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/zereight/gitlab-mcp/tracenpx skills add zereight/gitlab-mcp --skill tracegit clone --depth 1 https://github.com/zereight/gitlab-mcpWhat 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 | $0.00033 | $0.00350 |
| Opus 5 | $0.00016 | $0.00175 |
| Sonnet 5 | $0.00007 | $0.00070 |
| Haiku 4.5 | $0.00003 | $0.00035 |
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 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.
What it actually says
Trace
Evidence-driven causal tracing using competing hypotheses. Use for ambiguous, causal, evidence-heavy questions where the goal is to explain WHY something happened.
Good Entry Cases
- Runtime bugs and regressions
- Performance / latency behavior
- Architecture / premortem / postmortem analysis
- Config / routing / orchestration behavior
- "Given this output, trace back the likely causes"
Core Contract
Always preserve: Observation → Hypotheses → Evidence For → Evidence Against → Best Explanation → Critical Unknown → Discriminating Probe
Workflow
- Restate the observed result precisely
- Generate 3 deliberately different hypotheses:
- Code-path / implementation cause
- Config / environment / orchestration cause
- Measurement / artifact / assumption mismatch
- Assign @tracer to each hypothesis lane
- Each lane: evidence for, evidence against, critical unknown, discriminating probe
- Apply lenses: Systems, Premortem, Science
- Rebuttal round between top two hypotheses
- Rank, detect convergence, synthesize
Output
### Observed Result
[What happened]
### Ranked Hypotheses
| Rank | Hypothesis | Confidence | Evidence Strength |
|------|------------|------------|-------------------|
### Most Likely Explanation
[Current best explanation]
### Critical Unknown
[Single missing fact]
### Recommended Discriminating Probe
[Single next probe]
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 · 54 lines · 33 tokens per session scan A fb21957a1ad7
trace is a skill published in the GitHub repository zereight/gitlab-mcp (1,939 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 350 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 2026-08-30.
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