dabydat/alpha-omega

Última actividad de origen registrada: 25 de septiembre de 2026 a las 06:21

Catálogo de SkillsMP actualizado: 2 de noviembre de 2026 a las 18:02

skills recopiladas: 34

Estrellas en GitHub: 1
Forks en GitHub: 0

Instalar con un asistente de IA

Copia este prompt en el asistente de IA que estés usando.

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/dabydat/alpha-omega.

README del repositorio

Archivo de origen del README: README.md

Leer el README original

Mostrar README completo

# alpha-omega — Agent Harness ## What is this? (in plain words) Think of it as a **standard operating procedure (SOP) for an AI coding tool.** Run `install.sh` in a project — it **references** the harness (it doesn't copy it). When you open the project with an AI coding tool (opencode, Claude Code, Cursor, Copilot, Gemini), the AI reads the gate and **stops improvising**. Instead of just doing whatever you type, it follows a disciplined process: read the config → understand your request → decompose it → plan it → ask you to approve → execute in the right order → verify only if you asked. **The result:** more consistent, higher-quality code, and fewer wasted tokens (it picks the right model/tier per task, and never reads more than it needs). **Who it's for:** developers who use AI coding tools (opencode, Claude Code, Cursor, Copilot, Gemini) and want consistent, quality code without burning tokens. **What it does:** turns a coding AI into a disciplined, token-efficient team that follows a clear loop. **What it does NOT do:** it is not a model, not a framework you code against, not a CI/CD system, and not a replacement for your project. > Each project holds a **thin gate** (`AGENTS.md` / `.claude/CLAUDE.md` / > `.cursor/rules/nsp-rev.mdc` / …) that reads `.alpha-omega/config.json`, then goes > to the **harness source** (where `install.sh` ran) and follows the loop. The > harness is **referenced, not copied**. ### TL;DR — quick setup (full steps below) ```bash # 1. edit config.json in the harness folder (set ai_org, stacks, actions, budget) # 2. run install.sh into your project ./install.sh /path/to/my-project # 3. open the project with your AI tool → it reads the gate → follows the harness ``` --- ## The 3 things that make it work 1. **`config.json`** — the control panel. The AI reads this first. 2. **The instructions** (`.claude/CLAUDE.md`, `protocol.md`, `GUIDE.md`, `rules/`, `skills/`) — the process it follows. 3. **The gates** — one thin entry per AI tool that points to the generic content (no duplication, no auto-registration). ### `config.json` — the control panel (explained, dummy-level) | Key | What it does | Example | |-----|-------------|---------| | `ai_org` | **which ONE AI tool to read.** Only that folder is read. | `"opencode"` | | `language_policy` | `follow_user` = answer in the user's language. `force_config` = always use `language`. | `"follow_user"` | | `switch.stacks` | **the active stacks** (array). Each stack's knowledge lives at `skills/<stack>/`. | `["nestjs", "react"]` | | `switch.components` | which content folders are on (`core`, `rules`, `skills`, `evals`). | `["core","rules","skills","evals"]` | | `switch.meta_prompting` | `true` = AI asks context once + shows a plan to approve. `false` = acts directly. | `true` | | `switch.feature_registry` | `true` = AI tracks every task in `features.json` (status + metrics). | `true` | | `mcp` | **MCP servers to use.** `servers` = list; `message` = what the AI must do when one is **not** connected (tell you how to connect it, then wait). | `{"servers":["pulsar"],"message":"..."