how
Use for "how does X work", code walkthroughs before changing something, and placement / ownership / layering questions ("where should this live", "which…
Skill by Jrecos
Chalk Labs
Need help deploying how?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/jrecos-how
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/how
What's inside
- Skill name
how- Description
- Use for "how does X work", code walkthroughs before changing something, and placement / ownership / layering questions ("where should this live", "which package owns this", "is this the right layer"). Explains subsystem architecture, runtime flow, onboarding mental models. Use why for motivation.
- License
- MIT
- Instructions
- 32 lines in SKILL.md · view on GitHub
Files (3)
- SKILL.md
- explainer-prompt.md · references/
- explorer-prompt.md · references/
SKILL.md (first lines)
# How Explore the codebase to answer "how does X work?" questions. Produce architectural explanations at the level of a senior engineer onboarding onto a subsystem, enough to build a working mental model, not so much that it reads like annotated source code. Each spawn uses its configured role in `rules/pstack-models.md`, managed by `pstack_models` through `/setup-pstack`. Resolve `pstack_agent` with `role: "how explorer"` for explorers or `role: "how explainer"` for explainers and `kind: "readonly"`. Pass only the returned `agent` to native `task`. The returned `model` is metadata. If the role is unset or its model is unavailable, run `/setup-pstack`. Never substitute another model. `inherit-parent` and `auto` resolve to the live parent model through the returned agent. ## 1. Assess complexity If the scope is ambiguous, state your interpretation and explore. The user can redirect. - **Simple** (a single module, a small utility, a narrow question such as "how does function X work"): no explorers. Spawn one explainer that explores and explains in one pass, built from `references/explainer-prompt.md` without the explorer-findings section. Go to step 4. - **Complex** (a subsystem spanning multiple files or services, a cross-cutting feature, a full architectural overview): go to step 2. When in doubt, take the simple path. ## 2. Explore (complex only) Decompose the question into 2 to 4 angles, each a distinct slice of the subsystem. Spawn all explorers in a single native `task` batch, each with `references/explorer-prompt.md` and its angle filled in. ## 3. Synthesize (complex only) Once all explorers have returned, spawn one explainer with `references/explainer-prompt.md` and every explorer's findings filled in. ## 4. Present Present the explainer's output. Light edits for clarity or context from the conversation are fine. Do not substantially rewrite it. ## Output format The explanation uses the sections defined in `references/explainer-prompt.md`, dropping any that do not apply: Overview, Key Concepts, How It Works, Where Things Live, Gotchas.
From Jrecos/omp-pstack, MIT.
More from Jrecos/omp-pstack
- 0.601score
automate-me
Jrecos
Use for "automate me", creating or refreshing a personal -mode skill, capturing working preferences, or turning one narrow workflow into a reusable skill.
SkillAFreshness: A★ 3Claude Code +9 - 0.581score
create-verification-skill
Jrecos
Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform.
SkillAFreshness: A★ 3Claude Code +9 - 0.581score
architect
Jrecos
Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in.
SkillAFreshness: A★ 3Claude Code +9 - 0.581score
interrogate
Jrecos
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".
SkillAFreshness: A★ 3Claude Code +9 - 0.579score
make-bot-ui
Jrecos
Use when building a custom UI (page, dashboard, buttons) that should wake an agent routine over a local webhook, when the user must provide a webhook sender…
SkillAFreshness: A★ 3Claude Code +9 - 0.576score
arena
Jrecos
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it.
SkillAFreshness: A★ 3Claude Code +9 - 0.576score
maintain-verification-skill
Jrecos
Periodic pass that keeps a project's verification skill and feature map honest: parallel source readers per feature, one live session driving every feature…
SkillAFreshness: A★ 3Claude Code +9
Signals
- Stars
- 3
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 12, 2026 (18 days ago)
- Last release
- Oct 20, 2026 (10 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/Jrecos/omp-pstack
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from Jrecos
- 0.601score
automate-me
Jrecos
Use for "automate me", creating or refreshing a personal -mode skill, capturing working preferences, or turning one narrow workflow into a reusable skill.
SkillAFreshness: A★ 3Claude Code +9 - 0.581score
create-verification-skill
Jrecos
Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform.
SkillAFreshness: A★ 3Claude Code +9 - 0.581score
architect
Jrecos
Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in.
SkillAFreshness: A★ 3Claude Code +9 - 0.581score
interrogate
Jrecos
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".
SkillAFreshness: A★ 3Claude Code +9 - 0.579score
make-bot-ui
Jrecos
Use when building a custom UI (page, dashboard, buttons) that should wake an agent routine over a local webhook, when the user must provide a webhook sender…
SkillAFreshness: A★ 3Claude Code +9 - 0.576score
arena
Jrecos
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it.
SkillAFreshness: A★ 3Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get how running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
how skill. Agent Mart. https://agentmart.dev/skills/jrecos-how (last updated 2026-10-24)