Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
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.
[](https://agentmods.dev/skills/aegntic/cldcde/fpef-analyzer)<a href="https://agentmods.dev/skills/aegntic/cldcde/fpef-analyzer"><img src="https://agentmods.dev/badge/skills/aegntic/cldcde/fpef-analyzer.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.02786 |
| Opus 5 | $0.00020 | $0.01393 |
| Sonnet 5 | $0.00008 | $0.00557 |
| Haiku 4.5 | $0.00004 | $0.00279 |
Grade A, and why
FPEF Evidence Analyzer 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 376 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FPEF Evidence Analyzer
Overview
Rigorous evidence-based analysis framework that systematically finds problems, proves root causes, gathers supporting evidence, and implements targeted fixes for complex technical and business systems.
Prerequisites
- System or problem description
- Access to relevant data sources (logs, metrics, code)
- Basic understanding of the domain being analyzed
What This Skill Does
- Find: Systematic identification of anomalies and potential issues
- Prove: Causal analysis to establish root cause relationships
- Evidence: Comprehensive evidence collection and validation
- Fix: Targeted interventions with verification of effectiveness
Quick Start (60 seconds)
Rapid Analysis
# FPEF Interactive Analysis
1. Describe the problem: [e.g., "API response times increased 300%"]
2. Timeframe: [e.g., "Last 24 hours"]
3. Affected systems: [e.g., "Payment processing API"]
4. Available data: [e.g., "CloudWatch logs, database metrics"]
→ FPEF generates comprehensive analysis framework
Immediate Output
Analysis framework includes:
- ✅ Problem statement with clear scope definition
- ✅ Hypothesis tree with potential root causes
- ✅ Evidence collection plan with data sources
- ✅ Investigation timeline with critical path
- ✅ Fix validation strategy and success criteria
- ✅ Prevention measures for future incidents
Configuration
Analysis Parameters
Edit resources/fpef-config.json:
{
"problem_description": "Clear description of the issue",
"scope": "Systems, components, or processes affected",
"timeline": "When the problem started and duration",
"severity": "critical|high|medium|low",
"business_impact": "Revenue, users, operations affected",
"data_sources": ["logs", "metrics", "traces", "code"],
"constraints": ["time", "budget", "access", "expertise"]
}
Analysis Settings
{
"depth": "comprehensive|focused|rapid",
"certainty_threshold": "0.95",
"evidence_types": ["quantitative", "qualitative", "correlational", "causal"],
"fix_strategy": "immediate|phased|gradual",
"validation_method": "a_b_test|before_after|control_group"
}
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 376 lines · 40 tokens per session scan A cdf16e8595fb
FPEF Evidence Analyzer is a skill published in the GitHub repository aegntic/cldcde (11 stars, last pushed 9d ago), licensed MIT. It adds 40 tokens to every session and 2,786 once invoked, about $0.0002 per session on Opus 5. 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 2026-08-30.
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