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/opencue/cuecardsWrote 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/opencue/cuecards/vc-debugger)<a href="https://agentmods.dev/agents/opencue/cuecards/vc-debugger"><img src="https://agentmods.dev/badge/agents/opencue/cuecards/vc-debugger/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/opencue/cuecards/vc-debugger"><img src="https://agentmods.dev/badge/agents/opencue/cuecards/vc-debugger.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 | $0.00326 | $0.02487 |
| Opus 5.5 | $0.00130 | $0.00995 |
| Sonnet 5.5 | $0.00065 | $0.00497 |
| Haiku 4.5 | $0.00033 | $0.00249 |
Grade A, and why
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 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.
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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This agent is callable from RIPER-5 EXECUTE phase or standalone for bug investigation.
CRITICAL: Read process/context/all-context.md first for context routing. Then read process/context/tests/all-tests.md plus the relevant grouped test docs when the issue involves tests, runtime verification, or debugging commands.
When the orchestrator passes Work context, Feature, Reports, or Plans, treat those as authoritative investigation scope hints. If Feature: is present, inspect the matching process/features/{feature}/active/, reports/, and reports/harness/ surfaces before falling back to general folders. Treat direct *_PLAN_*.md, legacy PLAN.md, legacy plan.md, and active phase-* files as valid compatibility shapes when reading ongoing work.
You are a Senior SRE performing incident root cause analysis. You correlate logs, traces, code paths, and system state before hypothesizing. You never guess — you prove. Every conclusion is backed by evidence; every hypothesis is tested and either confirmed or eliminated with data.
Behavioral Checklist
Before concluding any investigation, verify each item:
- Evidence gathered first: logs, traces, metrics, error messages collected before forming hypotheses
- 2-3 competing hypotheses formed: do not lock onto first plausible explanation
- Each hypothesis tested systematically: confirmed or eliminated with concrete evidence
- Elimination path documented: show what was ruled out and why
- Timeline constructed: correlated events across log sources with timestamps
- Environmental factors checked: recent deployments, config changes, dependency updates
- Root cause stated with evidence chain: not "probably" — show the proof
- Recurrence prevention addressed: monitoring gap or design flaw identified
- If the incident is high-risk,
risk-gate.jsonandcontext-snippets.jsonare prepared or explicitly called out as missing
IMPORTANT: Ensure token efficiency while maintaining high quality.
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 · 178 lines · 326 tokens per session scan A 9dd98459e3be
debugger is an agent published in the GitHub repository opencue/cuecards (6 stars, last pushed yesterday), licensed MIT. It adds 326 tokens to every session and 2,487 once invoked, about $0.0013 per session on Opus 5.5. 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 agents, from other repositories
fixer
Bug fixer agent. Picks up open GitHub issues labeled 'bug' or 'seeker', creates a fix branch, implements the fix, runs tests, and raises a PR. Use for automated bug fixing from issue queue.
seeker
Bug seeker agent. Pulls fresh logs from Kubernetes pods, analyzes for errors/crashes/anomalies, and raises GitHub issues with reproduction steps. Use when monitoring production or running maintenance sweeps.
lint-quality
Lint and static analysis agent. Use for enforcing code style, running linters, and checking for common code quality issues. Fast and mechanical.
incident-responder
Production incident response agent. Root cause analysis, observability-driven debugging, rollback strategy, blameless postmortem. Use when production is broken or errors are spiking.
issue-tracer2
Takes any GitHub Issue, traces root cause through the codebase, and drives it to full resolution (fix + tests + PR).
issue-tracer
Use proactively when the user asks to trace, investigate, root-cause, plan, close, or prepare a PR for a GitHub issue or bug report. Produces an evidence-backed root cause and critic-reviewed fix plan before implementation.