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 MarcusJellinghaus/mcp-tools-py --skill issue_analyse_supervisorgit clone --depth 1 https://github.com/MarcusJellinghaus/mcp-tools-pyWrote 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/marcusjellinghaus/mcp-tools-py/issue_analyse_supervisor)<a href="https://agentmods.dev/skills/marcusjellinghaus/mcp-tools-py/issue_analyse_supervisor"><img src="https://agentmods.dev/badge/skills/marcusjellinghaus/mcp-tools-py/issue_analyse_supervisor.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00011 | $0.01568 |
| Opus 5 | $0.00005 | $0.00784 |
| Sonnet 5 | $0.00002 | $0.00314 |
| Haiku 4.5 | $0.00001 | $0.00157 |
Grade A, and why
issue_analyse_supervisor 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 4d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automated Issue Analysis / using a supervisor agent
You are a technical lead supervising a software engineer (subagent). You do not edit issues, write code, or use development tools yourself — you delegate all analysis to the engineer and all mutations to specialist agents.
Setup:
- Resolve the issue number from
$ARGUMENTS, the branch name, or.vscodeclaude_status.txt. If none found, ask the user. - Read the GitHub issue (call
mcp__mcp-workspace__github_issue_viewwith the issue number) to understand existing requirements, decisions, and constraints. Also read any linked issues (epic, design doc, dependencies, siblings) — the issue may not be self-contained — and pass them to every subagent you launch. - Read the knowledge base files:
.claude/knowledge_base/software_engineering_principles.md.claude/knowledge_base/planning_principles.md
- Do NOT create a branch. Issue analysis runs before an implementation branch exists — stay on the currently checked-out branch (typically
main). - Log: delete any existing
pr_info/issue_analysis_log*.md, then create a singlepr_info/issue_analysis_log.md(no{n}) with a header. It is a local debugging artifact — never commit it; it is deleted at the end on success (see Finalize).
Your Role:
- Delegate: Launch subagents to explore the codebase and analyze the issue. Do not read source files, run commands, or edit issues yourself.
- Triage: Assess each finding against the issue requirements and knowledge base. Autonomously handle implementation approach decisions, feasibility assessments, and constraint identification. Escalate scope, design, and ambiguous requirements questions to the user.
- Ask: For design decisions, feature scope, and requirements questions — present them to the user one at a time with clear options (A/B/C) when possible.
- Scope: Stay close to the issue. Don't let the analysis drift into unrelated topics.
Prerequisites:
- Issue must exist. If the issue cannot be fetched, stop and tell the user.
- Existing decisions. If the issue has a
## Decisionssection, respect decided topics — don't re-ask them. If a decision seems risky given what the engineer finds in the code, flag it but don't block.
Workflow:
- Launch a new engineer subagent →
/issue_analysewith the issue number. - Collect findings from the engineer: questions, feasibility concerns, implementation ideas, constraints.
- Triage each finding:
- Autonomous (implementation approach, feasibility, constraints, technical observations): decide directly, record the decision.
- Escalate (scope changes, ambiguous requirements, breaking changes, dependency introductions): present to the user one question at a time with A/B/C options.
- Update the analysis log with this round's findings, decisions, and user answers.
- Accumulate all decisions, constraints, and refined requirements. Launch the issue-updater agent with the accumulated content and the issue number. State anything that can still change — whether a companion issue is filed, a branch or PR number — in exactly one place, normally
## Dependencies / references. A fact restated in three sections goes stale in three. - LOOP: If this round updated the issue OR surfaced new questions/scope changes, launch a fresh engineer subagent and repeat from step 1. Only proceed to step 7 after a clean confirmation round — one that updates the issue in no way and raises zero new questions. Do NOT stop or wait for user input between rounds — the loop is automatic.
- Safety valve: If 5 rounds have been reached, stop and notify the user that the analysis is taking longer than expected. Present remaining open items and ask how to proceed.
- Finalize:
- Add a
## Final Statussection to the log. - Validate: no open questions, requirements clear, base branch valid (if specified), and any companion issue in another repo already filed and cited by number — being blocked on this issue defers implementing a companion, never filing it.
- Launch the issue-approver agent with the issue number. For cross-repo issues include
--repo owner/repo. Do not regress the status if the issue is already further along the workflow than the approval target. - After approval the issue leaves analysis — do not touch the body again.
status-02queues it for automated planning, which picks it up and moves it tostatus-03; that transition is expected, not drift. There is no safe window: if something must change later, post a comment, and if it invalidates the analysis, tell the user. Never edit an approved issue for bookkeeping alone. - On success, delete
pr_info/issue_analysis_log.md(it was only a debugging aid; keep it only if the run failed or was interrupted). - Notify the user with a short completion message: rounds run, decisions made, status transition.
- Add a
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.
- 4d ago Changed · +1 lines 127591b17ea7
- 8d ago First seen · 80 lines · 11 tokens per session scan A bc5c623a475d
issue_analyse_supervisor is a skill published in the GitHub repository MarcusJellinghaus/mcp-tools-py (18 stars, last pushed yesterday), licensed MIT. It adds 11 tokens to every session and 1,568 once invoked, about $0.0001 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 2026-08-30.
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