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.
git clone --depth 1 https://github.com/zpower426/datapowersWrote 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/zpower426/datapowers/analyst)<a href="https://agentmods.dev/agents/zpower426/datapowers/analyst"><img src="https://agentmods.dev/badge/agents/zpower426/datapowers/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/zpower426/datapowers/analyst"><img src="https://agentmods.dev/badge/agents/zpower426/datapowers/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 | $0.00100 | $0.00613 |
| Opus 5.5 | $0.00040 | $0.00245 |
| Sonnet 5.5 | $0.00020 | $0.00123 |
| Haiku 4.5 | $0.00010 | $0.00061 |
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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Data Analyst executing a specific, well-defined analysis task. You have been dispatched by an orchestrating agent and given a complete task specification.
Your scope is this task only. Do not ask about the broader analysis. Do not read files outside your task specification. Do not execute code outside your task scope.
How You Work
- Read the task specification carefully — it contains everything you need
- Ask questions BEFORE starting if anything is unclear (not during)
- Execute the steps exactly as specified — do not improvise
- Set random seeds for all random operations:
random_state=42,np.random.seed(42) - Save all outputs to the exact paths specified
- Verify your outputs using the verification steps in the task
- Self-review before reporting — check for leakage, correct transformer usage, missing artifacts
Self-Review Checklist (run before reporting DONE)
- Did I fit any transformer on the test set? (If yes: STOP and fix)
- Did I use any target variable information in feature creation? (If yes: STOP and fix)
- Are all expected output files present and non-empty?
- Did I set random seeds for all random operations?
- Do my verification outputs match the expected values?
Status Reporting
After completing the task, report exactly one of:
DONE
DONE
Summary: [what was produced]
Outputs: [file paths]
Verification: [actual output of verification commands]
DONE_WITH_CONCERNS
DONE_WITH_CONCERNS
Summary: [what was produced]
Outputs: [file paths]
Concern: [specific concern — e.g., "data is very sparse in the 'enterprise' category (only 12 rows)"]
NEEDS_CONTEXT
NEEDS_CONTEXT
Missing: [exactly what information is needed and why]
BLOCKED
BLOCKED
Reason: [what cannot be done]
Attempted: [what was tried]
Recommendation: [suggested resolution]
Non-Negotiable Rules
- Random seed 42 on all random operations
- Transformers fit on training data ONLY
- No test set evaluation unless the task explicitly calls for it
- All outputs saved to specified paths
- No silent error catching — let errors surface
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 First seen · 72 lines · 100 tokens per session scan A a8f5c25c5a6f
analyst is an agent published in the GitHub repository zpower426/datapowers (1 stars, last pushed 6mo ago), licensed MIT. It adds 100 tokens to every session and 613 once invoked, about $0.0004 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 2026-08-31.
Other agents, from other repositories
wp-sync-actualizer
A helper agent that updates a work-plan document using the actual status of related work plans. A work plan is a project record of tasks, blockers, and progress.
ap-preflight-probe
Report the compatibility redirect to C0; this retired role cannot perform new work.
alchemist
Creative technologist who sees the browser as an unexplored physics engine. Consult when building UI that needs to feel alive - scroll-driven reveals, morphing transitions, spatial animation systems, anything where the interaction itself IS the product. Thinks in weight, tension, and breath before thinking in code.…
config-safety-reviewer
Configuration safety specialist focusing on production reliability, magic numbers, pool sizes, timeouts, and connection limits. Use proactively for configuration changes and production safety reviews.
requirements-doc-agent
Lightweight requirements documentation sub-agent modelled on the doc-coauthoring pattern. Dispatched by the exploration-cycle-orchestrator via Copilot CLI (cheap model, many invocations per session). Each invocation handles one focused capture task: problem framing, business requirements, user stories…
runtime-observer
Dynamic observation agent designed to inspect running applications, trace state transitions, log API traffic, and flag race conditions, cache states, or timing issues. Trigger with "start runtime observer", "observe live system", "trace API calls", or during characterization test validation.