}` | | `blocked_files` | **file patterns the AI must NEVER read** (secrets, .env, keys). | `[".env","*.pem","*.key"]` | | `scripts` | **which scripts the AI may run** (on/off). Sole gate — independent of `actions`. | `{"lint":true,"test":true}` | | `budget` | token limits per session. | `{"session_tokens":24000}` | | `actions` | **what the AI may do**, by power (git, files, execution, network, deploy). `true`/`false`. | `{"power_git":{"commit":false}}` | **The two most important for control:** `blocked_files` (secrets it must never read) and `actions` (what it's allowed to do). Both are checked on EVERY prompt. **Multiple stacks & skills (your point 5) — it's simple:** A skill's stack is just **the folder it lives in** (`skills/<stack>/`). To use a frontend + a backend stack, list both: ```json "switch": { "stacks": ["nestjs", "react"] } ``` - `skills/nestjs/` → the backend stack (its skills). - `skills/react/` → the frontend stack (its skills). Each skill belongs to a stack by being inside that stack's folder. That's it. --- ## What happens on the first prompt ### Do you need to do something before? **Just install + configure.** Everything else the AI does itself. ```mermaid flowchart TD subgraph ACTIVATION["① ACTIVATION — the 2 human steps"] direction TB A1[1 · Install the harness into the project ./install.sh /path/to/project] --> A2[2 · Edit config.json set the knobs: ai_org · switch.* · mcp · blocked_files · scripts · actions] end subgraph PROMPT["② THE FIRST PROMPT — the AI runs this. Each step is governed by a config key (shown in [brackets])"] direction TB P0[User writes a prompt] --> P1["Read config.json FIRST [ai_org] which folder to read"] P1 --> P1b["Check blocked_files [blocked_files] NEVER read those files"] P1b --> P1c["Check mcp [mcp] which servers to use"] P1c --> P2["Read the gate entry (CLAUDE.md / AGENTS.md) [switch.components] then your adapter state"] P2 --> P2b{adapter state exists?} P2b -->|no| P2c[AI CREATES its adapter state from memory/ templates — by itself] P2b -->|yes| P3{"[switch.meta_prompting]?"} P2c --> P3 P3 -->|yes| P4[Normalize to META-PROMPT ask context once if empty] P3 -->|no| P5[Operate directly] P4 --> P6[Decompose into sub-acts multi-intent: 1 prompt = several questions] P5 --> P6 P6 --> P7[Route each sub-act ROUTING.md §11 synapse complexity+intent → neuron → tier T1-T4] P7 --> P8["Check budget [budget] Σ budget_i ≤ session_tokens"] P8 --> P9["Load active stack knowledge [switch.stacks] skills/stack/ just-in-time"] P9 --> P10[Plan + APPROVAL GATE] P10 -->|A Approve| P11["Execute T1→T4, cheap first [actions] only what's allowed"] P10 -->|C Change| P9 P10 -->|X Cancel| P14[Cancel] P11 --> P12["Verify ONLY if you asked [scripts] or a script is enabled"] P12 --> P13[Reinforce write routes.jsonl] P13 --> P14[Consolidate your adapter state] P14 --> P14b{"[switch.feature_registry] on?"} P14b -->|yes| P14c["Update features.json [switch.feature_registry] status + metrics"] P14b -->|no| P15[Deliver result + evidence] P14c --> P15 end A2 --> P0 ``` **Which config key governs each step?** | Config key | Governs | Where in the flow | |-----------|---------|-------------------| | `ai_org` | which gate is read (opencode→`AGENTS.md`, claude→`.claude/CLAUDE.md`, etc.) | P1 — reading config | | `switch.stacks` | which stacks are active → `skills/<stack>/` | P9 — loading knowledge | | `switch.components` | which content folders are on (`core`/`rules`/`skills`/`evals`) | P2 — what's available to read | | `switch.meta_prompting` | ask context once + show a plan to approve, or act directly | P3/P4 — the meta-prompt gate | | `switch.feature_registry` | track every task in `features.json` | P14b/P14c — consolidate | | `mcp` | which MCP servers to use | P1c — before reading files | | `blocked_files` | files the AI must NEVER read | P1b — before reading files | | `scripts` | which scripts the AI may run (lint/test/build…) | P12 — verify step | | `budget` | token limits per session | P8 — budget check | | `actions` | what the AI may do, by power (git/files/execution/network/deploy) | P11 — execute (and everywhere it acts) | **Key points:** - **You only do 2 things before** (install + edit config). No manual state setup. - **The AI self-initializes**: if its adapter state is missing, the AI creates it (from the `memory/` templates — the format) so it can keep control over time. - **Verification is on-demand** (your point 6): lint/tests run only when **you ask** or when the script is **enabled** in `config.json → scripts`. - **Once configured, it's on from the first read of `config.json`** — no mid-session activation. --- ## The map (where everything is) | Need | File | |------|------| | The law (loop, tiers, contracts) | `protocol.md` | | The agent