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 strikersam/autonomous-ai-agency --skill ticket-to-prgit clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote 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/strikersam/autonomous-ai-agency/ticket-to-pr)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/ticket-to-pr"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/ticket-to-pr/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/strikersam/autonomous-ai-agency/ticket-to-pr"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/ticket-to-pr.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 | $0.00000 | $0.00798 |
| Opus 5 | $0.00000 | $0.00399 |
| Sonnet 5 | $0.00000 | $0.00160 |
| Haiku 4.5 | $0.00000 | $0.00080 |
Grade A, and why
ticket-to-pr 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 12d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: ticket-to-pr
Purpose
Transform a GitHub issue (ticket) into a complete, merged pull request. End-to-end automation from reading the issue to pushing working, tested code.
Trigger
Use when:
- Given an issue number or URL to implement
- Told "implement issue #N"
- A GitHub issue is linked in the task
Process
Step 1: Parse the Issue
Read the issue title, body, labels, and comments. Extract:
- What: The feature/fix being requested
- Why: The motivation or user need
- Acceptance Criteria: Explicit or implied success conditions
- Constraints: Any technical requirements or limitations mentioned
- Context: Related issues, PRs, or discussions referenced
If acceptance criteria are not explicit, derive them from the description and list them out for confirmation.
Step 2: Context Prime
Run the context-prime skill to ensure full codebase understanding before writing code.
Focus particularly on:
- Files most likely affected by this change
- Existing tests for related functionality
- Any TODO comments or known issues in relevant areas
Step 3: Plan the Implementation
Before writing code, produce a plan:
## Implementation Plan for #[N]: [title]
### Files to Create
- path/to/new_file.py — [purpose]
### Files to Modify
- path/to/existing.py — [what changes and why]
### Tests to Add
- tests/test_feature.py — [what scenarios to cover]
### Acceptance Criteria Checklist
- [ ] [criterion 1]
- [ ] [criterion 2]
Step 4: Test-First Implementation
For each acceptance criterion:
- Write the test first (failing)
- Write the minimal implementation to pass it
- Refactor if needed
- Confirm test passes
Use test-first-executor skill for complex features.
Step 5: Run Full Validation
# All of these must pass:
- Unit tests
- Integration tests (if applicable)
- Lint/type checks (use auto-fix skill first)
- Any CI checks that can be run locally
Step 6: Commit with Smart-Commit
Use the smart-commit skill to create a well-structured commit:
- Reference the issue number in commit message
- Follow conventional commits format
- Include
Closes #Nin commit body
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.
- 12d ago First seen · 114 lines · 0 tokens per session scan A c9fadc96ee7d
ticket-to-pr is a skill published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 798 tokens. 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 skills, from other repositories
audit-trail
Full traceability from PRD to code commit through the CCPM spec-driven pipeline.
github-sync
Bidirectional synchronization of epics and tasks with GitHub issues, labels, and relationships.
issue-tracking
Track beads as git-backed issues with persistent attribution, supporting Gas Town's bead lifecycle and convoy progress monitoring.
pr-shepherding
Monitor PR lifecycle from creation through merge including CI monitoring, review comment handling, thread resolution, and merge readiness verification.
agent-sync-coordinator
Agent skill for sync-coordinator - invoke with $agent-sync-coordinator.
pipeline-conductor
Use when a pipeline conductor session is being seeded, or when inspecting/debugging one. Operating procedure for the kirocrew-pipeline-conductor agent - run one issue/PR pipeline on one repository as a supervised fleet. Auto-pick items, preflight every candidate to one deterministic claim verdict, stand up one worker…