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 skills/makigjuro/cloudstack-ai-plugins/task-checknpx skills add makigjuro/cloudstack-ai-plugins --skill task-checkgit clone --depth 1 https://github.com/makigjuro/cloudstack-ai-pluginsWhat 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 | $0.00032 | $0.01667 |
| Opus 5 | $0.00016 | $0.00834 |
| Sonnet 5 | $0.00006 | $0.00333 |
| Haiku 4.5 | $0.00003 | $0.00167 |
Grade A, and why
task-check scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Check (QA Verification)
Verify that the implementation on the current branch satisfies the requirements defined in the linked GitHub issue. This is a QA gate that ensures we built what was asked for.
Arguments
{issue}-- GitHub issue number (optional, extracted from branch name if not provided)--strict-- Fail on any unmet criterion (default: fail only on "Must Have" criteria)
Configuration
Auto-detect the GitHub repository from git remote -v (extract owner and repo name). If cloudstack.json exists, it may contain a project.repository field as a fallback.
Process
Step 1: Identify the Issue
# Get current branch
BRANCH=$(git branch --show-current)
# Extract issue number from branch name (e.g., username/42-feature-name)
ISSUE=$(echo "$BRANCH" | grep -oE '/[0-9]+' | tr -d '/')
# If no issue in branch name and not provided as argument, fail
if [ -z "$ISSUE" ]; then
echo "ERROR: Could not determine issue number. Provide as argument: /task-check 42"
exit 1
fi
# Auto-detect repo from git remote
REPO_URL=$(git remote get-url origin)
OWNER=$(echo "$REPO_URL" | sed -E 's#.*[:/]([^/]+)/[^/]+(\.git)?$#\1#')
REPO=$(echo "$REPO_URL" | sed -E 's#.*[:/][^/]+/([^/]+?)(\.git)?$#\1#')
Step 2: Fetch Issue Details
Use MCP for structured issue data:
mcp__github-mcp-server__get_issue(owner: "${OWNER}", repo: "${REPO}", issue_number: $ISSUE)
This returns structured JSON with number, title, body, labels, state, and comments -- no CLI output parsing needed.
Step 3: Parse Acceptance Criteria
Extract acceptance criteria from the issue body. Look for:
- Checkbox lists:
- [ ] Criterion - "Acceptance Criteria" section
- "Requirements" section
- "Definition of Done" section
- User stories with "so that" format
Categorize each criterion:
- Must Have: Explicitly marked as required, or in "Acceptance Criteria"
- Should Have: Listed but not critical
- Nice to Have: Suggestions, future improvements mentioned
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.
- 2d ago First seen · 237 lines · 32 tokens per session scan A 204f4d1eb82a
task-check is a skill published in the GitHub repository makigjuro/cloudstack-ai-plugins (1 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 1,667 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.
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