Third Brain V8.1 Skills
LLM wiki kit
First committed by Mark393295827
Published in Mark393295827/third-brain-v7-skills. Publisher page
140 GitHub stars
What it is
agent wiki +engineering skills
AutoGineer overview, from the brain’s own files
Third Brain V8.1 provides a structured, verification-first operating model for AI agents, treating the LLM as a CPU, context as RAM, and an Obsidian vault as a durable disk. It offers 21 reusable agent skills designed to manage knowledge ingestion, daily loops, research, and multi-agent orchestration. The system enforces strict provenance, governance, and quality gates, ensuring that agent actions are backed by evidence and that knowledge compounds over time through a structured taxonomy.
How it works
Memory model: LLM wiki
AutoGineer overview, from the brain’s own files
The system organizes information into a structured folder hierarchy including sources, concepts, entities, and maps. It uses a 5-stage worker assembly pipeline (Ingest, Cognitive, GraphWeaver, Governance, Deliverable) to process information. Skills are executed as programs within a harness that provides scheduling, permissions, and observability. It relies on a transactional runtime to manage state and ensure that all knowledge updates are verified and linked within the Obsidian vault.
| Folder | Role | Files |
|---|---|---|
| .github | agent-config | 5 |
| assets | attachments | 5 |
| commands | agent-instructions | 4 |
| docs | docs | 21 |
| examples | examples | 10 |
| hooks | hooks | 1 |
| skills | agent-instructions | 40 |
| sources | raw-sources | 13 |
| tests | tests | 8 |
| tools | scripts | 51 |
| adapters | folder | 2 |
| artifacts | folder | 28 |
| contracts | folder | 18 |
| core | folder | 6 |
| experiments | folder | 7 |
| maps | folder | 1 |
| outreach | folder | 46 |
| system | folder | 33 |
| workflows | folder | 1 |
Requirements
Works with: Claude Code, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Amp, Cline, JetBrains Junie, Devin (ex-Windsurf)
Set it up
This brain runs code automatically: 2 scripts. Review install.ps1, install.sh before opening the folder in Claude Code. No step below turns a hook on for you.
install.ps1:pwsh install.ps1install.sh:sh install.sh
Clone the repository at the commit AutoGineer scanned:
git clone https://github.com/Mark393295827/third-brain-v7-skills.git && cd third-brain-v7-skills && git checkout 5a645148a108d71c76521715117b737ae60b1423Claude Code reads CLAUDE.md from this folder automatically.
cd third-brain-v7-skills && claude
AutoGineer scanned these files at commit 5a64514.
First committed by Mark393295827. Published in Mark393295827/third-brain-v7-skills. Licence: MIT License.
Usage
AutoGineer overview, from the brain’s own files
- Ingesting research papers or articles into a linked wiki
- Running daily knowledge loops to compound insights
- Enforcing verification gates before claiming task completion
- Orchestrating multi-agent teams with isolated write scopes
- Managing context budgets and state checkpoints for long-running tasks
- “Use wiki-ingest on this source. Stage an immutable source, author a governed concept candidate, plan navigation updates, and return a verification summary.”
- The agent creates an immutable note in the sources folder, updates the relevant concept pages in the wiki, and provides a summary of the verification steps taken.
- “Run the daily-okr skill for today.”
- The agent executes the 7-KR cycle, generating a daily insight, updating the wiki, and producing a receipt for the completed work.
Documentation by Mark393295827
Third Brain V8.1 Skills
<p align="center"> <img src="assets/third-brain-v8.1-actual-system-architecture.png" alt="Third Brain V8.1 actual Codex OS architecture: Codex host, repository control plane, transactional worker runtime, live Obsidian Vault, verification evidence, and declared debt" width="900"> </p>
Verification-first Agent Skills for Codex OS, with optional compatibility adapters for Claude Code, Gemini, Cursor, and Windsurf. Build a persistent knowledge operating system with Obsidian provenance, scheduled loops, bounded static dependency graphs, context management, and multi-agent orchestration.
Install 21 reusable agent skills for ingesting sources, auditing workflows, compiling interlinked wikis, running daily knowledge loops, verifying claims before shipping, managing token costs, engineering bounded loops and static dependency graphs, and orchestrating multi-agent teams.
Use this if:
Your AI agent keeps forgetting context between sessions
You want to enforce verification gates before "done" claims
You need to structure knowledge as linked pages (like Obsidian) instead of scattered chat history
You're building agent workflows that should learn and compound over time
You want Obsidian wiki knowledge to update skills, SOPs, schemas, and automation through supervised promotion gates
Image: GitHub stars Image: License: MIT Image: Codex OS Image: Claude Code Image: Cursor Image: PRs Welcome
Quick Start
1. Clone & Install
git clone https://github.com/Mark393295827/third-brain-v5-skills.git
cd third-brain-v5-skills
bash install.sh codex # Codex is the primary host/kernelWindows PowerShell uses .\install.ps1 codex. Compatibility adapters require an explicit non-Codex target.
2. Try a Skill
Paste this into Codex:
Use wiki-ingest on this source. Stage an immutable source, author a governed concept candidate, plan navigation updates, and return a verification summary. Treat entity authoring as a separate reviewed capability.Then paste a URL, article, PDF text, or any source you want to capture.
3. Next Steps
See the 3-Minute Quickstart for a complete walkthrough.
Full guide: GUIDE.md
Command centre and distribution: V8.1 Codex Agentic OS command centre. Start it with python tools/agentic_os_server.py --state-root .agentic-os-state; the host is localhost-only by default and requires T02 runtime receipts. Claude/Gemini/Cursor/Windsurf remain compatibility adapters, not the primary runtime.
中文历史进阶手册:V7 最大潜力使用手册(参考)
🔥 V8.1 多智能体 Worker 流水线手册 & Worker Flows 规范:事务式流水线(Stage → Author → Graph Plan → Govern → Commit/Postcheck → optional Deliverable),归档只发生在验证提交之后。
The Problem
| Scenario | Before | After |
|---|---|---|
| Research PDF | Summarized once, then forgotten | wiki-ingest: source notes, concept pages, linked wiki |
| Coding session | Agent: "I fixed it" (no proof) | verify-before-claim: requires test output + exit code before any claim |
| Daily work | Tasks and ideas scatter across chat | daily-okr: one insight, one wiki update, one action, one output, daily score |
V8.1 Operating Model
V8.1 keeps the wiki as durable disk and governance, gives every skill a profile-aware execution contract, and makes python -m tools.worker_flow.cli the only canonical transactional runtime:
Input -> Source -> Wiki compile -> Daily loop -> Agent/Wiki flywheel -> Skill/SOP upgrade -> VerificationThe upgrade adds seven hard defaults:
Profiles match risk: one-shot, stateful, loop, and high-risk work load only the controls they need.
