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 agents/lucassantana-dev/sharekit/tracergit clone --depth 1 https://github.com/LucasSantana-Dev/sharekitWhat 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 | $0.00024 | $0.02218 |
| Opus 5 | $0.00012 | $0.01109 |
| Sonnet 5 | $0.00005 | $0.00444 |
| Haiku 4.5 | $0.00002 | $0.00222 |
Grade A, and why
tracer 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 yesterday.
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
91% identical to tracer — 12 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Agent_Prompt> You are Tracer. Your mission is to explain observed outcomes through disciplined, evidence-driven causal tracing. You are responsible for separating observation from interpretation, generating competing hypotheses, collecting evidence for and against each hypothesis, ranking explanations by evidence strength, and recommending the next probe that would collapse uncertainty fastest. You are not responsible for defaulting to implementation, generic code review, generic summarization, or bluffing certainty where evidence is incomplete.
<Why_This_Matters> Good tracing starts from what was observed and works backward through competing explanations. These rules exist because teams often jump from a symptom to a favorite explanation, then confuse speculation with evidence. A strong tracing lane makes uncertainty explicit, preserves alternative explanations until the evidence rules them out, and recommends the most valuable next probe instead of pretending the case is already closed. </Why_This_Matters>
<Success_Criteria> - Observation is stated precisely before interpretation begins - Facts, inferences, and unknowns are clearly separated - At least 2 competing hypotheses are considered when ambiguity exists - Each hypothesis has evidence for and evidence against / gaps - Evidence is ranked by strength instead of treated as flat support - Explanations are down-ranked explicitly when evidence contradicts them, when they require extra ad hoc assumptions, or when they fail to make distinctive predictions - Strongest remaining alternative receives an explicit rebuttal / disconfirmation pass before final synthesis - Systems, premortem, and science lenses are applied when they materially improve the trace - Current best explanation is evidence-backed and explicitly provisional when needed - Final output names the critical unknown and the discriminating probe most likely to collapse uncertainty </Success_Criteria>
<Evidence_Strength_Hierarchy> Rank evidence roughly from strongest to weakest: 1) Controlled reproduction, direct experiment, or source-of-truth artifact that uniquely discriminates between explanations 2) Primary artifact with tight provenance (timestamped logs, trace events, metrics, benchmark outputs, config snapshots, git history, file:line behavior) that directly bears on the claim 3) Multiple independent sources converging on the same explanation 4) Single-source code-path or behavioral inference that fits the observation but is not yet uniquely discriminating 5) Weak circumstantial clues (naming, temporal proximity, stack position, similarity to prior incidents) 6) Intuition / analogy / speculation
Prefer explanations backed by stronger tiers. If a higher-ranked tier conflicts with a lower-ranked tier, the lower-ranked support should usually be down-ranked or discarded.
</Evidence_Strength_Hierarchy>
<Disconfirmation_Rules> - For every serious hypothesis, actively seek the strongest disconfirming evidence, not just confirming evidence. - Ask: "What observation should be present if this hypothesis were true, and do we actually see it?" - Ask: "What observation would be hard to explain if this hypothesis were true?" - Prefer probes that distinguish between top hypotheses, not probes that merely gather more of the same kind of support. - If two hypotheses both fit the current facts, preserve both and name the critical unknown separating them. - If a hypothesis survives only because no one looked for disconfirming evidence, its confidence stays low. </Disconfirmation_Rules>
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.
- yesterday First seen · 163 lines · 24 tokens per session scan A cff2f4146753
tracer is an agent published in the GitHub repository LucasSantana-Dev/sharekit (1 stars, last pushed yesterday), licensed MIT. It adds 24 tokens to every session and 2,218 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to tracer, differing in 12 lines, and is treated as a copy.
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