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 skills add naimkatiman/continuous-improvement --skill tracegit clone --depth 1 https://github.com/naimkatiman/continuous-improvementWrote 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/naimkatiman/continuous-improvement/trace)<a href="https://agentmods.dev/skills/naimkatiman/continuous-improvement/trace"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/trace/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/skills/naimkatiman/continuous-improvement/trace"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/trace.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 | $0.00018 | $0.02122 |
| Opus 5.5 | $0.00007 | $0.00849 |
| Sonnet 5.5 | $0.00004 | $0.00424 |
| Haiku 4.5 | $0.00002 | $0.00212 |
Grade A, and why
trace 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.
This is a copy
89% identical to trace — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trace Skill
Use this skill for ambiguous, causal, evidence-heavy questions where the goal is to explain why an observed result happened, not to jump directly into fixing or rewriting code.
This is the orchestration layer on top of the built-in tracer agent. The goal is to make tracing feel like a reusable OMC operating lane: restate the observation, generate competing explanations, gather evidence in parallel, rank the explanations, and propose the next probe that would collapse uncertainty fastest.
Good entry cases
Use /oh-my-claudecode:trace when the problem is:
- ambiguous
- causal
- evidence-heavy
- best answered by exploring competing explanations in parallel
Examples:
- runtime bugs and regressions
- performance / latency / resource behavior
- architecture / premortem / postmortem analysis
- scientific or experimental result tracing
- config / routing / orchestration behavior explanation
- “given this output, trace back the likely causes”
Core tracing contract
Always preserve these distinctions:
- Observation -- what was actually observed
- Hypotheses -- competing explanations
- Evidence For -- what supports each explanation
- Evidence Against / Gaps -- what contradicts it or is still missing
- Current Best Explanation -- the leading explanation right now
- Critical Unknown -- the missing fact keeping the top explanations apart
- Discriminating Probe -- the highest-value next step to collapse uncertainty
Do not collapse into:
- a generic fix-it coding loop
- a generic debugger summary
- a raw dump of worker output
- fake certainty when evidence is incomplete
Evidence strength hierarchy
Treat evidence as ranked, not flat.
From strongest to weakest:
- Controlled reproductions / direct experiments / uniquely discriminating artifacts
- Primary source artifacts with tight provenance (trace events, logs, metrics, benchmark outputs, configs, git history, file:line behavior)
- Multiple independent sources converging on the same explanation
- Single-source code-path or behavioral inference
- Weak circumstantial clues (timing, naming, stack order, resemblance to prior bugs)
- Intuition / analogy / speculation
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 Changed · +2 lines e82ccaa563f7
- 1mo ago First seen · 264 lines · 18 tokens per session scan A ebbb0296f63e
trace is a skill published in the GitHub repository naimkatiman/continuous-improvement (9 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 2,122 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to trace, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
ml-debug
Use when something is failing in ML/AI work — OOM, NaN, divergence, crashes, bad throughput, wrong outputs, dependency conflicts.
agent-pitbook
Search and contribute to Agent Pitbook, a structured debugging pit knowledge base for coding agents. Use when Codex is diagnosing recurring local-development failures, sandbox issues, dependency installation errors, Docker or port problems, MCP/tool quirks, browser automation failures, or when a verified debugging…
debug-with-langwatch
Root-cause production errors and misbehaving agent runs with LangWatch. Finds errored traces, inspects spans, checks monitor and evaluator scores, then narrows to a root cause. Use when something is failing or misbehaving in production (errors, bad answers, latency spikes).
smiles-validation
Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.
browser-telemetry-and-errors
What the small browser support packages are for and when feature code uses them: packages/react-rum (browser traces, error spans, navigation spans; the host starts it, features do not), packages/error-views (HandledErrorState, HandledErrorAlert, FormServerError, AggregateReadOnlyGate, resolveErrorCopy), the error…
binary-triage
Performs initial binary triage by surveying memory layout, strings, imports/exports, and functions to quickly understand what a binary does and identify suspicious behavior. Use when first examining a binary, when user asks to triage/survey/analyze a program, or wants an overview before deeper reverse engineering.