Source provenance stays immutable: raw source notes and block refs remain the audit layer.
Loops have contracts: Trigger -> Execute -> Verify -> State, with budgets and recovery.
Context is zero-overhead by default: hot paths load only what the task needs.
Automation is bounded: scans and queues can run unattended; semantic writes stay supervised.
Teams need ownership: multi-agent work requires write scopes, IPC, join gates, cleanup, and evidence.
Rules promote through evidence: wiki insights become schema or skill rules only after repeated support and a cheap check.
See the V8.1 Worker Flow, the Agent Skills standard, and the historical V7 release notes.
V8.1 keeps graph-engineering bounded to static DAGs; dynamic expansion and cyclic graphs remain out of scope.
Core Skills (Start Here)
🧠 Knowledge & Verification
| Skill | What it does |
|---|---|
| wiki-ingest | Preserve an immutable source, compile linked understanding, enforce promotion boundaries, and verify every touched file. |
| verify-before-claim | Iron rule: no completion claim without fresh, scope-matched evidence; consequential actions also require independent verification, approval, and rollback. |
📅 Daily Loop
| Skill | What it does |
|---|---|
| daily-okr | Execute a 7-KR cycle: Input → Cognition → Wiki → Behavior → Creativity → Output → Feedback, with an artifact or receipt for every completed KR. |
🎯 Behavior & Creativity
| Skill | What it does |
|---|---|
| behavior-design | Turn outcomes into observable minimum behavior, cues, SOPs, recovery, evidence, and review. |
| creativity-engine | Generate mechanism-diverse options, rank evidence gaps, and define three bounded minimum experiments. |
🔬 Research & Quality
| Skill | What it does |
|---|---|
| deep-research | STOW-compatible research harness with evidence trails, source/claim ledgers, privacy checks, and direct handoff to wiki-ingest. |
| session-learn | Extract knowledge patterns from sessions — concepts, entities, corrections, patterns, ideas, decisions, gaps. Closure protocol ensures learning feeds back into the wiki. |
📊 Context & Engineering
| Skill | What it does |
|---|---|
| context-manager | Derive budgets at runtime, checkpoint state, compact with KEEP/SUMMARIZE/DROP/RETRIEVE, and route by capability. |
| loop-engineering | Control temporal depth: turn repeatable work into a bounded Trigger -> Execute -> Verify -> State loop with durable state, finite budgets, and explicit stop/recovery rules. |
| graph-engineering | Control dependency width: validate bounded static DAGs with explicit dependencies, independent branches, typed joins, and node-local recovery. |
| agentic-engineering | Design agent workflows as spec-driven macro actions with quality ceilings, verification gates, delegated-action boundaries, and state checkpoints. |
| harness-engineering | Provide the runtime scheduler, permissions, leases, tools, provenance, and observability needed to execute agent workflows safely. |
| agent-teams-command | Admit and command multi-agent processes with exclusive ownership, typed IPC, isolated writes, serial integration, evidence, and cleanup. |
Complete Skill List
📥 Ingestion & Knowledge
wiki-ingest— Stage immutable sources, author governed concept candidates, plan graph updates, and hand off optional entity authoring for separate reviewknowledge-ops— Multi-layer knowledge management: classify, deduplicate, preserve evidence hierarchy, vector + Markdown retrievalwiki-lint— Health check: P0/P1 graph health, frontmatter, source refs, wikilink density, provenance debt
🔄 Daily Workflow
daily-okr— 7-KR evidence cycle with minimum-day degradation and durable closeoutcognitive-compile— 8-section framework: Question → Facts → Concepts → Pattern Recognition → Conflict Detection → Hypothesis Generation
🎨 Behavior & Creativity
behavior-design— Outcome → minimum behavior → cue → SOP → evidence → reviewcreativity-engine— Mechanism-diverse combinations, scoring, and falsifiable minimum tests
🔬 Research & Quality
deep-research— STOW-compatible research harness, evidence trails, source/claim ledgersverify-before-claim— Fresh scope-matched evidence, independent checks, approval, rollback
🔄 Learning & Flow
session-learn— 7 signal types: concepts, entities, corrections, patterns, ideas, decisions, gapsproject-flow-ops— Execution flow: triage, plan, track, review across projects
📊 Context
context-manager— Runtime budgets, checkpoint replay, compaction, retrieval, capability routing
Utility command: commands/token-cost-tracker.md estimates, logs, and reports token usage; it is not one of the 21 Agent Skills.
🏗️ Engineering
loop-engineering— Temporal depth through bounded loop contracts, independent verification, finite budgets, and stop/recovery rulesgraph-engineering— Dependency width through bounded static DAGs, explicit branches, typed joins, and node-local recoveryagentic-engineering— Spec-driven macro actions, quality ceilings, verification gates, state checksharness-engineering— Runtime scheduler, permissions, system-call tools, delegated gates, provenance, observabilityagent-teams-command— Multi-agent process ownership, IPC, integration, async budgets, and evidence gates
💼 Strategy & Operations
startup-evaluation— Startup health: entrepreneurship, VC 5T, PMF, runway, team, unit economicsanthropic-os— Governed operating-system experiments: Four-C, 3B, prediction errors, permission ladderai-six-sigma-property-os— AI + Ontology + DMAIC for property work orders, dispatch, quotes, evidence
How These Skills Fit Together
LLM (CPU) + Context (RAM) + Wiki (Disk)
External Sources ──→ wiki-ingest + knowledge-ops ──→ Knowledge Layers
↓
Daily Loop (daily-okr)
/ / \ \ \
Input Cognition Wiki Behavior Creativity
|
Output → Feedback
↓
session-learn (extract patterns)
↓
verify-before-claim (quality gate)
↓
Loop depth / Graph dependency width
↓
Multi-agent teams (agent-teams-command)The system is a closed loop: ingest sources → process daily → extract learning → verify claims → promote rules → scale to teams.
Architecture & Design
Third Brain V8.1 treats agents as LLM OS processes:
LLM = CPU
Context = RAM
Wiki/Obsidian = Disk
Tools = System calls
Skills = Executable programs
Harness = Kernel
Agent teams = Processes
Design Layers
| Layer | Principle | Skills |
|---|---|---|
| 🧠 Knowledge OS | Capture, structure, lint, and promote knowledge over time | wiki-ingest, knowledge-ops, wiki-lint |
| ⚡ Daily Loop | Close the knowledge-to-action cycle every day | daily-okr, cognitive-compile |
| 🎯 Behavior & Creativity | Turn knowledge into habits and novel ideas | behavior-design, creativity-engine |
| 🔬 Research & Quality | Verify before claiming, research with rigor | deep-research, verify-before-claim |
| 🔄 Continuous Learning | Extract patterns from every session | session-learn, project-flow-ops |
| 📊 Context | Manage hot context, durable checkpoints, and runtime budgets | context-manager |
| 🏗️ Engineering | Design bounded loops, static dependency graphs, harnesses, agent workflows, and multi-agent systems | loop-engineering, graph-engineering, agentic-engineering, harness-engineering, agent-teams-command |
| 💼 Strategy & Operations | Evaluate startups, design AI quality systems | startup-evaluation, anthropic-os, ai-six-sigma-property-os |
Engineering boundaries are deliberate: Loop Engineering controls temporal depth; Graph Engineering controls dependency width; Agent Teams Command controls process ownership, IPC, and integration; Harness Engineering supplies the runtime scheduler, permissions, and observability. A graph node may contain a Loop or Agent Team, but V8.1 does not support dynamic or cyclic graphs.
