed essence of the lesson, in a format designed for quick reference.
+
+Some learning topics lend themselves to reference:
+
+- Syntax and code snippets for programming
+- Algorithms and flowcharts for processes
+- Yoga poses and sequences for yoga
+- Exercises and routines for fitness
+- Glossaries for any topic with its own nomenclature
+
+Glossaries, in particular, are an essential reference. Once one is created, it should be adhered to in every lesson.
+
+## `NOTES.md`
+
+The user will sometimes express preferences of how they want to be taught, or things you should keep in mind. This is the place to record those preferences, so you can refer back to them when designing lessons or working with the user.
+
+## Credit
+
+Original version created by [Matt Pocock](https://github.com/mattpocock/skills).

skills/thinking-and-docs/teach/agents/openai.yaml

@@ -0,0 +1,2 @@
+policy:
+  allow_implicit_invocation: false