Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline at the deploy seam. A merge into a branch that auto-deploys is not "done" until the deploy provider reports the merged commit SHA running and a healthcheck endpoint returns 200. Companion to the vendored finishing-a-development-branch skill — does not replace it, runs after it for projects on Railway, Cloudflare Workers, Vercel, Netlify, Fly.io, or any other auto-deploy target.
EnglishEnforces Law 2 (Plan Is Sacred) of the 7 Laws of AI Agent Discipline. Detects when a session has drifted away from its stated goal by scoring recent tool activity against the ' Goal' section of taskplan.md, so drift is caught mid-session instead of at end-of-session reflection.
EnglishEnforces all 7 Laws as a standing stance — go with Claude Code and the model, not against it. Skills are scaffolding that merges into the model over time; the durable core is goal-driven execution (the higher the stated goal, the better) plus self-discipline guardrails.
EnglishEnforces Law 5 (Reflect After Every Session) and Law 7 (Learn From Every Session) of the 7 Laws of AI Agent Discipline by giving the agent a durable file-based memory it can read on resume and write at session end. File-based memory system using Tiago Forte's PARA method. Use this skill whenever you need to store, retrieve, update, or organize knowledge across sessions. Covers three memory layers: (1) Knowledge graph in PARA folders with atomic YAML facts, (2) Daily notes as raw timeline, (3) Tacit knowledge about user patterns. Also handles planning files, memory decay, weekly synthesis, and recall via qmd. Trigger on any memory operation: saving facts, writing daily notes, creating entities, running weekly synthesis, recalling past context, or managing plans.
EnglishOrchestrator for all 7 Laws of AI Agent Discipline. Walks an agent-emitted recommendation list top-to-bottom under the 7 Laws — restate, route per item, verify before advancing, reflect at the end, close with the mandatory three-section block. Standalone with inline fallbacks; trigger phrases are matched by the companion hook, not enumerated here.
EnglishEnforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. After any failure in the verification ladder or auto-loop, classify the failure class before retrying — provider, tool-schema, deterministic-policy, git, worktree, runtime — so retry-vs-pause-vs-self-heal-vs-stop is an intentional decision, not a generic 'try again'.
EnglishUse this skill to prevent destructive operations when working on production systems or running agents autonomously.
EnglishEnforces Law 7 (Learn From Every Session) of the 7 Laws of AI Agent Discipline. Distills repeated successful tool sequences into reusable draft instincts, so a pattern that worked three times becomes a captured recipe instead of being re-derived from scratch every session.
EnglishEnforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline.
EnglishEnforces Law 2 (Plan Is Sacred) of the 7 Laws of AI Agent Discipline. Decision-framing lens that pairs WILD generation with RISA execution when emitting recommendation lists. Not a runtime hook.
EnglishEnforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. Pre-dispatch invariant: validate worktree root before any source-writing tool call.
English
AGENT SKILLS ATLASIncludes Deploy Receipt, Goal Monitor, Model Forward, and other workflows.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLISupported by the Atlas install prompt. Skill-specific requirements vary.
Pack stats
What’s included
Deploy Receipt
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline at the deploy seam. A merge into a branch that auto-deploys is not "done" until the deploy provider reports the merged commit SHA running and a healthcheck endpoint returns 200. Companion to the vendored finishing-a-development-branch skill — does not replace it, runs after it for projects on Railway, Cloudflare Workers, Vercel, Netlify, Fly.io, or any other auto-deploy target.
Goal Monitor
Enforces Law 2 (Plan Is Sacred) of the 7 Laws of AI Agent Discipline. Detects when a session has drifted away from its stated goal by scoring recent tool activity against the ' Goal' section of taskplan.md, so drift is caught mid-session instead of at end-of-session reflection.
Model Forward
Enforces all 7 Laws as a standing stance — go with Claude Code and the model, not against it. Skills are scaffolding that merges into the model over time; the durable core is goal-driven execution (the higher the stated goal, the better) plus self-discipline guardrails.
Monitor Stream
Stream live swarm events using the Monitor tool for real-time observability
Para Memory Files
Enforces Law 5 (Reflect After Every Session) and Law 7 (Learn From Every Session) of the 7 Laws of AI Agent Discipline by giving the agent a durable file-based memory it can read on resume and write at session end. File-based memory system using Tiago Forte's PARA method. Use this skill whenever you need to store, retrieve, update, or organize knowledge across sessions. Covers three memory layers: (1) Knowledge graph in PARA folders with atomic YAML facts, (2) Daily notes as raw timeline, (3) Tacit knowledge about user patterns. Also handles planning files, memory decay, weekly synthesis, and recall via qmd. Trigger on any memory operation: saving facts, writing daily notes, creating entities, running weekly synthesis, recalling past context, or managing plans.
Proceed with the Recommendation
Orchestrator for all 7 Laws of AI Agent Discipline. Walks an agent-emitted recommendation list top-to-bottom under the 7 Laws — restate, route per item, verify before advancing, reflect at the end, close with the mandatory three-section block. Standalone with inline fallbacks; trigger phrases are matched by the companion hook, not enumerated here.
Recovery Classification
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. After any failure in the verification ladder or auto-loop, classify the failure class before retrying — provider, tool-schema, deterministic-policy, git, worktree, runtime — so retry-vs-pause-vs-self-heal-vs-stop is an intentional decision, not a generic 'try again'.
Safety Guard
Use this skill to prevent destructive operations when working on production systems or running agents autonomously.
Skill Distillation
Enforces Law 7 (Learn From Every Session) of the 7 Laws of AI Agent Discipline. Distills repeated successful tool sequences into reusable draft instincts, so a pattern that worked three times becomes a captured recipe instead of being re-derived from scratch every session.
State Reconciliation
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. Pre-dispatch invariant: reconcile DB-vs-disk-vs-memory state before any unit runs, so a sta
Swarm Init
Initialize a multi-agent swarm with anti-drift configuration
Wild Risa Balance
Enforces Law 2 (Plan Is Sacred) of the 7 Laws of AI Agent Discipline. Decision-framing lens that pairs WILD generation with RISA execution when emitting recommendation lists. Not a runtime hook.
Worktree Safety
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. Pre-dispatch invariant: validate worktree root before any source-writing tool call. Catches
Quick start
- Choose your skills. Select individual skills above, or choose the entire pack.
- Copy the install prompt. Use Copy install prompt above the list.
- Send it to your agent. Paste into your coding agent’s chat. Your agent reviews the source and guides the setup.
Try it with your agent
After installation, start with a request like:
Use the Deploy Receipt skill to help with my task.
Ask me for the context you need before getting started.Requirements & license
- A coding agent that can access your project files and install skills.
- Your agent checks each skill’s instructions, dependencies, and permissions during setup.
- Reported license for the skills shown: MIT. The author’s terms apply.