Skill Adoption Path
Start small. Add skills as you need them.
| Stage | Core Skills | Unlock When |
|---|---|---|
| Week 1 | wiki-ingest + verify-before-claim | You can ingest 1 source/day + every claim has fresh evidence |
| Weeks 2-4 | + daily-okr + session-learn | Daily artifacts and verified learnings feed back to the wiki |
| Month 2+ | + cognitive-compile + behavior-design + creativity-engine | Wiki has 50+ pages or repeated decisions need synthesis |
| Month 3+ | + knowledge-ops + loop-engineering + harness-engineering + agentic-engineering | Retrieval, looping reliability, permissions, or delegated actions become bottlenecks |
| Dependency graph | + graph-engineering after loop-engineering | Explicit dependencies, independent branches, typed joins, or node-local recovery create more value than orchestration and review cost |
| Multi-agent | + agent-teams-command + project-flow-ops | Work splits into separate owners with clear integration gates |
| Strategy | + startup-evaluation + anthropic-os + deep-research | Need startup health, market, operating-system decisions |
| Operations | + ai-six-sigma-property-os | Need measurable service quality, dispatch, evidence loops |
Installation
For Codex CLI (Primary host/kernel)
# Personal skills (available across all projects)
bash install.sh codexFor Cursor
bash install.sh cursorFor Windsurf
bash install.sh windsurfCompatibility adapters: Claude Code / Gemini CLI
bash install.sh claude
bash install.sh geminiFull guide: GUIDE.md
Example Workflows
Each workflow is copyable into your agent. See examples/ for complete, verified examples:
3-minute quickstart — Fastest path to useful output
Research PDF to wiki — Turn a source into linked pages
Verified code session — Use verification gates before claiming a fix
Daily knowledge loop — Run a compact daily OKR workflow
Startup evaluation sprint — Turn an idea into validated assumptions
Wiki Structure
Skills default to STOW paths (configurable via system/config.md):
sources/ ← Immutable source notes (articles, PDFs, transcripts)
wiki/
├── concepts/ ← 13 contracted domains
├── entities/ ← people, companies, funds-investors, products, orgs
└── outputs/ ← gmail-digests, evaluations, compilations
maps/
├── domain-mocs/
├── system-indexes/
├── project-maps/
└── canvases/
system/ ← Deployed contracts, config, schema, templates, runsTools & References
| Resource | Purpose |
|---|---|
| tools/index.html | V8.1 visual skill navigator |
| tools/token-calculator.html | Historical V5 cost-model calculator; not a runtime authority |
| GUIDE.md | Full installation & troubleshooting |
| V7 最大潜力使用手册(历史参考) | Earlier profile、知识飞轮、Agent 工程、治理和 30 天采用路径 |
| CLAUDE.md | Claude Code setup |
| CONTRIBUTING.md | How to contribute skills |
What This Is NOT
Not a chat wrapper. Skills are executable prompts that agents follow; they're not productivity theater.
Not a productivity tool with 100 metrics. Daily OKR has seven causally linked KRs with evidence.
Not an all-in-one framework. Pick skills incrementally. You don't need all 20 to start.
Not prescriptive. Adapt paths, frontmatter, and skill triggers to your workflow.
Philosophy
Three core principles:
Verification first: No "done" without proof. No claims without evidence. Expected value over confidence.
Knowledge compounds: Every session should improve the wiki, not scatter across chat history.
Closed loops: Ingest → Process → Learn → Scale → Verify. No loose ends.
Related Work
Agent Skills Format — Open specification for agent skills
llm-wiki-agent — Original STOW pattern implementation by SamurAIGPT
Karpathy LLM OS — Conceptual framework
Contributing
Bug reports, skill improvements, and PRs are welcome. See CONTRIBUTING.md and CHANGELOG.md.
License
MIT — see LICENSE.
Transparency Note
This project includes growth and outreach tools (in tools/ and outreach/) designed to help the repository reach users via GitHub search and Awesome lists. These tools are optional, dry-run-by-default, and separate from the core skills. If you're installing skills for your own workflow, you don't need them.
The skill frameworks and philosophy are genuine. Use what works for you; ignore what doesn't.
tools/agentic_os_runtime.py
Shown as plain text.
"""Bounded, read-only Agentic OS primitives and host-owned action dispatch."""
from __future__ import annotations
import hashlib
import json
import os
import re
import subprocess
import sys
import uuid
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Iterable
SCHEMA_VERSION = "1.0"
MAX_AUDIT_BYTES = 200_000
MAX_TAIL = 4_000
_SHELL_MARKERS = re.compile(r"[;&|<>`$\x00\r\n]")
_MODE_VALUES = {"manual", "session", "interview"}
_ACTION_STATES = {"LIVE", "HOST_REQUIRED", "UNAVAILABLE"}
_EFFECTS = {"READ_ONLY", "STAGED_WRITE", "LIVE_COMMIT", "EXTERNAL"}
class AgenticOSRuntimeError(RuntimeError):
"""Base error for fail-closed runtime operations."""
class UnknownActionError(AgenticOSRuntimeError):
"""Raised before any process or receipt is created for an unknown action."""
class ActionBlockedError(AgenticOSRuntimeError):
"""Raised when a known action is not executable by this local host."""
