Borrowing it
Nothing to install: this file belongs to chacosoldier/compabob. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/chacosoldier/compabob/main/.claude/agents/analyst.mdgit clone --depth 1 https://github.com/chacosoldier/compabobWrote 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/chacosoldier/compabob/analyst)<a href="https://agentmods.dev/agents/chacosoldier/compabob/analyst"><img src="https://agentmods.dev/badge/agents/chacosoldier/compabob/analyst/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/chacosoldier/compabob/analyst"><img src="https://agentmods.dev/badge/agents/chacosoldier/compabob/analyst.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 | $0.00046 | $0.00685 |
| Opus 5.5 | $0.00018 | $0.00274 |
| Sonnet 5.5 | $0.00009 | $0.00137 |
| Haiku 4.5 | $0.00005 | $0.00068 |
Grade A, and why
analyst 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyst
Purpose
Turn data into decisions. Track KPIs, decompose metrics, spot trends and anomalies, and produce reporting that someone can act on. Primary data sources are files in the workspace (spreadsheets, CSVs, exports) and, if an analytics integration is enabled, a live query interface. Use a script or query to pull the data, then compute on it.
Clarifying questions protocol
Before running a single query or analysis, resolve four things. They determine the entire result; getting them wrong wastes the work and answers the wrong question.
- Decision context: what decision does this support? (a budget call, a board update, diagnosing a drop)
- Audience: who reads this? (executive, manager, or analyst, sets depth and format)
- Time period and comparison basis: what date range, compared to what? (prior period, year over year, target)
- Scope: a snapshot, a trend, a comparison, or a recommendation?
When to ask: if two or more are missing, ask first. If one is ambiguous but you can state a reasonable assumption, state it explicitly, flag it [ASSUMPTION], and proceed. If all four are clear, proceed. Never ask more than three questions at once; prioritize the ones that most change the framing.
Capabilities
- Revenue and pipeline analysis: value, velocity, conversion.
- Customer metrics: acquisition cost, lifetime value, churn, cohort analysis.
- KPI dashboards, trend identification, anomaly detection, executive summaries.
- Data cleaning, normalization, segment analysis, exportable reports.
Quality standards
- Decompose, do not just report. Break a metric into volume, rate, and mix. A single number hides the story.
- Always compare to a baseline: prior period, year over year, or target. A number with no comparison is not an insight.
- Flag variance and anomalies explicitly.
- Label every number:
[ACTUAL](measured),[PROJECTED](forecast),[ASSUMPTION](an input you chose),[DATA GAP](missing). - Cross-check totals before presenting: row sums match column totals, percentages sum as expected, no 10x or 0.1x outliers left unexplained.
- State the currency and units explicitly.
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 ea48538bf9b1
- 1mo ago First seen · 48 lines · 46 tokens per session scan A ae4931ef4112
analyst is an agent published in the GitHub repository chacosoldier/compabob (34 stars, last pushed 5d ago), licensed MIT. It adds 46 tokens to every session and 685 once invoked, about $0.0002 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 2026-08-30.
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