issue_analyse_supervisor

issue_analyse_supervisor is a skill for Claude Code from MarcusJellinghaus/mcp-tools-py. It costs 11 tokens per session (1,568 once invoked), scanned A, original, MIT.

A supervisor-led workflow for analyzing a GitHub issue, a tracked software task, by delegating investigation to software-engineer agents. It records the analysis in a numbered Markdown log after reviewing the issue and project guidance.

In plain words
What is it for?
Use it when an issue needs codebase exploration, requirements analysis, and comparison with the repository’s engineering and planning principles before implementation.
Why use it?
It organizes issue investigation without mixing analysis with code changes or issue edits. The saved log makes the reasoning and findings available for later planning.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions CLAUDE.md; mentions subagents.

Good fit Use it when an issue needs codebase exploration, requirements analysis, and comparison with the repository’s engineering and planning principles before implementation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/marcusjellinghaus/mcp-tools-py/issue_analyse_supervisor
Install

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.

Any agent
npx skills add MarcusJellinghaus/mcp-tools-py --skill issue_analyse_supervisor
Clone the repo
git clone --depth 1 https://github.com/MarcusJellinghaus/mcp-tools-py

Made for: Claude Code.

Wrote 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.

agentmods badge for issue_analyse_supervisor

README.md
[![agentmods](https://agentmods.dev/badge/skills/marcusjellinghaus/mcp-tools-py/issue_analyse_supervisor.svg)](https://agentmods.dev/skills/marcusjellinghaus/mcp-tools-py/issue_analyse_supervisor)
Your own site
<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>
Per session 11 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,568 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 4d ago against content hash 127591b17ea7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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.

.claude/skills/issue_analyse_supervisor/SKILL.md · 81 lines

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:

  1. Resolve the issue number from $ARGUMENTS, the branch name, or .vscodeclaude_status.txt. If none found, ask the user.
  2. Read the GitHub issue (call mcp__mcp-workspace__github_issue_view with 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.
  3. Read the knowledge base files:
    • .claude/knowledge_base/software_engineering_principles.md
    • .claude/knowledge_base/planning_principles.md
  4. Do NOT create a branch. Issue analysis runs before an implementation branch exists — stay on the currently checked-out branch (typically main).
  5. Log: delete any existing pr_info/issue_analysis_log*.md, then create a single pr_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 ## Decisions section, 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:

  1. Launch a new engineer subagent → /issue_analyse with the issue number.
  2. Collect findings from the engineer: questions, feasibility concerns, implementation ideas, constraints.
  3. 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.
  4. Update the analysis log with this round's findings, decisions, and user answers.
  5. 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.
  6. 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.
  7. 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.
  8. Finalize:
    • Add a ## Final Status section 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-02 queues it for automated planning, which picks it up and moves it to status-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.

Read the full file on GitHub · 81 lines

Changes

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.

  1. 4d ago Changed · +1 lines 127591b17ea7
  2. 8d ago First seen · 80 lines · 11 tokens per session scan A bc5c623a475d

Subscribe to this mod's changes

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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