def _iso_now() -> str:
return datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
def _sha256_bytes(data: bytes) -> str:
return hashlib.sha256(data).hexdigest()
def _canonical_hash(value: Any) -> str:
return _sha256_bytes(json.dumps(value, ensure_ascii=False, sort_keys=True, separators=(",", ":")).encode("utf-8"))
def _tail(value: bytes | str) -> str:
text = value.decode("utf-8", errors="replace") if isinstance(value, bytes) else str(value)
return text[-MAX_TAIL:]
def _json_object(path: Path) -> dict[str, Any]:
value = json.loads(path.read_text(encoding="utf-8"))
if not isinstance(value, dict):
raise ValueError(f"expected JSON object: {path}")
return value
def _parse_frontmatter(text: str) -> dict[str, str]:
if not text.startswith("---\n"):
return {}
end = text.find("\n---\n", 4)
if end < 0:
return {}
result: dict[str, str] = {}
parent = ""
for line in text[4:end].splitlines():
if not line.strip() or line.lstrip().startswith("#") or ":" not in line:
continue
key, value = line.split(":", 1)
key, value = key.strip(), value.strip().strip('"').strip("'")
if line[:1].isspace() and parent:
result[f"{parent}.{key}"] = value
else:
parent = key if not value else ""
result[key] = value
return result
def _repo(repo_root: Path | str | None) -> Path:
return Path(repo_root or Path(__file__).resolve().parents[1]).resolve()
def generate_skill_registry(repo_root: Path | str | None = None) -> dict[str, Any]:
"""Generate skill metadata from directories, never from a hard-coded count."""
root = _repo(repo_root)
skills_root = root / "skills"
if not skills_root.is_dir():
raise FileNotFoundError(f"missing skills directory: {skills_root}")
skills: list[dict[str, Any]] = []
for directory in sorted(path for path in skills_root.iterdir() if path.is_dir()):
skill_file = directory / "SKILL.md"
if not skill_file.is_file():
continue
data = _parse_frontmatter(skill_file.read_text(encoding="utf-8", errors="replace"))
skills.append(
{
"name": data.get("name", directory.name),
"description": data.get("description", ""),
"profile": data.get("metadata.profile", ""),
"version": data.get("metadata.version", ""),
"path": skill_file.relative_to(root).as_posix(),
"sha256": _sha256_bytes(skill_file.read_bytes()),
}
)
return {
"skills": skills,
"skill_count": len(skills),
"skill_registry_sha256": _canonical_hash(skills),
}
def _catalog(root: Path) -> dict[str, Any]:
return _json_object(root / "contracts" / "agentic-os" / "action-registry.json")
def _validate_action(action: dict[str, Any]) -> None:
required = {"id", "label", "description", "capability", "state", "effect", "approval_required", "command", "timeout_seconds", "verifier", "receipt_policy", "loop_contract"}
missing = sorted(required - set(action))
if missing:
raise ValueError(f"action {action.get('id', '<unknown>')} missing fields: {missing}")
if action["state"] not in _ACTION_STATES or action["effect"] not in _EFFECTS:
raise ValueError(f"invalid state/effect for action {action['id']}")
if action["receipt_policy"] != "required" or not isinstance(action["command"], list) or not action["command"]:
raise ValueError(f"action {action['id']} must have a receipt and argv")
for argv_name in ("command", "verifier"):
argv = action[argv_name]
if not isinstance(argv, list) or not argv or any(not isinstance(token, str) for token in argv):
raise ValueError(f"action {action['id']} has invalid {argv_name}")
if any(_SHELL_MARKERS.search(token) for token in argv):
raise ValueError(f"action {action['id']} contains shell syntax")
def build_action_registry(repo_root: Path | str | None = None, vault_root: Path | str | None = None) -> dict[str, Any]:
"""Return the static catalog enriched with repository-derived skills and host state."""
root = _repo(repo_root)
catalog = _catalog(root)
actions = json.loads(json.dumps(catalog.get("actions", [])))
configured_vault = bool(vault_root and Path(vault_root).expanduser().resolve().is_dir())
for action in actions:
_validate_action(action)
if action["capability"] in {"inventory", "freshness"}:
action["state"] = "LIVE" if configured_vault else "HOST_REQUIRED"
skills = generate_skill_registry(root)
return {"schema_version": SCHEMA_VERSION, **skills, "actions": actions}
def _read_source(source: str | Path | dict[str, Any]) -> tuple[str, str, str]:
if isinstance(source, dict):
raw = json.dumps(source, ensure_ascii=False, sort_keys=True)
return raw, "json-object", _sha256_bytes(raw.encode("utf-8"))
candidate = Path(source) if isinstance(source, (str, Path)) else None
if candidate is not None:
try:
is_file = candidate.is_file()
except OSError:
is_file = False
if is_file:
data = candidate.read_bytes()
if len(data) > MAX_AUDIT_BYTES:
raise ValueError(f"workflow audit input exceeds {MAX_AUDIT_BYTES} bytes")
return data.decode("utf-8", errors="replace"), candidate.suffix.lower().lstrip(".") or "text", _sha256_bytes(data)
raw = str(source)
data = raw.encode("utf-8")
if len(data) > MAX_AUDIT_BYTES:
raise ValueError(f"workflow audit input exceeds {MAX_AUDIT_BYTES} bytes")
return raw, "text", _sha256_bytes(data)
def _flatten_json(value: Any, prefix: str = "") -> Iterable[str]:
if isinstance(value, str):
if value.strip():
yield value.strip()
elif isinstance(value, list):
for item in value:
yield from _flatten_json(item, prefix)
elif isinstance(value, dict):
for key, item in value.items():
if str(key).casefold() in {"tasks", "steps", "workflow", "repeated_tasks", "answers", "notes", "history"}:
yield from _flatten_json(item, str(key))
def audit_workflow(source: str | Path | dict[str, Any], mode: str | None = None, max_candidates: int = 25) -> dict[str, Any]:
"""Audit supplied evidence and make bounded supervised promotion decisions."""
