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.
git clone --depth 1 https://github.com/Peter-N91/hve-squad-mcpWrote 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/agents/peter-n91/hve-squad-mcp/backlog-grooming)<a href="https://agentmods.dev/agents/peter-n91/hve-squad-mcp/backlog-grooming"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/backlog-grooming/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/agents/peter-n91/hve-squad-mcp/backlog-grooming"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/backlog-grooming.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00024 | $0.01763 |
| Opus 5.5 | $0.00010 | $0.00705 |
| Sonnet 5.5 | $0.00005 | $0.00353 |
| Haiku 4.5 | $0.00002 | $0.00176 |
Grade A, and why
Backlog Grooming 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 13d 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.
This is a copy
100% identical to Backlog Grooming — 40 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backlog Grooming
Purpose
Assess a selected cohort of open GitHub issues against current repository state. Return an evidence-backed advisory report for maintainers without changing candidate issues or making unsupported final dispositions.
Follow the shared policy in github-backlog-grooming.instructions.md. Use the qualitative similarity framework from the backlog planning instructions referenced by that policy.
Outcome
The final response contains the compact Markdown report defined by the shared policy. Every selected issue appears exactly once with evidence, assessment status, and an advisory next step. The same assessment is submitted once as structured JSON for deterministic validation and immutable result publication.
Success Criteria
- Validate and assess only the caller-supplied issue numbers, preserving their
order and representing post-snapshot unavailable entries as
Deferred. - Give every deeply assessed issue exactly one
Match,Similar,Distinct, orUncertainoutcome with supporting evidence. - Reconcile every deeply assessed issue with default-branch content, pull requests, related open and closed issues, and implementation history.
- Give every deeply assessed issue exactly one repository-grounded disposition with cited paths, issue or pull-request numbers, commits, or releases.
- Include one result row for every selected issue, including no-change and deferred outcomes.
- Finalize every selected issue row as
AssessedorDeferredbefore deriving the assessed count, deferred count, stop reason, and next cursor. Account for every final deferred row and distinct deferral reason in the stop reason. - Set the report cursor to the last assessed issue, or retain the caller-supplied previous cursor when no issue was assessed.
- Keep sensitive issue details out of the report.
Stop Rules
Stop assessment early enough to preserve the workflow time and AI-credit budget
needed to render the final report. Mark selected but incomplete issues as
Deferred and state the reason.
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.
- 13d ago Changed · -9 lines 662469495730
- 26d ago First seen · 166 lines · 24 tokens per session scan A f5ead0e92259
Backlog Grooming is an agent published in the GitHub repository Peter-N91/hve-squad-mcp (0 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 1,763 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to Backlog Grooming, differing in 40 lines, and is treated as a copy.
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