Borrowing it
Nothing to install: this file belongs to agigante80/actual-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/agigante80/actual-mcp-server/main/.claude/skills/implement-ticket/SKILL.mdgit clone --depth 1 https://github.com/agigante80/actual-mcp-serverWrote 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/agigante80/actual-mcp-server/implement-ticket)<a href="https://agentmods.dev/skills/agigante80/actual-mcp-server/implement-ticket"><img src="https://agentmods.dev/badge/skills/agigante80/actual-mcp-server/implement-ticket.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 98 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00162 | $0.03312 |
| Opus 5 | $0.00081 | $0.01656 |
| Sonnet 5 | $0.00032 | $0.00662 |
| Haiku 4.5 | $0.00016 | $0.00331 |
Grade A, and why
implement-ticket 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 8d 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement Ticket
A repeatable pipeline that takes a GitHub issue number and carries it from
"ready to build" to "merged on develop", with adversarial review and automatic
follow-up ticketing baked in. It chains tools we already have (the ticket-gate
agent, the project validation sequence, /code-review, /local-env) so a
single instruction does what a careful engineer would do across an afternoon.
When this runs
Trigger on any request to implement a specific issue by number. Extract the
issue number from the prompt. If no number is present, ask for one before
starting. Everything happens on develop; never touch main (see CLAUDE.md).
Inputs and state to track
TICKET: the issue number under implementation.DEPTH: recursion depth, starts at0for the user-invoked ticket. Spinning off and auto-implementing a discovered ticket increments it. The cap is2.SEEN: the set of ticket numbers this run has already started, so a follow-up ticket can never re-enter a ticket already in progress (loop guard).
Hold these in a TodoWrite list so progress survives a long run.
The pipeline
Run the phases in order. A phase that cannot pass after its bounded retries stops the run with a clear report rather than limping forward.
Order depends on ticket type. For a bug, behaviour-change, or external /
fork-harvested ticket, run Phase 0 (Reproduce) BEFORE Phase 1 (Gate): gating,
security-reviewing, and synthesising a ticket that turns out to be a phantom is
wasted work, so prove it reproduces first and gate only what survives. For
greenfield features, docs, and chore/CI tickets (nothing to reproduce), skip
Phase 0 and start at Phase 1.
Phase 0: Reproduce first (bug, behaviour-change, and external/fork tickets)
Run this FIRST, ahead of the gate, when the ticket is labelled bug, proposes a
change to existing observable behaviour, OR is external / community /
fork-harvested. Skip it only for greenfield features with nothing to reproduce,
docs, and chore/CI tickets (say so in the report, then start at Phase 1).
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.
- 8d ago First seen · 256 lines · 162 tokens per session scan A fa2eaf1023dd
implement-ticket is a skill published in the GitHub repository agigante80/actual-mcp-server (50 stars, last pushed yesterday), licensed MIT. It adds 162 tokens to every session and 3,312 once invoked, about $0.0008 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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pr-triage
4-phase PR backlog management with audit, deep code review, validated comments, and optional worktree setup. Use when triaging pull requests, catching up on pending code reviews, or managing a backlog of open PRs. Args: 'all' to review all, PR numbers to focus (e.g. '42 57'), 'en'/'fr' for language, no arg = audit…
review-pr
Perform a comprehensive code review of a pull request.
pr
Analyze changes, detect scope issues, and create a well-structured PR.
validate-changes
Evaluate staged changes using LLM-as-a-Judge before committing.