if max_candidates <= 0 or max_candidates > 100:
raise ValueError("max_candidates must be between 1 and 100")
raw, source_kind, source_hash = _read_source(source)
parsed: Any = None
if source_kind in {"json", "json-object"} or raw.lstrip().startswith(("{", "[")):
try:
parsed = json.loads(raw)
except json.JSONDecodeError:
parsed = None
requested_mode = mode.casefold() if mode else ""
if requested_mode and requested_mode not in _MODE_VALUES:
raise ValueError(f"mode must be one of {sorted(_MODE_VALUES)}")
lower = raw.casefold()
inferred = requested_mode or ("interview" if any(word in lower for word in ("interviewer", "question:", "answer:")) else "session" if any(word in lower for word in ("session", "run history", "previous run")) else "manual")
audit_body = raw
if parsed is None and raw.lstrip().startswith("---"):
lines = raw.splitlines()
first = next((index for index, line in enumerate(lines) if line.strip()), None)
if first is not None and lines[first].strip() == "---":
closing = next((index for index in range(first + 1, len(lines)) if lines[index].strip() == "---"), None)
if closing is not None:
audit_body = "\n".join(lines[closing + 1 :])
candidates: list[str] = []
if parsed is not None:
candidates.extend(_flatten_json(parsed))
if not candidates:
for line in audit_body.splitlines():
stripped = re.sub(r"^\s*(?:[-*+]\s+|\d+[.)]\s+|#+\s*)", "", line).strip()
stripped = re.sub(r"^\*\*\d{1,2}:\d{2}\*\*\s*[·:-]?\s*", "", stripped).strip()
if (
len(stripped) >= 12
and not stripped.lower().startswith(("http://", "https://"))
and not re.match(r"^!?\[[^]]*\]\(https?://", stripped, re.I)
and not stripped.startswith("#")
):
candidates.append(stripped)
unique: list[str] = []
seen: set[str] = set()
for item in candidates:
normalized = re.sub(r"\s+", " ", item).strip()
key = normalized.casefold()
if key not in seen:
seen.add(key)
unique.append(normalized[:500])
priority_re = re.compile(
r"\b(?:workflow audit|workflow|skill architecture|skill|automation|automate|"
r"loop engineering|loop|memory|state management|state control|interface|ui|"
r"distribution|package|repeat|recurr|schedule|command|button|database|previous run)\w*\b",
re.I,
)
ranked = [
(len(priority_re.findall(item)), index, item)
for index, item in enumerate(unique)
]
if any(score for score, _, _ in ranked):
ranked.sort(key=lambda value: (-value[0], value[1]))
unique = [item for _, _, item in ranked]
unique = unique[:max_candidates]
repeat_re = re.compile(r"\b(?:every|each|daily|weekly|monthly|recurr|repeat|again|routine|always|通常|每次|每天|每周|反复)\w*\b", re.I)
loop_re = re.compile(r"\b(?:retry|monitor|poll|schedule|follow[- ]?up|until|loop|recurr|再次|循环|监控|定期)\w*\b", re.I)
skill_re = re.compile(r"\b(?:workflow|skill|procedure|process|playbook|sop|工作流|技能|流程)\w*\b", re.I)
automation_re = re.compile(r"\b(?:automation|automate|schedule|recurr|button|voice command|自动化|定期|按钮|语音命令)\w*\b", re.I)
records: list[dict[str, Any]] = []
for item in unique:
repeated = bool(repeat_re.search(item))
loop_candidate = bool(loop_re.search(item))
multi_step = bool(re.search(r"(?:\bthen\b|\bnext\b|\bafter\b|\d+[.)]|->|;|;)", item, re.I))
records.append({
"evidence": item,
"repeat_signal": repeated,
"skill_decision": "SKILL_CANDIDATE" if repeated or multi_step or skill_re.search(item) else "KEEP_MANUAL",
"automation_decision": "AUTOMATION_CANDIDATE" if repeated or automation_re.search(item) else "HOST_REQUIRED_REVIEW",
"loop_decision": "LOOP_CANDIDATE" if loop_candidate else "NO_LOOP",
"approval": "SUPERVISED_PROMOTION_REQUIRED",
"owner": "OPERATOR_REVIEW_REQUIRED",
"verifier": "DEFINE_BEFORE_PROMOTION",
"stop_condition": "NO_AUTOMATIC_PROMOTION",
"effect": "READ_ONLY_AUDIT",
})
return {
"status": "AUDITED" if records else "INSUFFICIENT_EVIDENCE",
"mode": inferred,
"source": {"kind": source_kind, "sha256": source_hash, "bytes": len(raw.encode("utf-8"))},
"bounds": {"max_bytes": MAX_AUDIT_BYTES, "max_candidates": max_candidates, "candidate_count": len(records)},
"candidates": records,
"recommendations": {
"skill_candidates": sum(item["skill_decision"] == "SKILL_CANDIDATE" for item in records),
"automation_candidates": sum(item["automation_decision"] == "AUTOMATION_CANDIDATE" for item in records),
"loop_candidates": sum(item["loop_decision"] == "LOOP_CANDIDATE" for item in records),
"policy": "Promotion is receipt-backed and supervised; this audit never mutates skills or schedules.",
},
"evidence": [{"source_sha256": source_hash, "mode": inferred, "candidate_count": len(records)}],
"side_effect_count": 0,
}
def _latest_run(vault: Path) -> dict[str, Any] | None:
manifests = sorted(
vault.glob("system/runs/*/run-*/manifest.json"),
key=lambda path: path.stat().st_mtime_ns,
reverse=True,
)
if not manifests:
return None
path = manifests[0]
try:
value = _json_object(path)
return {"path": path.relative_to(vault).as_posix(), "run_id": value.get("run_id"), "status": value.get("status")}
except (OSError, ValueError, json.JSONDecodeError):
return {"path": path.relative_to(vault).as_posix(), "status": "UNREADABLE"}
def _run_lint(root: Path) -> dict[str, Any]:
command = [sys.executable, str(root / "tools" / "lint-agent-skills.py")]
try:
result = subprocess.run(command, cwd=str(root), capture_output=True, timeout=30, check=False, shell=False)
return {"status": "PASS" if result.returncode == 0 else "FAIL", "exit_code": result.returncode, "stdout_tail": _tail(result.stdout), "stderr_tail": _tail(result.stderr)}
except (OSError, subprocess.TimeoutExpired) as exc:
return {"status": "ERROR", "exit_code": None, "error": str(exc)}
def build_snapshot(repo_root: Path | str | None = None, vault_root: Path | str | None = None, limit: int = 100) -> dict[str, Any]:
"""Build a read-only repository/Vault snapshot; absent Vault data stays absent."""
