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 hoangsonww/Claude-Code-Agent-Monitor --skill delegation-auditgit clone --depth 1 https://github.com/hoangsonww/Claude-Code-Agent-MonitorWrote 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/hoangsonww/claude-code-agent-monitor/delegation-audit)<a href="https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/delegation-audit"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/delegation-audit/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/hoangsonww/claude-code-agent-monitor/delegation-audit"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/delegation-audit.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 | $0.00071 | $0.00611 |
| Opus 5 | $0.00036 | $0.00305 |
| Sonnet 5 | $0.00014 | $0.00122 |
| Haiku 4.5 | $0.00007 | $0.00061 |
Grade A, and why
delegation-audit 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 9d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Delegation Audit
Audit how a Claude Code session delegated work: model-to-subagent mapping and whether each delegation paid off.
Input
The user provides: $ARGUMENTS
A session ID. If empty, fetch GET /api/sessions?limit=1 and audit the most recent session, stating which one.
Data Sources
| Endpoint | Returns |
|---|---|
GET /api/workflows/{sessionId} |
The modelDelegation dataset (which models are delegated which subagent types) and the effectiveness dataset (per-type completion/success rate, avg duration, task success) |
GET /api/agents |
Raw subagent records (type, model, status, depth, parent) to corroborate counts and statuses |
Report Sections
1. Delegation Matrix
From modelDelegation: a model × subagent-type table of how many agents of each type each model ran.
| Model | explore | code-review | debugger | ... | Total |
|---|
2. Effectiveness by Subagent Type
From effectiveness: per type, the success rate and average duration.
| Subagent type | Count | Success rate | Avg duration | Verdict |
|---|---|---|---|---|
| Mark types below ~70% success as low-yield. |
3. Wasted Delegations
Flag, with evidence:
- A heavy model (e.g. Opus) assigned to a simple/low-stakes subagent type that a cheaper model handled successfully elsewhere — candidate for rebalancing.
- Subagent types with low success rates (effort spent, task not completed).
- Duplicate delegations: the same type spawned repeatedly with poor success (retry churn).
4. Rebalancing Suggestions
Concrete model reassignments grounded in the matrix and effectiveness data. State the type, the model used, the success rate, and the suggested model — only where the data supports it.
Output
- Markdown tables for the matrix and effectiveness.
- Success rates as percentages; durations in human units (e.g.
1m 12s). - Use ▲/▼ when comparing a type's success rate against the session-wide average.
- Cite only numbers returned by the API; do not infer success rates that the
effectivenessdataset does not provide. - If the dashboard is unreachable, tell the user to start it with
npm startfrom the repo root.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 57 lines · 71 tokens per session scan A 92ea6b0bc480
delegation-audit is a skill published in the GitHub repository hoangsonww/Claude-Code-Agent-Monitor (991 stars, last pushed 4d ago), licensed MIT. It adds 71 tokens to every session and 611 once invoked, about $0.0004 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 2026-09-03.
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