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 Chemaclass/agnostic-ai --skill gh-issuegit clone --depth 1 https://github.com/Chemaclass/agnostic-aiWrote 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/chemaclass/agnostic-ai/gh-issue)<a href="https://agentmods.dev/skills/chemaclass/agnostic-ai/gh-issue"><img src="https://agentmods.dev/badge/skills/chemaclass/agnostic-ai/gh-issue/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/chemaclass/agnostic-ai/gh-issue"><img src="https://agentmods.dev/badge/skills/chemaclass/agnostic-ai/gh-issue.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 149 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
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 | $0.00023 | $0.01700 |
| Opus 5 | $0.00012 | $0.00850 |
| Sonnet 5 | $0.00005 | $0.00340 |
| Haiku 4.5 | $0.00002 | $0.00170 |
Grade A, and why
gh-issue 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 9d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Issue Workflow
Context
Read both the issue body and every comment as requirements input. Maintainer follow-ups frequently add scope, edge cases, or override the original description; when a later comment conflicts with the body, prefer the comment.
!gh issue view ${ARGUMENTS#\#} --json number,url,title,body,labels,assignees,state,comments 2>/dev/null || echo "Provide an issue number"
Instructions
Phase 1: Setup
-
Parse the issue number from
$ARGUMENTS(strip#if present). -
Assign yourself if unassigned:
gh issue edit <number> --add-assignee @me -
Create a branch from fresh
origin/mainbased on the issue type:Determine the branch prefix from labels:
bug→fix/enhancement→feat/documentation→docs/- No label →
feat/(default)
Branch name format:
<prefix><issue-number>-<slug>git checkout main && git pull --ff-only git checkout -b <branch-name>
Phase 2: Plan
-
Enter Plan Mode to design the implementation:
- Explore the codebase to understand affected areas.
- Identify files that need changes.
- Respect adapter independence:
.claude/rules/no-cross-adapter-imports.md. - Honor the adapter skeleton:
.claude/rules/adapter-pattern.md. - Plan the TDD approach (what tests to write first).
-
Create implementation plan with:
- Summary of what the issue requires.
- List of files to create/modify.
- Test strategy (unit per package, integration under
tests/integration). - Step-by-step implementation order.
Phase 3: Implement
- After plan approval, implement following TDD:
- Write failing tests first (
*_test.gonext to the code under test). - Implement minimum code to pass.
- Refactor while keeping tests green.
- Wrap returned errors per
.claude/rules/error-wrapping.md. - Follow
.claude/rules/test-conventions.md(uset.TempDir(),testutil.Chdir, behavior-named tests).
- Write failing tests first (
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.
- 9d ago First seen · 169 lines · 23 tokens per session scan A 2a8752199aeb
gh-issue is a skill published in the GitHub repository Chemaclass/agnostic-ai (11 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 1,700 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.
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