Borrowing it
Nothing to install: this file belongs to VasiHemanth/tokentelemetry. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/VasiHemanth/tokentelemetry/main/.claude/skills/issue-brief/SKILL.mdgit clone --depth 1 https://github.com/VasiHemanth/tokentelemetryWrote 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/vasihemanth/tokentelemetry/issue-brief)<a href="https://agentmods.dev/skills/vasihemanth/tokentelemetry/issue-brief"><img src="https://agentmods.dev/badge/skills/vasihemanth/tokentelemetry/issue-brief/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/vasihemanth/tokentelemetry/issue-brief"><img src="https://agentmods.dev/badge/skills/vasihemanth/tokentelemetry/issue-brief.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 | $0.00195 | $0.01508 |
| Opus 5 | $0.00097 | $0.00754 |
| Sonnet 5 | $0.00039 | $0.00302 |
| Haiku 4.5 | $0.00019 | $0.00151 |
Grade A, and why
issue-brief 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
issue-brief — explain an issue before building it
The output is an explanation, not a design doc and not a diff. Assume the reader has not read the reporter's post and does not have the file layout in their head. Lead with the plain meaning; the architecture comes fourth.
1. Fetch the thing
Issues:
gh issue view <number-or-url> --json number,title,state,author,createdAt,closedAt,labels,body,comments
Discussions need GraphQL. There is no gh discussion command (verified on
gh 2.92), and gh issue view will not resolve a discussion number. Use:
gh api graphql -f query='
{ repository(owner:"VasiHemanth", name:"tokentelemetry") {
discussion(number: NNN) {
number title url category{name} author{login} createdAt body
comments(first:20){ nodes { author{login} body } }
} } }'
Then, before writing anything:
- Check it isn't already done. Grep the codebase for the feature's nouns.
Issue 135 (Pi agent support) was closed and fully shipped; the tell was
PI_SESSIONS_DIRandtest_pi_scan.pyalready sitting on main. - Search for the same root cause elsewhere, open and closed
(
gh issue list --search). Issues 198 and 96 were the same two-port problem reported twice, a year apart, by different people. - Check open PRs that touch the files you'd touch (
gh pr list). They set the landing order.
2. Answer in this order
- What the reporter actually wants, in one or two sentences of plain language, jargon stripped. Name them. Link the earlier issue or PR if it's a repeat.
- A concrete walkthrough of the problem. Pick one realistic setup and trace it as numbered steps, with real hostnames, real ports, real endpoints. Never "suppose a user does X". Say what the user sees first, then why it happens.
- How it works today, only the part that bears on the issue, with
file.py:lineanchors so the claim can be checked. - What implementing it would take: the shape of the change and which layer it belongs in, not a full diff.
- Traps and interactions. Security regressions, open PRs on the same files, closed issues with the same root cause, what has to land first.
- A verdict and one next step ("worth accepting, want me to draft the reply comment?").
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 · 121 lines · 195 tokens per session scan A a9b0be692c3c
issue-brief is a skill published in the GitHub repository VasiHemanth/tokentelemetry (344 stars, last pushed yesterday), licensed MIT. It adds 195 tokens to every session and 1,508 once invoked, about $0.0010 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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