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 zpower426/datapowers --skill analysis-manifestgit 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/skills/zpower426/datapowers/analysis-manifest)<a href="https://agentmods.dev/skills/zpower426/datapowers/analysis-manifest"><img src="https://agentmods.dev/badge/skills/zpower426/datapowers/analysis-manifest/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/zpower426/datapowers/analysis-manifest"><img src="https://agentmods.dev/badge/skills/zpower426/datapowers/analysis-manifest.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 | $0.00045 | $0.02289 |
| Opus 5 | $0.00023 | $0.01144 |
| Sonnet 5 | $0.00009 | $0.00458 |
| Haiku 4.5 | $0.00005 | $0.00229 |
Grade A, and why
analysis-manifest scanned grade A with 1 finding 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 9d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
git_commit = subprocess.check_output( How it starts
The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analysis Manifest
Maintain a persistent JSON record of the current analysis session. Every skill stage reads the manifest to understand what has been completed and writes its outputs back. This solves context drift in long analyses where agents lose track of earlier decisions.
Why a manifest: In a multi-hour analysis, agents forget what hypotheses were agreed in brainstorming, what metric was declared primary, and what artifacts already exist. The manifest is the answer to "where are we?" at any point in the session.
Iron Law
NO STAGE MAY CONTRADICT A DECISION RECORDED IN THE MANIFEST WITHOUT EXPLICIT USER APPROVAL.
If model-selection wants to change the primary metric that was declared in brainstorming, that change requires a human decision — not a silent override.
Manifest Structure
artifacts/analysis_manifest.json:
{
"project": "<analysis name>",
"created_at": "<ISO timestamp>",
"last_updated": "<ISO timestamp>",
"git_commit": "<hash at session start>",
"brainstorming": {
"completed": false,
"design_doc": null,
"hypotheses": [],
"primary_metric": null,
"baseline_expectation": null,
"validation_strategy": null
},
"data_profiling": {
"completed": false,
"profile_path": null,
"target_col": null,
"n_rows": null,
"n_cols": null,
"hidden_nulls_found": []
},
"data_exploration": {
"completed": false,
"eda_report_path": null,
"quality_score": null,
"leakage_candidates": []
},
"data_validation": {
"completed": false,
"tdds_report_path": null,
"decision": null
},
"leakage_guard": {
"completed": false,
"report_path": null,
"decision": null
},
"feature_engineering": {
"completed": false,
"registry_path": null,
"n_features": null,
"transformer_paths": []
},
"model_selection": {
"completed": false,
"chosen_model": null,
"baseline_score": null,
"best_cv_score": null,
"hpo_trials": null
},
"model_evaluation": {
"completed": false,
"test_evaluated": false,
"final_score": null,
"ci_lower": null,
"ci_upper": null,
"shap_path": null
},
"report": {
"completed": false,
"report_path": null
},
"warnings": [],
"human_approvals": []
}
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.
- 9d ago First seen · 252 lines · 45 tokens per session scan A 2773bde1b5c7
analysis-manifest is a skill published in the GitHub repository zpower426/datapowers (1 stars, last pushed 5mo ago), licensed MIT. It adds 45 tokens to every session and 2,289 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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