manual (how to operate, what NOT to do) | `GUIDE.md` | | Master entry (the model reads this first) | `.claude/CLAUDE.md` | | The control panel (ai_org, stacks, scripts, actions) | `config.json` | | Routing (single source: situation → agent + complexity → tier) | `ROUTING.md` | | Action control by powers | `actions.md` | | State — **the live one, written by the AI** | your adapter folder (e.g. `.claude/memory/STATE.md`) | | State — **the template, never written** | `<harness>/memory/STATE.md` | | Generic agent roster (all adapters) | `agents/` | | Generic rules (never omit) | `rules/` | | Generic commands (what you can run) | `commands/` | | Universal skills | `skills/<name>/` | | **Active stack knowledge** (`skills/<stack>/` per stack) | `skills/nestjs/` | | The pattern linter (deterministic quality gate) | `scripts/lint-patterns.js` | | Honest pheromone + budget allocation | `scripts/routes.js` · `scripts/budget.js` | | The gates — **one thin entry per tool** (points to generic, no auto-registration) | `AGENTS.md` (opencode) · `.claude/CLAUDE.md` (claude) · `.cursor/rules/nsp-rev.mdc` · `.github/copilot-instructions.md` · `.gemini/GEMINI.md` | > **Gates are thin, not copies.** The generic content (agents, rules, commands, > skills, neurons, config) lives at the root and is loaded **just-in-time**. Each > gate is ONE file that reads `config.json` and points to the generic content. > Nothing is auto-registered, so there is no token leakage. --- ## Feature tracking (when features.json / features.schema.json activate) These two files are **one feature tracker** (NOT "for Anthropic" or "for GPT" — they work together as data + schema): - **`features.json`** — the state of your features (id, status, tier, tokens, score, time). - **`features.schema.json`** — the contract that validates `features.json` (JSON Schema). - `scripts/features.js` validates the data against the schema. **When do they activate?** Only when `config.json → switch.feature_registry` is `true` (default). When on, **every task** the AI does registers/updates a feature entry and moves it through its lifecycle: ``` proposed → planned → in_progress → blocked → done | rejected ``` It records real metrics per feature (time_actual_ms, tokens_actual, score, retries). **If you don't want feature tracking**, set `switch.feature_registry: false` (and you can delete `features.json` + `features.schema.json`). --- ## Setup (do this once per project) > **BEFORE you install, edit the harness's `config.json`** (the one in the > alpha-omega folder, where `install.sh` lives). `install.sh` copies that config > into the new project — so set it once and every project starts already configured. ### Step 0 — Set the harness config (in the alpha-omega folder) Edit `config.json`: ```json { "ai_org": "opencode", // your AI tool: opencode | claude | cursor | copilot | gemini "switch": { "stacks": ["nestjs"] }, // your stack(s) → knowledge at skills/<stack>/ "actions": { "power_files": { "write": true } }, // what the AI may do "budget": { "session_tokens": 24000 } // token limit per session } ``` ### Step 1 — Install into a project (references the harness, doesn't copy it) ```bash cd /path/to/alpha-omega # the folder where install.sh is ./install.sh /path/to/my-project ``` It creates in `my-project`: - `.alpha-omega/config.json` — this project's config (copy of step 0). - `AGENTS.md` — a thin gate: reads `.alpha-omega/config.json`, then *"goes to the harness"* at the source (where `install.sh` ran). - `.opencode/` (or your `ai_org`) with `memory/` — the project's traceability. - It also **appends** these created paths to the project's `.gitignore` (so they are **not** committed to GitHub). ### Step 2 — Open the project with your AI tool Open `my-project` with opencode / Claude Code / Cursor / etc. It **auto-reads the gate** (`AGENTS.md` / `CLAUDE.md` / `.cursor/rules/nsp-rev.mdc`) — you don't invoke the harness manually. The gate points to `.alpha-omega/config.json` and the source harness, and the AI follows the loop. ### Step 3 — Write your first task > *"Add a GET /users/:id endpoint. EDD: spec, implement, evaluate."