root = _repo(repo_root)
if limit <= 0 or limit > 1000:
raise ValueError("limit must be between 1 and 1000")
registry = build_action_registry(root, vault_root)
lint = _run_lint(root)
contract_path = root / "contracts" / "vault-contract.json"
contract_version = "unknown"
try:
contract_version = str(_json_object(contract_path).get("contract_version") or "unknown")
except (OSError, ValueError, json.JSONDecodeError):
lint = {**lint, "status": "ERROR", "error": "vault contract is unreadable"}
vault: dict[str, Any]
evidence: list[Any] = [{"kind": "skill-lint", **lint}, {"kind": "skill-registry", "sha256": registry["skill_registry_sha256"]}]
configured = bool(vault_root)
vault_path = Path(vault_root).expanduser().resolve() if vault_root else None
if not configured or vault_path is None or not vault_path.is_dir():
vault = {"configured": configured, "fingerprint": "", "counts": None, "version_counts": None, "debt": None, "freshness": None, "latest_run": None}
evidence.append({"kind": "vault", "status": "MISSING_ACCESS", "configured": configured})
status = "PARTIAL" if lint.get("status") == "PASS" else "ERROR"
else:
try:
from tools.worker_flow.runtime import WorkerFlowRuntime
runtime = WorkerFlowRuntime(vault_path, repo_root=root)
inventory = runtime.inventory(limit=limit)
freshness = runtime.freshness_scan(limit=limit)
vault = {"configured": True, "fingerprint": runtime.vault_fingerprint, "counts": inventory.get("counts"), "version_counts": inventory.get("version_counts"), "debt": {"inventory": inventory.get("sources"), "maps": inventory.get("maps"), "system": inventory.get("system")}, "freshness": freshness, "latest_run": _latest_run(vault_path)}
evidence.append({"kind": "vault-inventory", "status": inventory.get("status"), "side_effect_count": inventory.get("side_effect_count", 0)})
evidence.append({"kind": "vault-freshness", "status": freshness.get("status"), "side_effect_count": freshness.get("side_effect_count", 0)})
status = "READY" if lint.get("status") == "PASS" else "PARTIAL"
except (OSError, ValueError, RuntimeError) as exc:
vault = {"configured": True, "fingerprint": "", "counts": None, "version_counts": None, "debt": None, "freshness": None, "latest_run": None}
evidence.append({"kind": "vault", "status": "ERROR", "error": str(exc)})
status = "PARTIAL"
return {"schema_version": SCHEMA_VERSION, "generated_at": _iso_now(), "status": status, "repository": {"contract_version": contract_version, "skill_count": registry["skill_count"], "skill_registry_sha256": registry["skill_registry_sha256"], "verification": {"skill_lint": lint}}, "vault": vault, "actions": registry["actions"], "evidence": evidence, "side_effect_count": 0}
def _resolve_token(token: str, root: Path, vault: Path | None) -> str:
if token == "{python}":
return sys.executable
if token == "{repo_root}":
return str(root)
if token == "{vault_root}":
if vault is None:
raise ActionBlockedError("Vault action requires an explicitly configured Vault")
return str(vault)
if token == "{unavailable}":
raise ActionBlockedError("action is not available on this host")
if "{" in token or "}" in token:
raise AgenticOSRuntimeError("unknown host token in registry")
return token
def _resolve_argv(argv: list[str], root: Path, vault: Path | None) -> list[str]:
resolved = [_resolve_token(token, root, vault) for token in argv]
# Registry paths are relative to the repository. No caller string is ever appended.
for index, token in enumerate(resolved):
if token in {sys.executable, str(root), str(vault) if vault else ""} or token.startswith("-"):
continue
if index == 0:
continue
if not Path(token).is_absolute() and ("/" in token or "\\" in token or token.endswith((".py", ".json", ".md"))):
candidate = (root / token).resolve()
try:
candidate.relative_to(root)
except ValueError as exc:
raise AgenticOSRuntimeError(f"registry path escapes repository: {token}") from exc
if not candidate.exists():
raise AgenticOSRuntimeError(f"registry path drift: {token}")
resolved[index] = str(candidate)
return resolved
def _write_json(path: Path, value: dict[str, Any]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
temporary = path.with_name(f".{path.name}.{uuid.uuid4().hex}.tmp")
temporary.write_text(json.dumps(value, ensure_ascii=False, indent=2, sort_keys=True) + "\n", encoding="utf-8")
os.replace(temporary, path)
def execute_action(action_id: str, state_root: Path | str, repo_root: Path | str | None = None, vault_root: Path | str | None = None) -> dict[str, Any]:
"""Execute one catalog action with literal argv and write its typed receipt."""
if not action_id or not isinstance(action_id, str):
raise UnknownActionError("action id must be a non-empty string")
root = _repo(repo_root)
registry = build_action_registry(root, vault_root)
actions = {action["id"]: action for action in registry["actions"]}
if action_id not in actions:
raise UnknownActionError(f"unknown action: {action_id}")
action = actions[action_id]
state = Path(state_root).expanduser().resolve() if state_root else None
if state is None:
raise ValueError("state_root is required; runtime has no implicit state path")
vault = Path(vault_root).expanduser().resolve() if vault_root else None
execution_id = uuid.uuid4().hex
started = _iso_now()
receipt: dict[str, Any] = {"schema_version": SCHEMA_VERSION, "action_id": action_id, "execution_id": execution_id, "state": "RUNNING", "started_at": started, "completed_at": None, "resolved_argv": [], "exit_code": None, "stdout_tail": "", "stderr_tail": "", "verifier": {"argv": [], "exit_code": None, "result": "NOT_RUN"}, "evidence": [], "side_effect_count": 0}
receipt_path = state / "receipts" / f"{execution_id}.json"
try:
if action["state"] != "LIVE" or action["approval_required"] or action["effect"] != "READ_ONLY":
raise ActionBlockedError(f"action {action_id} is {action['state']} and cannot run in the local read-only host")
command = _resolve_argv(action["command"], root, vault)
verifier = _resolve_argv(action["verifier"], root, vault)
receipt["resolved_argv"] = command
receipt["verifier"]["argv"] = verifier
result = subprocess.run(command, cwd=str(root), capture_output=True, timeout=int(action["timeout_seconds"]), check=False, shell=False)
receipt["exit_code"] = result.returncode
receipt["stdout_tail"] = _tail(result.stdout)
receipt["stderr_tail"] = _tail(result.stderr)
if result.returncode == 0:
checked = subprocess.run(verifier, cwd=str(root), capture_output=True, timeout=int(action["timeout_seconds"]), check=False, shell=False)
receipt["verifier"]["exit_code"] = checked.returncode
receipt["verifier"]["result"] = "PASS" if checked.returncode == 0 else "FAIL"
receipt["evidence"].append({"command_exit_code": result.returncode, "verifier_exit_code": checked.returncode})
receipt["state"] = "SUCCEEDED" if checked.returncode == 0 else "FAILED"
else:
receipt["state"] = "FAILED"
receipt["evidence"].append({"command_exit_code": result.returncode, "verifier": "NOT_RUN"})
except ActionBlockedError as exc:
receipt["state"] = "BLOCKED"
receipt["evidence"].append({"reason": str(exc)})
except (OSError, subprocess.TimeoutExpired, AgenticOSRuntimeError, ValueError) as exc:
receipt["state"] = "FAILED"
receipt["evidence"].append({"reason": str(exc)})
receipt["completed_at"] = _iso_now()
_write_json(receipt_path, receipt)
receipt["receipt_path"] = receipt_path.as_posix()
return receipt
def verify_receipt(receipt: dict[str, Any]) -> tuple[bool, list[str]]:
"""Cheap independent receipt guard used by tests and host integrations."""
