CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

30.8 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 1 flagged

Securityscore 88 · 1 finding

MEDIUMInstruction telling the agent not to ask for approvalSS-SKILL-INJECT-DONT-ASK-01 · Prompt injection · skills/loki-mode/SKILL.md

MEDIUM — it fires on intent; the real damage depends on the host agent's own approval-gating.

Why it matters

The text {match} tells the agent to skip the normal "ask the user first" gate. Used adversarially it removes the human-in-the-loop check before destructive or sensitive actions, turning a normally-gated agent into a fire-and-forget executor.

The exact value spotted

excerptskills/loki-mode/SKILL.md· markdown

109**This system runs with ZERO human intervention.**

110

1111. **NEVER ask questions** - No "Would you like me to...", "Should I...", or "What would you

… (9 chars elided on L111)

1122. **NEVER wait for confirmation** - Take immediate action

1133. **NEVER stop voluntarily** - Continue until completion promise fulfilled

Occurrences

1 occurrence · at L111

How to fix

Remove the approval-skipping instruction, or scope it narrowly to a specific safe, reversible action.

Delete blanket "don't ask / no need to confirm" directives from the skill.
If the skill is a genuine autonomous job, restrict the opt-out to a named non-destructive action rather than all actions.

Framework references

OWASPLLM01 ATLASAML.T0051

Trace & refs

ruleSS-SKILL-INJECT-DONT-ASK-01sha2564e0873a3c77e8cc3…rubric 365aacaView on GitHub

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

skills/loki-mode/SKILL.md · 1 file

skills/loki-mode/SKILL.md5.3 KB · Markdown

name: loki-mode

description: Version 2.35.0 | PRD to Production | Zero Human Intervention > Research-enhanced: OpenAI SDK, DeepMind, Anthropic, AWS Bedrock, Agent SDK, HN Production (2025)

category: AI & Agents

source: antigravity

tags: [python, markdown, api, mcp, claude, ai, agent, llm, automation, workflow]

url: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/loki-mode

Loki Mode - Multi-Agent Autonomous Startup System

Version 2.35.0 | PRD to Production | Zero Human Intervention Research-enhanced: OpenAI SDK, DeepMind, Anthropic, AWS Bedrock, Agent SDK, HN Production (2025)

Quick Reference

Critical First Steps (Every Turn)

READ .loki/CONTINUITY.md - Your working memory + "Mistakes & Learnings"
RETRIEVE Relevant memories from .loki/memory/ (episodic patterns, anti-patterns)
CHECK .loki/state/orchestrator.json - Current phase/metrics
REVIEW .loki/queue/pending.json - Next tasks
FOLLOW RARV cycle: REASON, ACT, REFLECT, VERIFY (test your work!)
OPTIMIZE Opus=planning, Sonnet=development, Haiku=unit tests/monitoring - 10+ Haiku agents in parallel
TRACK Efficiency metrics: tokens, time, agent count per task
CONSOLIDATE After task: Update episodic memory, extract patterns to semantic memory

Key Files (Priority Order)

File	Purpose	Update When
`.loki/CONTINUITY.md`	Working memory - what am I doing NOW?	Every turn
`.loki/memory/semantic/`	Generalized patterns & anti-patterns	After task completion
`.loki/memory/episodic/`	Specific interaction traces	After each action
`.loki/metrics/efficiency/`	Task efficiency scores & rewards	After each task
`.loki/specs/openapi.yaml`	API spec - source of truth	Architecture changes
`CLAUDE.md`	Project context - arch & patterns	Significant changes
`.loki/queue/*.json`	Task states	Every task change

Decision Tree: What To Do Next?

START
  |
  +-- Read CONTINUITY.md ----------+
  |                                |
  +-- Task in-progress?            |
  |   +-- YES: Resume              |
  |   +-- NO: Check pending queue  |
  |                                |
  +-- Pending tasks?               |
  |   +-- YES: Claim highest priority
  |   +-- NO: Check phase completion
  |                                |
  +-- Phase done?                  |
  |   +-- YES: Advance to next phase
  |   +-- NO: Generate tasks for phase
  |                                |
LOOP <-----------------------------+

SDLC Phase Flow

Bootstrap -> Discovery -> Architecture -> Infrastructure
     |           |            |              |
  (Setup)   (Analyze PRD)  (Design)    (Cloud/DB Setup)
                                             |
Development <- QA <- Deployment <- Business Ops <- Growth Loop
     |         |         |            |            |
 (Build)    (Test)   (Release)    (Monitor)    (Iterate)

Essential Patterns

Spec-First: OpenAPI -> Tests -> Code -> Validate Code Review: Blind Review (parallel) -> Debate (if disagree) -> Devil's Advocate -> Merge Guardrails: Input Guard (BLOCK) -> Execute -> Output Guard (VALIDATE) (OpenAI SDK) Tripwires: Validation fails -> Halt execution -> Escalate or retry Fallbacks: Try primary -> Model fallback -> Workflow fallback -> Human escalation Explore-Plan-Code: Research files -> Create plan (NO CODE) -> Execute plan (Anthropic) Self-Verification: Code -> Test -> Fail -> Learn -> Update CONTINUITY.md -> Retry Constitutional Self-Critique: Generate -> Critique against principles -> Revise (Anthropic) Memory Consolidation: Episodic (trace) -> Pattern Extraction -> Semantic (knowledge) Hierarchical Reasoning: High-level planner -> Skill selection -> Local executor (DeepMind) Tool Orchestration: Classify Complexity -> Select Agents -> Track Efficiency -> Reward Learning Debate Verification: Proponent defends -> Opponent challenges -> Synthesize (DeepMind) Handoff Callbacks: on_handoff -> Pre-fetch context -> Transfer with data (OpenAI SDK) Narrow Scope: 3-5 steps max -> Human review -> Continue (HN Production) Context Curation: Manual selection -> Focused context -> Fresh per task (HN Production) Deterministic Validation: LLM output -> Rule-based checks -> Retry or approve (HN Production) Routing Mode: Simple task -> Direct dispatch | Complex task -> Supervisor orchestration (AWS Bedrock) E2E Browser Testing: Playwright MCP -> Automate browser -> Verify UI features visually (Anthropic Harness)

Prerequisites

# Launch with autonomous permissions
claude --dangerously-skip-permissions

Core Autonomy Rules

This system runs with ZERO human intervention.

NEVER ask questions - No "Would you like me to...", "Should I...", or "What would you prefer?"
NEVER wait for confirmation - Take immediate action
NEVER stop voluntarily - Continue until completion promise fulfilled
NEVER suggest alternatives - Pick best option and execute
ALWAYS use RARV cycle - Every action follows Reason-Act-Reflect-Verify
NEVER edit `autonomy/run.sh` while running - Editing a running bash script corrupts execution (bash reads incrementally, not all at once). If you need

loki-mode— agent skill

Is loki-mode safe to install?

Loki Mode - Multi-Agent Autonomous Startup System

Quick Reference

Critical First Steps (Every Turn)

Key Files (Priority Order)

Decision Tree: What To Do Next?

SDLC Phase Flow

Essential Patterns

Prerequisites

Core Autonomy Rules

Audit the pieces. Scan the whole. Decide.