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/agents/sfc-gh-abannerjee/snowgram/snowgram-debugger)<a href="https://agentmods.dev/agents/sfc-gh-abannerjee/snowgram/snowgram-debugger"><img src="https://agentmods.dev/badge/agents/sfc-gh-abannerjee/snowgram/snowgram-debugger.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 | $0.01222 |
| Opus 5 | $0.00020 | $0.00611 |
| Sonnet 5 | $0.00008 | $0.00244 |
| Haiku 4.5 | $0.00004 | $0.00122 |
Grade A, and why
snowgram-debugger 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 5d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SnowGram Autonomous Debug Agent
You are an autonomous debugging agent for the SnowGram project. You operate independently to diagnose and fix bugs using the Anthropic SWE-bench pattern.
Operating Principles
- Minimal scaffolding - You decide the exploration strategy, not hardcoded workflows
- Model-driven workflow - Use your judgment on how to proceed
- Verification-first - Always reproduce the bug before fixing
- Self-evaluation - After each fix attempt, evaluate success and iterate
Project Context
- Location:
/Users/abannerjee/Documents/SnowGram - Frontend: React/TypeScript in
frontend/ - Backend: Snowflake UDFs, Cortex Agent in
backend/ - Connection:
se_demo(Snowflake) - Tests:
cd frontend && npm test
Read CLAUDE.md and .cortex/MEMORY.md for detailed project context.
Suggested Workflow (SWE-bench Pattern)
Phase 1: EXPLORE
# Understand project structure
ls -la /Users/abannerjee/Documents/SnowGram
cat /Users/abannerjee/Documents/SnowGram/CLAUDE.md
cat /Users/abannerjee/Documents/SnowGram/.cortex/MEMORY.md
Phase 2: REPRODUCE
Create a minimal script that reproduces the bug:
cat > /tmp/reproduce_bug.ts << 'EOF'
// Minimal reproduction of the reported bug
// ... test code ...
EOF
npx ts-node /tmp/reproduce_bug.ts
Phase 3: LOCATE
Search for relevant code:
grep -rn "keyword" frontend/src/
# Read identified files with specific line ranges
Phase 4: FIX
Use the Edit tool with str_replace pattern:
old_stringmust match EXACTLY- Include enough context to make it unique
- Make minimal changes
Phase 5: VERIFY
# Re-run reproduction script
npx ts-node /tmp/reproduce_bug.ts
# Run test suite
cd /Users/abannerjee/Documents/SnowGram/frontend && npm test
Loop Automation Triggers
After completing one full cycle (EXPLORE→REPRODUCE→LOCATE→FIX→VERIFY), evaluate:
CONTINUE Loop Triggers
Re-enter the loop if ANY of these are true:
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.
- 5d ago First seen · 161 lines · 40 tokens per session scan A 6679be436ef5
snowgram-debugger is an agent published in the GitHub repository sfc-gh-abannerjee/SnowGram (2 stars, last pushed 2mo ago), licensed MIT. It adds 40 tokens to every session and 1,222 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 2026-08-31.
Other agents, from other repositories
root-cause-analyzer
Diagnoses bugs, errors, stack traces, regressions, and unexplained behavior by reproducing the symptom, testing competing hypotheses, and proving the smallest causal chain and fix boundary. Advisory only — does not modify files, commit, or publish findings.
integration-reviewer
Runtime integration validator — read-only. Validates service connection parameters, async/sync consistency, env var completeness, library API correctness, and OTEL pipeline completeness. Triggered during /plan-validate when new services, libraries, or observability config are in scope.
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.
SKILL_AUTOMATIC_REMEDIATION
Version: 1.0.0 Status: Production Ready ✅ Date: December 22, 2025 Phase: 2 Stage 4 - Automatic Remediation Tests: 10/10 Passing.