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 skills/saitarrun/devforge-ai/root-cause-analysisnpx skills add saitarrun/Devforge-ai --skill root-cause-analysisgit clone --depth 1 https://github.com/saitarrun/Devforge-aiWrote 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/saitarrun/devforge-ai/root-cause-analysis)<a href="https://agentmods.dev/skills/saitarrun/devforge-ai/root-cause-analysis"><img src="https://agentmods.dev/badge/skills/saitarrun/devforge-ai/root-cause-analysis.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 | $0.00034 | $0.00759 |
| Opus 5 | $0.00017 | $0.00380 |
| Sonnet 5 | $0.00007 | $0.00152 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
root-cause-analysis 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 2d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hyper-Scale Root Cause Analysis & Automated Remediation Skill
This skill defines the methodology for pinpointing bugs, subtle edge cases, data corruptions, and performance degradations across massive (1,000,000+ files) enterprise codebases (such as Insurance, Banking, HealthTech, and ERP systems).
1. The Hyper-Scale Funnel Strategy (1.5M Files -> 1 File)
In a 1,500,000-file repository, full text search or naive scans will freeze and consume millions of tokens. Follow the Hyper-Scale Funnel:
[Level 1: Domain & Service Isolation] (1.5M files → ~500 files)
↳ Match error logs, stack traces, tenant IDs, or insurance claim IDs to the specific Domain Module (e.g. `apps/claims-engine`, `libs/underwriting-rules`)
↓
[Level 2: AST Knowledge Graph Traversal] (~500 files → ~20 files)
↳ Query `code-review-graph` / LSP for the exact Execution Seam & Call Stack (e.g., `calculateClaimDeductible()`)
↓
[Level 3: Delta & State Flow Backtracking] (~20 files → 1-3 files)
↳ Trace inputs, mutations, and database transaction queries to isolate the failing logic line
↓
[Level 4: Surgical Automated Remediation] (Exact lines modified)
↳ Run Ralph Loop: Generate test harness -> Verify repro -> Apply fix -> Run regression suite
2. Deep Root-Cause Backtracking (5-Whys for Enterprise Code)
When a complex failure occurs (e.g., "Insurance claim calculation is off by $124.50 on policy renewal"):
- Symptom Isolation: Capture exact inputs (Policy schema, Claim items, State regulations) and actual vs expected output.
- Backtrack Execution Flow:
- Ingress API: What endpoint accepted the payload?
- Validation & Middleware: Did any claim sanitization alter the numbers?
- Domain Calculation Engine: Where does the deductible formula execute?
- Persistence & Database State: Is a stale exchange rate or cached tier rule being queried?
- Isolate Root Cause: Determine if the defect is:
- Code Logic Error (e.g., rounding formula, boundary condition
<=vs<). - State Drift / Stale Cache (e.g., Redis cache invalidation missing after policy update).
- Schema / Migration Inconsistency (e.g., integer vs float precision mismatch).
- Race Condition / Concurrency Lock (e.g., double claim submission without distributed lock).
- Code Logic Error (e.g., rounding formula, boundary condition
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.
- 2d ago First seen · 61 lines · 34 tokens per session scan A be82cd4eeadd
root-cause-analysis is a skill published in the GitHub repository saitarrun/Devforge-ai (5 stars, last pushed 23d ago), licensed Apache-2.0. It adds 34 tokens to every session and 759 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 2026-09-03.
Other skills, from other repositories
zapier-status
Check the health of your Zapier MCP setup. Three modes — health check (dashboard view), audit (find waste and duplicates), diagnose (systematic troubleshooting). Use when asking "is my MCP working?", "check my tools", "audit my setup", "what's broken?", or "zapier status".
g-doctor
Read-only health diagnostics for G-Forge projects — 25 checks including hook registration, installed-copy drift, Check 23 plugin-version-lag, Check 24 CLAUDE.md injection-rule compliance, and Check 25 integration-tier guard. Recommends /plugins or /g-update by direction. Never writes.
g-optimize
Full-codebase or targeted performance audit. Detects algorithmic complexity problems, N+1 queries, re-render waste, resource leaks, and caching opportunities. Targeted scope produces an inline report. Whole-codebase scope produces a prioritised roadmap milestone.
postmortem
Auto-generates a structured postmortem from a completed campaign. Reads the campaign file, telemetry logs, and feature ledger. Produces a documented analysis of what broke, what the safety systems caught, and what patterns emerged. Can also be invoked manually for any incident.
systematic-debugging
4-phase root cause analysis: observe, hypothesize, verify, fix. Enforces investigation before any code changes. Emergency stop after 2 failed fixes. Prevents shotgun debugging and fix cascades.
audit
Project health audit and health check — architecture, performance, tests, dependencies, code quality. Use when assessing overall project health, before releases, or after refactors.