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/thangchung/agent-engineering-experiment/debug-issuenpx skills add thangchung/agent-engineering-experiment --skill debug-issuegit clone --depth 1 https://github.com/thangchung/agent-engineering-experimentWhat 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 | $0.00013 | $0.00248 |
| Opus 5 | $0.00006 | $0.00124 |
| Sonnet 5 | $0.00003 | $0.00050 |
| Haiku 4.5 | $0.00001 | $0.00025 |
Grade A, and why
debug-issue 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- debug-issue — 100% identical, 2 lines differ
What it actually says
Debug Issue
Use the knowledge graph to systematically trace and debug issues.
Steps
- Use
semantic_search_nodes_toolto find code related to the issue. - Use
query_graph_toolwithcallers_ofandcallees_ofto trace call chains. - Use
get_flowto see full execution paths through suspected areas. - Run
detect_changes_toolto check if recent changes caused the issue. - Use
get_impact_radius_toolon suspected files to see what else is affected.
Tips
- Check both callers and callees to understand the full context.
- Look at affected flows to find the entry point that triggers the bug.
- Recent changes are the most common source of new issues.
Token Efficiency Rules
- ALWAYS start with
get_minimal_context(task="<your task>")before any other graph tool. - Use
detail_level="minimal"on all calls. Only escalate to "standard" when minimal is insufficient. - Target: complete any review/debug/refactor task in ≤5 tool calls and ≤800 total output tokens.
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 · 28 lines · 13 tokens per session scan A ed347c3ee7f7
debug-issue is a skill published in the GitHub repository thangchung/agent-engineering-experiment (24 stars, last pushed 1mo ago), licensed MIT. It adds 13 tokens to every session and 248 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
phoenix-cli
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding…
phoenix-github
Manage GitHub issues, labels, project boards, sprint operations, and roadmap health for the Arize-ai/phoenix repository. Use when filing roadmap issues, triaging bugs, applying labels, running sprint close-out and rollover, auditing board hygiene, checking ticket-load balance across the team, keeping roadmap epics up…
pxi-eval-dataset
Generate synthetic evaluation datasets for the PXI eval harness (evals/pxi/). Use whenever the user asks to create, author, draft, expand, or audit an eval dataset for a PXI tool, skill, or behavior — including phrases like "write evals for ", "test PXI behavior", "synthetic dataset for PXI", "cover this tool with…
phoenix-docs-gap-audit
Audit documentation gaps across the Phoenix repo by analyzing recent commits to main (default: last 7 days). Use this skill whenever the user asks to find undocumented features, identify docs gaps, audit what shipped without docs, check which recent changes need documentation, review stale docs against current code…
phoenix-release-notes
Create Phoenix release documentation grounded in actual code changes. Use this skill whenever the user asks to write release notes, document a release, update release documentation, or mentions undocumented releases. Also trigger when the user wants to update GitHub release descriptions, add entries to the release…
phoenix-skills-audit
Audit recent changes to Phoenix's user-facing surfaces (Python clients, TypeScript clients, CLI, REST/GraphQL APIs) and patch the three external-facing agent skills — phoenix-tracing, phoenix-cli, and phoenix-evals — so they stay in sync with what actually shipped. Use this skill whenever a user asks to update those…