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 skills add pjt222/agent-almanac --skill create-github-issuesgit clone --depth 1 https://github.com/pjt222/agent-almanacWrote 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/pjt222/agent-almanac/create-github-issues)<a href="https://agentmods.dev/skills/pjt222/agent-almanac/create-github-issues"><img src="https://agentmods.dev/badge/skills/pjt222/agent-almanac/create-github-issues/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/pjt222/agent-almanac/create-github-issues"><img src="https://agentmods.dev/badge/skills/pjt222/agent-almanac/create-github-issues.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 | $0.00057 | $0.01747 |
| Opus 5 | $0.00028 | $0.00873 |
| Sonnet 5 | $0.00011 | $0.00349 |
| Haiku 4.5 | $0.00006 | $0.00175 |
Grade A, and why
create-github-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 7d 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create GitHub Issues
Structured GitHub issue creation from review findings or task breakdowns. Converts a list of findings (from review-codebase, security-audit-codebase, or manual analysis) into well-formed GitHub issues with labels, acceptance criteria, and cross-references.
When to Use
- After a codebase review produces a findings table that needs tracking
- After a planning session identifies work items that should become issues
- When converting a TODO list or backlog into trackable GitHub issues
- When batch-creating related issues that need consistent formatting and labeling
Inputs
- Required:
findings— a list of items, each with at minimum a title and description. Ideally also includes: severity, affected files, and suggested labels - Optional:
group_by— how to batch findings into issues:severity,file,theme(default:theme)label_prefix— prefix for auto-created labels (default: none)create_labels— whether to create missing labels (default:true)dry_run— preview issues without creating them (default:false)
Procedure
Step 1: Prepare Labels
Ensure all needed labels exist in the repository.
- List existing labels:
gh label list --limit 100 - Identify labels needed by the findings (from severity, phase, or explicit label fields)
- Map severities to labels if not already mapped:
critical,high-priority,medium-priority,low-priority - Map phases/themes to labels:
security,architecture,code-quality,accessibility,testing,performance - If
create_labelsis true, create missing labels:gh label create "name" --color "hex" --description "desc" - Use consistent colors: red for critical/security, orange for high, yellow for medium, blue for architecture, green for testing
Got: All labels referenced by findings exist in the repository. No duplicate labels created.
If fail: If gh CLI is not authenticated, instruct the user to run gh auth login. If label creation is denied (insufficient permissions), proceed without creating labels and note which labels are missing.
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.
- 7d ago First seen · 152 lines · 57 tokens per session scan A 7668127d931c
create-github-issues is a skill published in the GitHub repository pjt222/agent-almanac (32 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 1,747 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
review-spec
Independent read-only review of a frozen Product half before engineering planning. Runs the exact Product checks in a clean context and returns only SPEC-REVIEW-PASS, SPEC-REVIEW-FAIL, or NEEDS-DESIGN with a content-bound receipt. Never edits the reviewed SPEC. Triggers: "review-spec", "review the spec", "review…
ship-roadmap
Found or continue a roadmap autopilot one stage per invocation. Default: human merge. --fullauto is invocation-scoped and uses the transient wrapper only after a fresh audit. Triggers: "ship-roadmap", "ship the roadmap", "autopilot this project".
workflow-status
Read-only workflow sensor: compute repository, roadmap, dependency, PR, finding, and recovery state, then emit the fixed machine envelope. Never edits. Triggers: "workflow-status", "workflow status", "what can I build next", "state of the run".
cocoharvest
Decompose an approved plan into parallel workstreams, assign specialist personas, classify stages as HITL or AFK (CocoLens), generate flow.json stages with checkpoints and dual-file state, and create per-stage prompt files. Includes adaptive parallelism, stall detection, shell identity injection, and consecutive…
plan
Enter the Plan phase of CocoBrew. Runs CocoSpec quality gate pre-flight, reads spec.md and discuss.md (if present), invokes Coco native plan mode as a mandatory gate, captures the approved plan to plan.md, creates initial flow.json template, and commits. Must have $spec completed first.
lean-debt
CocoLean shortcut debt ledger — harvests cocoplus: comment markers across the CocoPod and produces a ranked deferral report sorted by ceiling imminence and age.