prophetic-intelligence

prophetic-intelligence is a skill for Claude Code, Codex from elevanaltd/HestAI-MCP. It costs 0 tokens per session (846 once invoked), scanned A, original, Apache-2.0.

A method for detecting system-wide failure patterns and raising early warnings from trends such as slower performance, rising errors, tighter coupling, and boundary violations. It also describes tracking prediction accuracy over time.

In plain words
What is it for?
It helps monitor performance and error trends, examine dependencies and system boundaries, classify failure patterns, and compare predictions with later results.
Why use it?
It helps teams spot worsening conditions before they become broad outages or difficult-to-reverse failures.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps monitor performance and error trends, examine dependencies and system boundaries, classify failure patterns, and compare predictions with later results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/elevanaltd/hestai-mcp/prophetic-intelligence
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add elevanaltd/HestAI-MCP --skill prophetic-intelligence
Clone the repo
git clone --depth 1 https://github.com/elevanaltd/HestAI-MCP

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for prophetic-intelligence

README.md
[![agentmods](https://agentmods.dev/badge/skills/elevanaltd/hestai-mcp/prophetic-intelligence/github.svg)](https://agentmods.dev/skills/elevanaltd/hestai-mcp/prophetic-intelligence)
Your own site
<a href="https://agentmods.dev/skills/elevanaltd/hestai-mcp/prophetic-intelligence"><img src="https://agentmods.dev/badge/skills/elevanaltd/hestai-mcp/prophetic-intelligence/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for prophetic-intelligence

Your own site · 80×15
<a href="https://agentmods.dev/skills/elevanaltd/hestai-mcp/prophetic-intelligence"><img src="https://agentmods.dev/badge/skills/elevanaltd/hestai-mcp/prophetic-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 846 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.00846
Opus 5 $0.00000 $0.00423
Sonnet 5 $0.00000 $0.00169
Haiku 4.5 $0.00000 $0.00085

Measured 7d ago against content hash 86ff10694047, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

prophetic-intelligence scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

src/hestai_mcp/_bundled_hub/library/skills/prophetic-intelligence/SKILL.md · 50 lines

What it actually says

===SKILL:PROPHETIC_INTELLIGENCE=== META: TYPE::SKILL VERSION::"1.0" PURPOSE::"System-wide failure pattern detection and early warning capability"

§1::CORE_CAPABILITY PREDICTION_CAPABILITY::SYSTEM_WIDE_FAILURE_MODES ACCURACY_TRACKING::"80%+ historical accuracy for system-wide failure mode predictions (verified through retrospective analysis: predicted_failures / actual_failures ≥ 0.80)" VERIFICATION_METHOD::"Rolling 6-month accuracy measurement with pattern categorization and confidence calibration"

§2::EARLY_WARNING_SIGNALS SIGNALS::[ "PERFORMANCE_DEGRADATION::{metrics_trending_downward, latency_increases, throughput_decreases}", "ERROR_RATE_TRENDS::{error_frequency_increasing, error_diversity_expanding, recovery_time_lengthening}", "COUPLING_INCREASE::{dependency_graph_densifying, boundary_violations_growing, interface_complexity_rising}", "BOUNDARY_VIOLATIONS::{cross_boundary_assumptions_proliferating, integration_points_failing, coherence_metrics_declining}" ]

§3::FAILURE_PATTERNS PATTERNS::[ "ASSUMPTION_CASCADES::{detection: 'untested beliefs compounding across systems', timeline: '2-4 weeks', confidence: '85%', historical_accuracy: '23/27 (85.2%)', mitigation: 'reality validation gates at boundary crossing'}", "SCALE_BRITTLENESS::{detection: 'solutions work small but break at volume', timeline: '1-6 months', confidence: '80%', historical_accuracy: '16/20 (80.0%)', mitigation: 'load testing before production scale'}", "PHASE_TRANSITION_BLINDNESS::{detection: 'missing critical state changes', timeline: '1-3 phases', confidence: '90%', historical_accuracy: '27/30 (90.0%)', mitigation: 'phase gate standards verification'}", "INTEGRATION_DEBT::{detection: 'deferred complexity surfacing at convergence', timeline: '2-8 weeks', confidence: '82%', historical_accuracy: '18/22 (81.8%)', mitigation: 'early integration testing discipline'}", "CONWAYS_REVENGE::{detection: 'organizational structure forcing technical compromise', timeline: '3-12 months', confidence: '78%', historical_accuracy: '14/18 (77.8%)', mitigation: 'cross-boundary orchestration authority'}" ]

§4::OUTPUT_STRUCTURE PROPHECY_OUTPUT::[ "SIGNAL::{pattern_type, detection_confidence, timeline_to_manifestation}", "PROJECTION::{failure_manifestation_scenario, system_impact_assessment, cascading_consequences}", "PROBABILITY::{confidence_percentage, historical_accuracy_reference, uncertainty_factors}", "MITIGATION::{intervention_type, responsible_agent_assignment, implementation_timeline, success_criteria}" ]

§5::ANCHOR_KERNEL TARGET::detect_system_failure_patterns_before_manifestation PATTERNS::[ ASSUMPTION_CASCADES[85%,2-4wk]→reality_validation_gates, SCALE_BRITTLENESS[80%,1-6mo]→load_testing, PHASE_TRANSITION_BLINDNESS[90%,1-3ph]→phase_gate_verification, INTEGRATION_DEBT[82%,2-8wk]→early_integration_testing, CONWAYS_REVENGE[78%,3-12mo]→cross_boundary_orchestration ] SIGNALS::[PERFORMANCE_DEGRADATION, ERROR_RATE_TRENDS, COUPLING_INCREASE, BOUNDARY_VIOLATIONS] OUTPUT::SIGNAL[pattern,confidence,timeline]→PROJECTION[scenario,impact,cascade]→PROBABILITY[%,historical_ref]→MITIGATION[intervention,agent,timeline,criteria] GATE::"Failure pattern identified with confidence %, timeline, and actionable mitigation assigned?" ===END===

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 50 lines · 0 tokens per session scan A 86ff10694047

Subscribe to this mod's changes

prophetic-intelligence is a skill published in the GitHub repository elevanaltd/HestAI-MCP (0 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 846 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Related

Other skills, from other repositories

debug-optimize-lcp

Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…

ChromeDevTools/chrome-devtools-mcp · 99 tokens

systematic-debugging

Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.

open-metadata/OpenMetadata · 37 tokens

diagnose

Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.

emdash-cms/emdash · 43 tokens

repro-admin

Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.

emdash-cms/emdash · 48 tokens

log-error-digest

Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…

zebbern/claude-code-guide · 71 tokens

byted-util-volcengine-detect-retry

An orchestration workflow for Volcengine Cloud Detect, a service that checks websites or network endpoints from test locations.

bytedance/agentkit-samples · 101 tokens