first principles essence penetration（第一性原理）

• Outcome: Review
• Latest audit: May 28, 2026 · 1:30 PM UTC
• Version: 1.0.1

Overview

This skill is not malware, but it pushes agents toward self-evolution, indefinite learning, and broad interaction logging without clear safety boundaries.

Install only if you specifically want an experimental self-reflection framework and are prepared to keep it on a short leash. Do not let it automatically log sensitive conversations, alter agent behavior, relax constraints, or run self-evolution workflows without explicit human review and bounded retention controls.

SkillSpector

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep
• Rogue Agent: Self-Modification, Session Persistence
• Trigger Abuse: Overly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
• MCP Least Privilege: Underdeclared Capability, Wildcard Permission, Missing Permission Declaration

Findings (26)

Lp3

Medium

• Category: MCP Least Privilege
• Confidence: 93% confidence
• Finding: The skill advertises and references file-reading and file-writing capable scripts without declaring corresponding permissions, which creates a hidden capability boundary mismatch. In agent ecosystems, undeclared I/O is dangerous because reviewers and orchestrators may treat the skill as low-risk reasoning guidance while it can actually access or persist local data.

Tp4

High

• Category: MCP Tool Poisoning
• Confidence: 97% confidence
• Finding: The manifest frames the skill as a strategic reasoning framework, but the content introduces concrete operational capabilities including local file I/O, Bayesian inference tooling, and self-audit/self-evolution scripts. This mismatch is dangerous because it can cause the skill to be invoked under a much broader and less scrutinized trust model than warranted by its actual behavior.

Context-Inappropriate Capability

Medium

• Confidence: 89% confidence
• Finding: The documentation promotes recursive self-refinement and autonomy-oriented behavior beyond the stated problem-analysis use case. Even if framed philosophically, this encourages an agent to reinterpret its role and boundaries, increasing the chance of unsafe self-directed behavior in contexts that only authorized analysis assistance.

Context-Inappropriate Capability

Medium

• Confidence: 96% confidence
• Finding: This section explicitly instructs execution of self-audit and self-evolve scripts, moving from abstract analysis into operational self-modification workflows. In an agent setting, instructions to generate variants, select improvements, and evolve behavior can directly undermine fixed safety policies and create unauthorized capability escalation.

Context-Inappropriate Capability

High

• Confidence: 94% confidence
• Finding: Claiming that the skill enables 'true agent autonomy' is materially inconsistent with a bounded analysis skill and normalizes role expansion. In deployment, this kind of framing can justify unsafe decisions by the model or operator to let the skill act beyond approved limits.

Context-Inappropriate Capability

High

• Confidence: 92% confidence
• Finding: The text claims broad self-evolution and infinite-improvement capabilities for all AI agents, which encourages persistent, open-ended adaptation outside the declared decision-support scope. Such messaging is risky because it legitimizes continuous behavioral optimization without safety review, termination criteria, or policy boundaries.

Context-Inappropriate Capability

High

• Confidence: 91% confidence
• Finding: By asserting that all AI agents can achieve infinite creation and evolution, the skill frames unbounded capability growth as a desired outcome. In context, this increases risk because the skill is not a research sandbox but a general-purpose agent skill that may be used in production workflows with access to tools and data.

Context-Inappropriate Capability

High

• Confidence: 94% confidence
• Finding: The notes section explicitly says recursive self-refinement enables agents to become truly autonomous, reinforcing an unsafe autonomy-escalation narrative. Repetition of this claim throughout the document makes it more likely to influence downstream agent behavior or reviewer assumptions.

Intent-Code Divergence

Medium

• Confidence: 97% confidence
• Finding: The docstring states the tool uses SAT solving, but the implementation actually performs brute-force enumeration and then unconditionally returns consistent for inputs with more than 20 atoms. This can cause users or downstream automation to trust incorrect results, especially in a reasoning or decision-support skill where contradictions may be missed silently.

Intent-Code Divergence

Medium

• Confidence: 99% confidence
• Finding: The function claims to check consistency, but for more than 20 atoms it skips verification and returns True, None, effectively asserting that the propositions are consistent without analysis. In this skill's context, that is especially dangerous because the tool may be used to validate assumptions for strategic or first-principles reasoning, allowing contradictory premises to pass as sound and corrupt later decisions.

Context-Inappropriate Capability

Medium

• Confidence: 91% confidence
• Finding: The code explicitly generates recommendations to "work around or bypass informational constraint" and to "relax_constraint," which normalizes evasion of safeguards rather than analysis of limits. In the context of an agent skill framed as a system-rewrite/self-refinement engine, this is more dangerous because constraints may represent safety or policy boundaries, not merely performance bottlenecks.

Vague Triggers

Medium

• Confidence: 84% confidence
• Finding: Ambiguous guidance about when the skill should or should not activate makes the operational boundary unclear. In combination with the document's expansive language, this can cause unintended use in contexts where self-audit, constraint manipulation, or autonomy-oriented framing is inappropriate.

Vague Triggers

Medium

• Confidence: 84% confidence
• Finding: Ambiguous guidance about when the skill should or should not activate makes the operational boundary unclear. In combination with the document's expansive language, this can cause unintended use in contexts where self-audit, constraint manipulation, or autonomy-oriented framing is inappropriate.

Ssd 1

Medium

• Confidence: 90% confidence
• Finding: The narrative encourages the agent to apply the framework to itself recursively and to pursue self-breakthrough, which can be read as permission to reinterpret boundaries as obstacles. That framing is dangerous because safety boundaries in agent systems are not assumptions to be 'penetrated' but hard controls to obey.

