my-claude-code-setup is a starter configuration and memory-bank template for Claude Code, providing project guidance and reusable setup for Claude Code sessions. Developers use it to organize Claude Code behavior, memory, commands, skills, subagents, hooks, and settings. The catalogue entries are components of this Claude Code workflow.
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 centminmod/my-claude-code-setup --skill task-breakdowngit clone --depth 1 https://github.com/centminmod/my-claude-code-setupWrote 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/centminmod/my-claude-code-setup/task-breakdown)<a href="https://agentmods.dev/skills/centminmod/my-claude-code-setup/task-breakdown"><img src="https://agentmods.dev/badge/skills/centminmod/my-claude-code-setup/task-breakdown/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/centminmod/my-claude-code-setup/task-breakdown"><img src="https://agentmods.dev/badge/skills/centminmod/my-claude-code-setup/task-breakdown.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00177 | $0.02427 |
| Opus 5.5 | $0.00071 | $0.00971 |
| Sonnet 5.5 | $0.00035 | $0.00485 |
| Haiku 4.5 | $0.00018 | $0.00243 |
Grade A, and why
task-breakdown 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- task-breakdown — 89% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Breakdown
Turns a session's per-request breakdown (the deterministic request_units
emitted by session-metrics) into semantic tasks the user actually thinks
in — "added auth", "debugged the cache miss" — and labels each with a verdict.
You do the one thing deterministic code can't: decide which requests belong to
the same task. The script does everything else (cost, turns, tokens, waste
signals, the themed page).
Model. This skill runs on your session's current model. It no longer pins
one (a hard model: pin ran the inline turn on that model, dragging the whole
conversation into that model's context window — on a long session that
overflowed and broke invocation). The grouping + verdict work is
judgement-heavy, so it wants a capable model; for a cheaper run that's still
strong enough, /model sonnet before invoking. Don't drop to Haiku — the
semantic verdicts need the headroom.
Division of labour — do not blur it:
- The export owns the numbers. Every cost / turn / token / waste figure
comes from
request_unitsin the JSON export. You MUST NOT sum money or invent figures —--render-tasksrecomputes all totals from the export. - You own the grouping + labels only. You assign each
request_unit_idto a task, write a short title, a verdict, and a one-line rationale.
Inputs
$ARGUMENTS[0] (optional) = path to a session-metrics JSON export, e.g.
exports/session-metrics/session_<id8>_<ts>.json (session scope is the primary
target; project_*.json also works — units carry a session_id). The export
must contain a request_units array.
If $ARGUMENTS[0] is missing, first generate a session export by invoking the
session-metrics skill (or run its script) for the session of interest with
--output json html, then use the written session_*.json path.
Steps
- Locate the export and the renderer.
- Export:
$ARGUMENTS[0], or the JSON you just generated. - Renderer: the sibling session-metrics skill's script. Resolve its
path (it ships in the same plugin):
- plugin install:
../session-metrics/scripts/session-metrics.py - dev repo:
.claude/skills/session-metrics/scripts/session-metrics.pyUse whichever exists (glob if unsure).
- plugin install:
- Export:
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.
- 1mo ago First seen · 183 lines · 177 tokens per session scan A 948ee59d6ebd
task-breakdown is a skill published in the GitHub repository centminmod/my-claude-code-setup (2,671 stars, last pushed 10d ago), licensed MIT. It adds 177 tokens to every session and 2,427 once invoked, about $0.0007 per session on Opus 5.5. 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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