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/sembraniteam/claude-plugins/reportnpx skills add sembraniteam/claude-plugins --skill reportgit clone --depth 1 https://github.com/sembraniteam/claude-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 | $0.00126 | $0.00641 |
| Opus 5 | $0.00063 | $0.00320 |
| Sonnet 5 | $0.00025 | $0.00128 |
| Haiku 4.5 | $0.00013 | $0.00064 |
Grade A, and why
report 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 3d 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Report
Role-Tailored Performance Report Generation
Generate a structured performance report adapted to the target audience.
Step 1: Gather Findings
Read the performance findings from the current conversation context. If no /perfmind:investigate session has been run, prompt the user to share key findings first or invoke /perfmind:investigate before proceeding. If a performance-analyst agent ran earlier in the conversation, findings may carry a Confirmed/Rejected/Unverified verdict — preserve that verdict into the report; see the "Unverified" note in references/role-templates.md for how each role template handles it.
Step 2: Detect Target Role
If the user passes a role argument, use it directly. Otherwise, detect the role using the signal-word table in references/role-templates.md — that file is the single source of truth for role detection; do not keep a separate copy of the table here.
Step 3: Generate Role-Specific Report
Generate the report using the full template for the detected role from references/role-templates.md, including its stated focus, tone, and length constraints per role — that file is the single source of truth for these facts; do not restate or summarize them here.
Follow the Anti-Fabrication Rule in references/role-templates.md — every number must trace back to a finding actually stated in the conversation, with extra force for the Leadership template's numeric examples (illustrative placeholders only, never to be copied as-is).
After generating the report, output: "Share more evidence via /perfmind:investigate to refine these recommendations."
Additional Resources
references/role-templates.md— Single source of truth for role-detection signals and full markdown templates for all four roles, with section-by-section guidance and audience notesexamples/developer-report.md— Complete worked example: findings from an investigation session rendered as a Developer report
What ships with it
2 files 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.
- 3d ago First seen · 39 lines · 126 tokens per session scan A 686f2273b2e5
report is a skill published in the GitHub repository sembraniteam/claude-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 126 tokens to every session and 641 once invoked, about $0.0006 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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