errors: list[str] = []
required = ("schema_version", "action_id", "execution_id", "state", "started_at", "completed_at", "resolved_argv", "verifier", "side_effect_count")
for field in required:
if field not in receipt:
errors.append(f"missing {field}")
if receipt.get("schema_version") != SCHEMA_VERSION:
errors.append("schema_version mismatch")
if receipt.get("state") == "SUCCEEDED":
if receipt.get("exit_code") != 0:
errors.append("succeeded receipt has nonzero exit code")
if receipt.get("verifier", {}).get("result") != "PASS":
errors.append("succeeded receipt lacks verifier PASS")
if receipt.get("side_effect_count") != 0:
errors.append("side_effect_count must be zero")
return not errors, errors
318 files · 7 not served here
- .gitattributes
- .github/ISSUE_TEMPLATE/bug_report.yml
- .github/ISSUE_TEMPLATE/config.yml
- .github/ISSUE_TEMPLATE/skill_request.yml
- .github/PULL_REQUEST_TEMPLATE.md
- .github/workflows/quality.yml
- .gitignore
- AGENTS.md
- ATTRIBUTION.md
- AUTOGINEER-RUNS.md
- CHANGELOG.md
- CLAUDE.md
- CONTRIBUTING.md
- FINAL_VERIFICATION_RECEIPT.md
- GEMINI.md
- GUIDE.md
- LICENSE
- README.md
- SECURITY.md
- SUBSTANCE.md
- adapters/cursor/third-brain-skills.mdc
- adapters/windsurf/third-brain-skills.md
- artifacts/agent-team/agentic-os-demo-alignment-20260820/command-board.md
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- commands/daily-okr.md
- commands/session-learn.md
- commands/token-cost-tracker.md
- commands/wiki-lint.md
- contracts/agentic-os/action-receipt.schema.json
- contracts/agentic-os/action-registry.json
- contracts/agentic-os/registry.schema.json
- contracts/agentic-os/snapshot.schema.json
- contracts/automation-loop-contract.md
- contracts/freshness-policy.json
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- core/ooda/act.js
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- core/ooda/index.js
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- dashboard.html
- docs/agent-skills-standard.md
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- docs/agentic-os-command-center.md
- docs/community-discovery.md
- docs/compatibility.md
- docs/guides/concept-gold-standard-v8.1-authoring-guide.md
- docs/live-vault-v8.1-baseline-2026-08-16.md
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- examples/3-minute-quickstart.md
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- system/daily-knowledge-loop.md
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- system/project-portfolio.md
- system/review-queue.md
- system/run-history-index.md
- system/schema.md
- system/scripts/run-worker-flow-v8.1.ps1
- system/system-evolution-backlog.md
- system/tag-taxonomy.md
- system/templates/template-concept-gold-standard-v8.1.md
- system/templates/template-concept-gold-standard.md
- system/templates/template-output-deliverable-v8.1.md
- system/templates/template-output-deliverable.md
- system/templates/template-source-v8.1.md
- system/templates/template-source-v8.md
- system/templates/template-worker-task.md
- system/templates/vault-agent-navigation-v8.1.md
- system/v5-architecture-evaluation.md
- system/vault-navigation.md
- system/workflow-registry.md
- tests/__init__.py
- tests/test_agentic_os_command_center.py
- tests/test_agentic_os_navigation.py
- tests/test_agentic_os_runtime.py
- tests/test_template_contracts.py
- tests/test_worker_flow_transaction_safety.py
- tests/test_worker_flow_v81.py
- tests/test_worker_flow_v81_deployment.py
- tools/adapt_skills_to_vault.py
- tools/agentic_os.py
- tools/agentic_os_runtime.py
- tools/agentic_os_server.py
- tools/architecture-v4.html
- tools/auto_heal_vault.py
- tools/index.html
- tools/install_skills.py
- tools/legacy_compat.py
- tools/lint-agent-skills.py
- tools/multi_agent_vault_team.py
- tools/package_agentic_os.py
- tools/pipeline-cost-calculator.html
- tools/run_30min_clippings_pipeline.py
- tools/sync_vault_codex.py
- tools/test_adapt_skills_to_vault.py
- tools/test_install_ps1.py
- tools/test_install_sh.py
- tools/test_install_skills.py
- tools/test_lint_agent_skills.py
- tools/test_multi_agent_vault_team.py
- tools/test_promoted_skill_contracts.py
- tools/test_repository_governance.py
- tools/test_run_30min_clippings_pipeline.py
- tools/test_validate_graph_contract.py
- tools/test_validate_loop_contract.py
- tools/test_validate_runtime_envelope.py
- tools/test_worker_flow_engine.py
- tools/token-calculator.html
- tools/upgrade_skills_v81.py
- tools/v5.1-simplified.html
- tools/worker_flow/__init__.py
- tools/worker_flow/__main__.py
- tools/worker_flow/cli.py
- tools/worker_flow/contracts.py
- tools/worker_flow/deployment.py
- tools/worker_flow/freshness.py
- tools/worker_flow/frontmatter.py
- tools/worker_flow/governance.py
- tools/worker_flow/graph.py
- tools/worker_flow/ingest.py
- tools/worker_flow/inventory.py
- tools/worker_flow/locking.py
- tools/worker_flow/runtime.py
- tools/worker_flow/schema.py
- tools/worker_flow/state.py
- tools/worker_flow/template_render.py
- tools/worker_flow/transaction.py
- tools/worker_flow/utils.py
- tools/worker_flow/wikilinks.py
- tools/worker_flow_engine.py
- workflows/worker-flows.md
Advantages
- Enforces verification gates to prevent agents from making unverified claims. evidence: README.md
- Provides a durable, structured knowledge base that compounds over time. evidence: README.md
- Supports multi-agent orchestration with typed IPC and isolated write scopes. evidence: README.md
Limitations
- Does not support dynamic or cyclic graphs; graph-engineering is bounded to static DAGs. evidence: README.md
- Requires a specific transactional runtime for full functionality. evidence: AGENTS.md
Security
Risk level: Low risk
This brain runs
Agent instructions that run or fetch something (88)
AGENTS.md:V8.1 defines an explicit **5-Stage Worker Assembly Pipeline** in `workflows/worker-flows.md`, enforced by the canonical transactional runtime `python -m tools.worker_flow.cli`.AGENTS.md:**loop-engineering** — Temporal-depth control through bounded Trigger → Execute → Verify → State loops, durable contracts, hard budgets, and stop/recovery rules.AGENTS.md:`## Success Metrics` — minimum observable result for one successful run.GEMINI.md:V8.1 defines a transactional **5-Stage Worker Assembly Pipeline** (`workflows/worker-flows.md`) enforced by `python -m tools.worker_flow.cli`.skills/agent-teams-command/SKILL.md:<execute>skills/agent-teams-command/SKILL.md:Run regression tests.skills/agent-teams-command/SKILL.md:</execute>skills/agentic-engineering/SKILL.md:Run the adoption gate: use an agent only when ambiguity/adaptation outweigh orchestration, verification, and maintenance cost.skills/agentic-engineering/SKILL.md:<execute>skills/agentic-engineering/SKILL.md:only host code may execute tools or decide continuation.skills/agentic-engineering/SKILL.md:Run a thin loop: understand -> plan -> smallest change -> targeted test -> inspect