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 finishing-an-analysis-branchgit 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/finishing-an-analysis-branch)<a href="https://agentmods.dev/skills/zpower426/datapowers/finishing-an-analysis-branch"><img src="https://agentmods.dev/badge/skills/zpower426/datapowers/finishing-an-analysis-branch/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/finishing-an-analysis-branch"><img src="https://agentmods.dev/badge/skills/zpower426/datapowers/finishing-an-analysis-branch.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 | $0.00041 | $0.01581 |
| Opus 5 | $0.00020 | $0.00790 |
| Sonnet 5 | $0.00008 | $0.00316 |
| Haiku 4.5 | $0.00004 | $0.00158 |
Grade A, and why
finishing-an-analysis-branch 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 11d 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 — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Finishing an Analysis Branch
Overview
Guide completion of analysis work by verifying artifacts, presenting structured delivery options, and executing the chosen workflow.
Core principle: Verify artifacts → Present options → Execute choice → Clean up.
Announce at start: "I'm using the finishing-an-analysis-branch skill to complete this work."
The Process
Step 1: Verify Artifacts and Validation
Before presenting options, verify the analysis is complete:
from pathlib import Path
import json
required_artifacts = [
"artifacts/analysis_manifest.json",
"artifacts/data_profile.md",
]
for path in required_artifacts:
if not Path(path).exists():
print(f"MISSING: {path}")
else:
print(f"OK: {path}")
# Check manifest for incomplete stages
manifest = json.loads(Path("artifacts/analysis_manifest.json").read_text())
incomplete = [
k for k, v in manifest.items()
if isinstance(v, dict) and not v.get("completed")
and k not in ["warnings", "human_approvals"]
]
if incomplete:
print(f"WARNING: Incomplete stages: {incomplete}")
If required artifacts are missing:
Analysis incomplete. Missing artifacts:
[List missing files]
Cannot deliver until analysis stages are complete.
Stop. Don't proceed to Step 2.
If all artifacts present: Continue to Step 2.
Step 2: Summarize Analysis State
Read the manifest and produce a brief summary:
import json
from pathlib import Path
manifest = json.loads(Path("artifacts/analysis_manifest.json").read_text())
print(f"Project: {manifest['project']}")
print(f"Primary metric: {manifest['brainstorming'].get('primary_metric')}")
print(f"Final score: {manifest['model_evaluation'].get('final_score')}")
print(f"Warnings: {manifest.get('warnings', [])}")
Report any unresolved warnings before offering delivery options.
Step 3: Present Options
Present exactly these 4 options:
Analysis complete. What would you like to do?
1. Commit artifacts and merge to main branch locally
2. Push branch and create a Pull Request
3. Keep the branch as-is (I'll handle delivery later)
4. Archive this analysis (save artifacts, discard branch work)
Which option?
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.
- 11d ago First seen · 245 lines · 41 tokens per session scan A 2da67bff4cb2
finishing-an-analysis-branch is a skill published in the GitHub repository zpower426/datapowers (1 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 1,581 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 2026-08-31.
Other skills, from other repositories
personal-guide-start
A starter workflow that creates a private GitHub repository for a personal development guide and fills it with six starter files.
lesson-close
A workflow for finishing the day's lesson file, recording its status and duration, then saving it to the personal-guide GitHub repository.
spec-kitty-git-workflow
Understand how Spec Kitty manages git: what git operations Python handles automatically, what agents must do manually, worktree lifecycle, auto-commit behavior, merge execution, and the safe-commit pattern. Triggers: "how does spec-kitty use git", "worktree management", "auto-commit", "who commits what", "git…
merge-strategy
Git merge strategies, conflict resolution approaches, merge vs rebase recommendations, and branch integration patterns in sidecar. Covers pull strategy menu, direct merge workflow, squash merge, commit message templates, configurable defaults, and protected branches. Use when working on git merge features or making…
worktree-switching
Git worktree support in sidecar: worktree detection, switching between worktrees, worktree state management, and plugin reinitialization. Covers the full lifecycle of worktree context switching including registry reinit, per-worktree state persistence, deleted worktree detection and fallback. Use when working on git…
spk-admin-git-workflow
Operate Spec Kitty git workflows, worktrees, safe commits, merge preflights, stale state checks, and recovery.