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 commands/blendsdk/claude-codeops/gh_issuesgit clone --depth 1 https://github.com/blendsdk/claude-codeopsWhat 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 | $0.00092 | $0.01847 |
| Opus 5 | $0.00046 | $0.00924 |
| Sonnet 5 | $0.00018 | $0.00369 |
| Haiku 4.5 | $0.00009 | $0.00185 |
Grade A, and why
gh_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 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.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gh_issues — adaptive GitHub issues table
Render a repo's issues as one markdown table. The semantic columns (Type, Priority, Effort) are resolved through the repo's own scheme — its labels, native issue types, and project fields — discovered fresh on every run. Never impose a label convention on a repo; its vocabulary is the truth.
Strictly read-only. Never create, edit, close, comment on, or label anything. gh api graphql is used for read queries only — no mutations, ever.
Flags
| Flag | Meaning |
|---|---|
--state <open|closed|all> |
issue state filter (default open) |
--label <name> |
filter by label (repeatable) |
--assignee <login> |
filter by assignee |
--mine |
shorthand for --assignee @me |
--author <login> |
filter by author |
--milestone <m> |
filter by milestone |
--search "<q>" |
GitHub search-syntax query |
--limit <n> |
max issues fetched (default: gh's own default, 30) |
--repo owner/repo |
target repo (default: the current directory's repo) |
--type <t> |
semantic filter through the discovered type scheme |
--priority <p> |
semantic filter through the discovered priority scheme |
--sort <priority|effort|updated|number> |
sort key (default priority) |
--no-deps |
skip dependency detection entirely (no GraphQL call) |
Protocol
- Preflight. Run
gh auth status. Ifghis missing or unauthenticated, stop with an actionable hint (install: https://cli.github.com · authenticate:gh auth login) — never render a partial table. Resolve the target repo:--repo owner/repowhen given, otherwise the current directory's repo viagh repo view. Not inside a repo and no--repo→ stop and ask for--repo owner/repo. - Flag validation.
--limitmust be numeric — reject with a usage line otherwise. An unknown--sortkey → error listing the valid keys. Every user-supplied string (labels, search query, milestone, logins…) is passed toghas a quoted argument — never interpolated unquoted, never througheval. - Scheme discovery. One
gh label list --json name,description --limit 100call. Classify the label families heuristically at runtime:- priority-ish — e.g.
P1,prio/high,priority: high,critical - type-ish — e.g.
bug,enhancement,type: feature,docs - effort-ish — e.g.
size/M,effort: 3,XL, t-shirt sizes Nothing is hardcoded: classify whatever this repo actually uses, and derive each family's internal ranking from its own naming (P1outranksP2;highoutrankslow; …).
- priority-ish — e.g.
- Fetch. One
gh issue list --json number,title,labels,assignees,milestone,body,state,updatedAtcall, passing through every native filter flag the user gave (--state --label --assignee --author --milestone --search --limit);--mineexpands to--assignee @me. - Relations & types (skipped entirely under
--no-deps). One batched GraphQL query fetching, for the listed issues: the native issue-type name (when the repo/org has issue types) and parent/sub-issue ("tracked-by") relations; include the search's total issue count when the result may be truncated (footer, below). Combine relations with a body-marker scan —Depends on #N,Blocked by #N,Depends: #N, all case-insensitive. If the GraphQL query fails, degrade to body-marker-only detection and say so in a notice. - Semantic filtering.
--type <t>and--priority <p>match through the discovered scheme —--priority highmatchesP1in aP1/P2/P3repo, with a mapping notice ("--priority high → P1"). A value not present in the scheme → error listing the values that DO exist. A scheme family absent entirely → one notice line and the flag is ignored. - Render per the rules below, including notices and the truncation footer.
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 · 120 lines · 92 tokens per session scan A e7763f0b2a28
gh_issues is a command published in the GitHub repository blendsdk/claude-codeops (4 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 1,847 once invoked, about $0.0005 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.