issues

A command that reviews open GitHub issues, identifies the ones marked ready for agents, and sends them through an automated issue-solving workflow. GitHub issues are tasks or bug reports attached to a repository.

In plain words
What is it for?
It is for triaging issues, filtering by labels, marking work in progress, and delegating agent-ready issues for implementation and merging.
Why use it?
It reduces the manual work of sorting a large issue list and ensures the automated workflow focuses on explicitly approved tasks.

Command

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 commands/bjcoombs/ai-native-toolkit/issues
Clone the repo
git clone --depth 1 https://github.com/bjcoombs/ai-native-toolkit
Per session 23 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,626 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.

ModelPer sessionOnce invoked
Fable 5 $0.00023 $0.01626
Opus 5 $0.00012 $0.00813
Sonnet 5 $0.00005 $0.00325
Haiku 4.5 $0.00002 $0.00163

Measured 2d ago against content hash 2096e1de65ae, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

issues 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 2d 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.

commands/issues.md · 150 lines

How it starts

The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GitHub Issue Marathon

Thin orchestrator. Triages open issues, then delegates execution of agent-ready issues to the marathon skill (same engine as /tm).

Configuration

Read the repo's CLAUDE.md ## Marathon Configuration (GitHub Issues subsection) for label names, with defaults:

  • Agent-ready label: agent-ready
  • Needs-triage label: needs-triage
  • In-progress label: in-progress
  • Issue exclude labels: (none)

Also read base branch, required approvals, and bot-reviewer rules (shared with /tm).

Phase 0: Capability Detection

echo $CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS   # set $TEAMS_AVAILABLE (true if "1")

Routing

ORG=$(gh repo view --json owner --jq '.owner.login')
REPO=$(gh repo view --json name --jq '.name')
# Optional scope filter from $ARGUMENTS — narrows the issue universe; routing still applies within it.
SCOPE_LABEL="$ARGUMENTS"   # empty = all open issues
FILTER=(); [ -n "$SCOPE_LABEL" ] && FILTER=(--label "$SCOPE_LABEL")
READY=$(gh issue list --label "agent-ready" "${FILTER[@]}" --state open --json number --jq 'length')
  • READY > 0Marathon mode. Work ONLY the agent-ready issues. Do NOT assess or modify untagged issues — the human has curated the queue by tagging.
  • READY == 0Triage mode (below).

Triage Mode (no agent-ready issues exist)

Enumerate open issues minus the exclude labels:

gh issue list "${FILTER[@]}" --state open --json number,title,body,labels | \
  jq '[.[] | select((.labels[].name) as $l | ($l | IN("<exclude-labels>")) | not)]'

If $ARGUMENTS (a scope label) was given, only issues carrying it are considered; routing still applies within that subset.

For each issue, assess whether it is actionable as-is (clear scope, acceptance criteria inferable, no open question):

  • Clear enough → add the agent-ready label: gh issue edit <N> --add-label "agent-ready"
  • Ambiguous → post clarifying questions as a comment, then label needs-triage:
    gh issue comment <N> --body "$(cat <<'EOF'
    Triage questions before this can be picked up by an agent:
    1. <question>
    2. <question>
    EOF
    )"
    gh issue edit <N> --add-label "needs-triage"
    

Read the full file on GitHub · 150 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.

  1. 2d ago First seen · 150 lines · 23 tokens per session scan A 2096e1de65ae

Subscribe to this mod's changes

issues is a command published in the GitHub repository bjcoombs/ai-native-toolkit (30 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 1,626 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 2026-08-30.