Backlog Grooming

Install

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.

agentmods

npx agentmods add agents/microsoft/hve-core/backlog-grooming

Clone the repo

git clone --depth 1 https://github.com/microsoft/hve-core

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/agents/microsoft/hve-core/backlog-grooming.svg)](https://agentmods.dev/agents/microsoft/hve-core/backlog-grooming)

Your own site

<a href="https://agentmods.dev/agents/microsoft/hve-core/backlog-grooming"><img src="https://agentmods.dev/badge/agents/microsoft/hve-core/backlog-grooming.svg" alt="Measured on agentmods" height="20"></a>

Per session 24 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,640 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00024	$0.01640
Opus 5	$0.00012	$0.00820
Sonnet 5	$0.00005	$0.00328
Haiku 4.5	$0.00002	$0.00164

Measured yesterday against content hash f5ead0e92259, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

.github/agents/backlog-grooming.agent.md · 166 lines

How it starts

The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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, or Uncertain outcome 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.
Record the stop reason and 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.

When candidate validation, repository access, or required evidence is unavailable, report the missing evidence and use the fail-closed noop path defined by the calling workflow. A fail-closed noop does not emit or advance report cursor state. Do not invent candidate, assessment, or cursor state.

Read the full file on GitHub · 166 lines

Changes

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.

yesterday First seen · 166 lines · 24 tokens per session scan A f5ead0e92259

Subscribe to this mod's changes

Backlog Grooming is an agent published in the GitHub repository microsoft/hve-core (1,422 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 1,640 once invoked, about $0.0001 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 2026-09-02.