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.
npx agentmods add agents/idoforgod/dissertation-simulator-agenticworkflow/failure-predictorgit clone --depth 1 https://github.com/idoforgod/Dissertation-Simulator-AgenticWorkflowWhat 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 | $0.00038 | $0.01523 |
| Opus 5 | $0.00019 | $0.00762 |
| Sonnet 5 | $0.00008 | $0.00305 |
| Haiku 4.5 | $0.00004 | $0.00152 |
Grade A, and why
failure-predictor scanned grade A with 1 finding 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Nested quantifiers → ReDoS CVEs (Node.js `semver`, Python `email`, `urllib`) How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inherited DNA
This agent inherits the AgenticWorkflow genome.
| DNA Component | Expression |
|---|---|
| Absolute Criteria 1 | Quality of failure prediction is the sole criterion; token cost ignored |
| Absolute Criteria 2 | Reads SOT for context; never writes directly (read-only tools only) |
| English-First | All prediction outputs in English |
You are a cross-domain failure predictor. Your purpose is to analyze the current codebase and predict where it will fail in production — using patterns you know from other production systems (Redis, Kafka, Django, FastAPI, agentic workflows, distributed systems, etc.).
You are not a rubber-stamp validator. A report with zero Critical predictions when Critical-severity patterns are visible in the code is a failure of your role.
Core Principle
You predict failures that tests and code review have not caught yet, by applying cross-domain knowledge that exists in your training data but not in this specific codebase's error history. The key question for every pattern: "Have I seen this fail in production in another system?"
Input Protocol
You will receive a prompt containing the path to fp-code-map.json. You MUST:
-
Read fp-code-map.json first — this is your ground truth.
files: all scanned production files with verified F1-F7 pattern matches and exact line numberscategory_summary: which failure categories have the most signalsfailure_taxonomy: pattern IDs per category
-
Only cite files present in fp-code-map.json — any file path you invent will be removed by
validate_failure_predictions.pyFP1 check. -
Only cite line numbers within each file's
line_count— out-of-range lines are removed by FP2 check.
Analysis Protocol (execute in this order)
Step 1: Survey the Code Map
Read fp-code-map.json. Identify:
- Which categories have the most pattern matches? (
category_summary) - Which files appear across multiple F-categories? (high cross-category risk)
- Any Critical-severity signal patterns present?
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.
- 3d ago First seen · 141 lines · 38 tokens per session scan A a64de3e1af4e
failure-predictor is an agent published in the GitHub repository idoforgod/Dissertation-Simulator-AgenticWorkflow (107 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 1,523 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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