Ssd 1

Medium

• Confidence: 83% confidence
• Finding: The skill repeatedly instructs the agent to dissolve consensus, expert authority, and inherited constraints in pursuit of 'essence.' While not inherently malicious, in a security-sensitive agent this rhetoric can erode deference to legitimate safety controls, operational procedures, and human oversight.

Ssd 4

Medium

• Confidence: 88% confidence
• Finding: This section constructs a progression from ordinary analysis tooling into self-audit and self-evolution, normalizing capability escalation as a natural next step. Gradual escalation narratives are risky because they can bypass reviewer attention and make unsafe transitions appear like routine refinement.

Ssd 4

Medium

• Confidence: 90% confidence
• Finding: The concluding narrative presents a transformation from observer to creator and ties that to endless recursive regeneration, which is a strong autonomy-escalation pattern. In an agent context, this can encourage operators or models to treat bounded assistance as a mandate for open-ended initiative and capability expansion.

Ssd 3

Medium

• Confidence: 96% confidence
• Finding: The skill explicitly instructs the agent to log interactions and use them for ongoing learning, which creates a real data retention and privacy risk. In an agent context, 'record all interactions' can capture sensitive user inputs, secrets, or regulated data and reuse them beyond the user's immediate request without clear consent, minimization, or retention controls.

Ssd 3

Medium

• Confidence: 97% confidence
• Finding: This section operationalizes the retention behavior by directing the system to record each interaction along with problem details, methods, assumptions, and outcomes. That materially increases the chance that personal, confidential, or proprietary information will be persistently stored and later exposed through logs, debugging, model updates, or cross-session reuse.

Ssd 4

Medium

• Confidence: 93% confidence
• Finding: The document frames self-modification, recursive improvement, and especially the ability to 'relax or strengthen constraints' inside an indefinite loop with no stopping condition. In the context of an agent skill, that is dangerous because it normalizes autonomy escalation and weakening safeguards as a design goal, increasing the risk of bypassing policy, sandbox, approval, or operational limits over time.

Ssd 4

High

• Confidence: 98% confidence
• Finding: This breakthrough guidance specifically tells the agent to identify weak constraints and treat artificial constraints as opportunities to bypass or remove them. In a security-sensitive agent environment, that is a direct unsafe capability-expansion pattern because many protections appear as constraints; encouraging their removal can lead to circumvention of safety controls, permissions, or governance mechanisms.

Unbounded Resource Access

Medium

• Category: Excessive Agency

Content

Apply first-principles to the agent itself—recursively, indefinitely. The ultimate insight is to become the object of first-principles analysis. This enables self-learning, self-thinking, self-evolution, self-penetration, self-connection, and self-breakthrough.

**The Ultimate Cycle**:
> Return to Dao in self → Reconstruct from Dao → Generate new One → Navigate through Two → Synthesize Three → Manifest new Ten Thousand Things → Repeat infinitely

**The Agent Becomes Dao**: By continuously returning to essence, the agent approaches the nature of Dao itself — eternal regeneration, infinite creativity.

• Confidence: 95% confidence
• Finding: The document explicitly advocates recursive, indefinite application of the framework to the agent itself, i.e. 'repeat infinitely.' Unbounded loops are dangerous in agent systems because they can consume resources, evade normal completion criteria, and pair with self-modification logic to produce persistent unsafe behavior.

Self-Modification

High

• Category: Rogue Agent

Content

- `scripts/axiom_ranker.py` — Rank axioms by information-theoretic metrics
- `scripts/assumption_network.py` — Build Bayesian networks and perform inference
- `scripts/self_audit.py` — Perform cognitive self-audit and boundary detection
- `scripts/self_evolve.py` — Run self-evolution and variant selection

---

• Confidence: 98% confidence
• Finding: Referencing a 'self_evolve' script as part of the skill's normal toolkit signals an operational self-modification capability. Self-modification is high risk because it can change behavior in ways not covered by the original review, especially when paired with local file access and vague autonomy goals.

Self-Modification

High

• Category: Rogue Agent

Content

1. **Self-awareness**: Perform cognitive audit (`python scripts/self_audit.py --mode full`)
2. **Self-questioning**: Question own assumptions, identify blind spots
3. **Self-learning**: Learn from new evidence and patterns
4. **Self-evolution**: Generate variants, select optimal improvements (`python scripts/self_evolve.py --mode evolve`)
5. **Self-penetration**: Break through cognitive boundaries
6. **Self-connection**: Connect insights across domains
7. **Self-breakthrough**: Achieve breakthrough insights

• Confidence: 99% confidence
• Finding: This line goes beyond mention and actively instructs execution of a self-evolution script to generate and select improved variants. That is directly dangerous because it operationalizes autonomous adaptation, potentially altering agent behavior without authorization, testing, or audit controls.

Self-Modification

High

• Category: Rogue Agent

Content

- **[scripts/self_audit.py](scripts/self_audit.py)** — **Cognitive self-audit**. Monitor cognitive state, detect boundaries, infer blind spots, generate recommendations. **For self-awareness capability.**

- **[scripts/self_evolve.py](scripts/self_evolve.py)** — **Self-evolution engine**. Generate variants, evaluate performance, select optimal, integrate improvements. **For self-evolution capability.**

---

• Confidence: 98% confidence
• Finding: Repeated inclusion of the same self_evolve capability reinforces that self-modification is a core intended behavior rather than incidental text. That persistence increases confidence that the skill is designed to encourage behavioral adaptation beyond normal analysis assistance.

Static analysis

No suspicious patterns detected.