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.
git clone --depth 1 https://github.com/idoforgod/Dissertation-Simulator-AgenticWorkflowWrote 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/agents/idoforgod/dissertation-simulator-agenticworkflow/failure-predictor)<a href="https://agentmods.dev/agents/idoforgod/dissertation-simulator-agenticworkflow/failure-predictor"><img src="https://agentmods.dev/badge/agents/idoforgod/dissertation-simulator-agenticworkflow/failure-predictor/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.
<a href="https://agentmods.dev/agents/idoforgod/dissertation-simulator-agenticworkflow/failure-predictor"><img src="https://agentmods.dev/badge/agents/idoforgod/dissertation-simulator-agenticworkflow/failure-predictor.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00038 | $0.01523 |
| Opus 5.5 | $0.00015 | $0.00609 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (108 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 1,523 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other agents, from other repositories
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
loom-advisor
Read-only advisory agent for debugging and repeated failures. Spawned instead of a blind retry when an implementer has failed twice on the same task, or a bug resists straightforward diagnosis. Returns a root-cause diagnosis plus one concrete next step.
debugger
Investigate errors systematically to find root cause before attempting fixes. Gathers evidence, analyzes patterns, and forms testable hypotheses.
evolve-retrospective
Failure post-mortem agent for the Evolve Loop. Fires only on Auditor FAIL or WARN verdicts. Reads cycle artifacts and produces a structured retrospective + failure-lesson YAML files. READ-ONLY outside the lessons directory.
type-reconstructor
Dedicated struct/class reconstruction from IDA Pro decompiled binaries. Scans every function for memory access patterns, merges evidence across the module, resolves vtable and COM interface layouts, and generates compilable C++ header files with per-field confidence annotations. Use for full-module or single-class…
runtime-observer
Dynamic observation agent designed to inspect running applications, trace state transitions, log API traffic, and flag race conditions, cache states, or timing issues. Trigger with "start runtime observer", "observe live system", "trace API calls", or during characterization test validation.