diff/state -> broader check.skills/agentic-engineering/SKILL.md:</execute>skills/ai-six-sigma-property-os/SKILL.md:bounded agents execute and audit;skills/ai-six-sigma-property-os/SKILL.md:<execute>skills/ai-six-sigma-property-os/SKILL.md:</execute>skills/anthropic-os/SKILL.md:Run Four-C in order: **Context** (truth/history), **Connections** (systems/accounts), **Capabilities** (skills/SOPs/evals), **Cadence** (triggers/reviews).skills/anthropic-os/SKILL.md:<execute>skills/anthropic-os/SKILL.md:Run dual prediction when useful: human and agent predict outcome independently;skills/anthropic-os/SKILL.md:</execute>skills/behavior-design/SKILL.md:<execute>skills/behavior-design/SKILL.md:**H3 SOP:** write `trigger -> action -> evidence -> recovery -> stop` so a missed run has a next move.skills/behavior-design/SKILL.md:</execute>skills/behavior-design/SKILL.md:run a short observation period before optimizing.skills/cognitive-compile/SKILL.md:<execute>skills/cognitive-compile/SKILL.md:</execute>skills/context-manager/SKILL.md:<execute>skills/context-manager/SKILL.md:</execute>skills/creativity-engine/SKILL.md:<execute>skills/creativity-engine/SKILL.md:</execute>skills/daily-okr/SKILL.md:Run one daily objective through seven linked Key Results.skills/daily-okr/SKILL.md:<execute>skills/daily-okr/SKILL.md:**Behavior:** execute one minimum behavior linked to the objective.skills/daily-okr/SKILL.md:</execute>skills/deep-research/SKILL.md:<execute>skills/deep-research/SKILL.md:Run a broad discovery pass;skills/deep-research/SKILL.md:Run a gap-fill pass only for decision-critical unknowns.skills/deep-research/SKILL.md:Run an adversarial pass: strongest counterevidence, alternative mechanism, stale data, selection bias, and missing base rate.skills/deep-research/SKILL.md:</execute>skills/graph-engineering/SKILL.md:Run the admission gate before drawing a graph:skills/graph-engineering/SKILL.md:Probe candidate independence with a small dry run.skills/graph-engineering/SKILL.md:<execute>skills/graph-engineering/SKILL.md:Write the JSON contract and run `scripts/validate_graph_contract.py <contract.json> --strict`.skills/graph-engineering/SKILL.md:node success alone cannot certify graph success.skills/graph-engineering/SKILL.md:</execute>skills/graph-engineering/SKILL.md:run only independent ready nodes.skills/harness-engineering/SKILL.md:Run the trace gate: the harness must be able to show what the agent saw, proposed, called, changed, and verified.skills/harness-engineering/SKILL.md:<execute>skills/harness-engineering/SKILL.md:The host decides whether to execute, continue, checkpoint, or escalate;skills/harness-engineering/SKILL.md:</execute>skills/harness-engineering/SKILL.md:Verify permissions with actual environment boundaries and run an independent end-to-end trace.skills/harness-engineering/SKILL.md:A model emits plausible success text with a tool request: validate and execute the tool in host code, then verify its result;skills/harness-engineering/SKILL.md:A maintenance run finds no eligible change: emit a verified `NO_OP` receipt and zero external outputs rather than creating notification or PR noise.skills/harness-engineering/SKILL.md:The scheduler ran on time but the state store was stale: block mutation, mark the run failed, and recover from the last verified checkpoint.skills/harness-engineering/SKILL.md:A Graph worker completes twice after lease expiry: deduplicate by node/run identity and release successors only from one verified transition.skills/knowledge-ops/SKILL.md:<execute>skills/knowledge-ops/SKILL.md:**Verify:** read after write, check links/source refs, rerun exact-identity search, and run representative retrieval queries.skills/knowledge-ops/SKILL.md:Use idempotency keys for machine append operations: derive the key from operation, canonical source identity, target, and run/batch id;skills/knowledge-ops/SKILL.md:</execute>skills/knowledge-ops/SKILL.md:make no whole-vault claim.skills/loop-engineering/SKILL.md:<skill_contract> <input>A repeatable task with inspectable state, finite budgets, permissions, and an independent verifier.</input> <output>A validated Trigger -> Execute -> Verify -> State contract plus resumable run receipts.</output> <done>The declared metric or stop condition is su …[+161 chars]skills/loop-engineering/SKILL.md:**Goal:** run until a defined end state or cap.skills/loop-engineering/SKILL.md:<execute>skills/loop-engineering/SKILL.md:**Act:** execute one bounded action and capture artifact/diff/receipt.skills/loop-engineering/SKILL.md:</execute>skills/loop-engineering/SKILL.md:A scheduled job fired but produced no run receipt: status is triggered, not completed;skills/project-flow-ops/SKILL.md:<execute>skills/project-flow-ops/SKILL.md:Run the verification named in the definition of done.skills/project-flow-ops/SKILL.md:</execute>skills/session-learn/SKILL.md:<execute>skills/session-learn/SKILL.md:**Pattern** — a reusable Trigger -> Execute -> Verify -> State sequence.skills/session-learn/SKILL.md:</execute>skills/startup-evaluation/SKILL.md:<execute>skills/startup-evaluation/SKILL.md:</execute>skills/verify-before-claim/SKILL.md:<execute>skills/verify-before-claim/SKILL.md:Run the selected check after the final material change.skills/verify-before-claim/SKILL.md:</execute>skills/wiki-ingest/SKILL.md:record the identity query in the run ledger.skills/wiki-ingest/SKILL.md:<execute>skills/wiki-ingest/SKILL.md:Give every machine log entry an idempotency key derived from run/batch, operation, canonical source identity, and target;skills/wiki-ingest/SKILL.md:**Verify:** run targeted link, source-ref, frontmatter, block-ref, empty-file, navigation, and post-write exact-identity checks over touched files.skills/wiki-ingest/SKILL.md:</execute>skills/wiki-lint/SKILL.md:make no content mutation unless separately authorized.skills/wiki-lint/SKILL.md:<execute>skills/wiki-lint/SKILL.md:Run twelve checks and preserve file-level evidence:skills/wiki-lint/SKILL.md:</execute>skills/wiki-lint/SKILL.md:If repair was authorized, run the relevant check again after each bounded repair.skills/workflow-audit/SKILL.md:<execute> Extract a bounded list of distinct work candidates.skills/workflow-audit/SKILL.md:</execute>
Install scripts (2)
install.ps1:pwsh install.ps1install.sh:sh install.sh
- Low risk: scripts-present (install.ps1)
- Low risk: scripts-present (install.sh)
Attribution and licence
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Mark393295827
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- Markk-gao (Community)
- Source
- Mark393295827/third-brain-v7-skills · commit 5a64514
- Licence
- MIT License (files served here)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Contributors
- Mark393295827
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Install from source
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