* The harness decomposes → routes → plans (asks you to approve `[A/C/X]`) → executes → verifies → consolidates. ### Step 4 — Verify it worked ```bash cd /path/to/alpha-omega # the harness — scripts live here, not in the project node scripts/lint-patterns.js # quality gate (0 violations = OK) ``` Check the traceability in `.claude/memory/STATE.md` (or your `ai_org`). That file arrives holding only its title and a pointer back to the harness — the AI fills in the rest. The format it follows lives in `<harness>/memory/STATE.md`, which stays untouched. --- ## How the flow works (the mental model) ``` 1. Read .alpha-omega/config.json ← this project's control panel 2. Go to the harness (source) ← the pwd where install.sh ran 3. Execute the rules it declares ← loop, routing, agents, skills 4. Deliver in the project 5. Leave traceability in <ai_org>/memory/ ``` > The harness is **referenced, not copied** — it lives in one place (where > `install.sh` runs). Each project only holds a thin pointer. If the gate already > exists, the installer **prepends** alpha-omega (so it takes precedence over any > other flow/harness). --- ## Quality gate (on demand) `scripts/lint-patterns.js` checks real code against `rules/coding.md`: `file-too-long` · `console-log` · `long-function` · `too-many-params` · `controller-repo` · `query-returns-entity` · `vo-not-frozen`. It's deterministic (~0 tokens) and exits `1` on violations. Run it **only when you ask**: `node scripts/lint-patterns.js` or `/lint`. It only runs if `config.json → scripts.lint` is `true`. --- ## Actions (powers) `config.json → actions` controls what the AI may do. `true` = allowed, `false` = forbidden. ``` power_git: create_branch ✓ switch_branch ✓ commit ✗ push ✗ power_files: read ✓ write ✓ edit ✓ delete ✗ power_execution: read_only_search ✓ tests ✓ build ✓ install ✓ mutating_fs ✗ network_egress ✗ db_write ✗ vcs_write ✗ power_network: fetch ✗ web_search ✗ power_deploy: staging ✗ production ✗ rollback ✗ ``` **Golden rule:** the CONFIG rules. If an action is `false`, the AI does not do it. ### Powers are grouped by EFFECT, not by tool `bash: true|false` used to be one switch for two very different things: *looking at your code* and *destroying it*. Turning it off to prevent `rm -rf` also blocked `grep`, which made the harness unusable and — predictably — got ignored. So it split: | Power | Covers | Default | |---|---|---| | `read_only_search` | `grep` `glob` `find` `ls` `cat` `head` `wc` `jq` | ✓ allowed | | `run_tests` / `build` / `install_dependencies` | project tooling | ✓ allowed | | `mutating_fs` | `rm` `mv` `dd` `truncate` `chmod -R` | ✗ | | `network_egress` | `curl` `wget` `nc` `scp` `ssh` | ✗ | | `db_write` | `DROP` `TRUNCATE` `DELETE FROM` | ✗ | | `vcs_write` | `commit` `push` `reset --hard` `clean` | ✗ | Plus a `denylist` that is refused **regardless of any power above**, even if you ask. > **`read_only_search` never overrides `blocked_files`.** A search is harmless > until you point it at a secret: `grep KEY .env` exfiltrates exactly as much as > `cat .env`. Search freely — never at a blocked path. ### Scripts are not shell access `node scripts/<name>.js` is gated by `scripts.<name>` **and nothing else**. These are deterministic (~0 tokens, fixed output), so they are never subject to `power_execution` — a locked-down execution power does not disable them. **Invariant:** `switch.metrics: true` ⇒ `scripts.metrics: true` (same for `feature_registry`/`features`). Breaking it is a config error the AI must report, not silently skip. --- ## Copy to a new project ```bash ./install.sh /path/to/new-project/ ``` It reads `ai_org` from `.alpha-omega/config.json` and creates only the **pointer** (`.alpha-omega/config.json` + the gate + `<ai_org>/memory/`). It does **not** copy the harness — it references it from where `install.sh` runs. Edit `.alpha-omega/config.json` in the new project to set its own stack/actions/budget.

Cargando el README del repositorio…

Skills en este repositorio

clasificación pendiente

sin clasificar

Token economy and context optimization. Use when seeking to minimize token consumption or the context exceeds 60%.

Idioma del texto original: inglés

2026-09-25

Mostrando 34 de 34 skills recopiladas.