agentmods.dev

landed

A post-merge workflow skill for checking a merged code change, verifying continuous integration (automated build and test checks), optionally checking deployment, and cleaning up branches.

In plain words
What is it for?
Use it after a pull request is merged to identify the merge, inspect CI results, watch running checks, review deployment status, and clean up branches.
Why use it?
It helps confirm that a merged change passed its checks and that the repository is ready for the next piece of work.

Skill for Claude CodeCodex

2 4mo ago A scan 26 tokens original MIT
View the source file stranma/claude-code-python-template
Install

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.

agentmods
npx agentmods add skills/stranma/claude-code-python-template/landed
Any agent
npx skills add stranma/claude-code-python-template --skill landed
Clone the repo
git clone --depth 1 https://github.com/stranma/claude-code-python-template

Made for: Claude Code, Codex.

Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,126 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00026 $0.01126
Opus 5 $0.00013 $0.00563
Sonnet 5 $0.00005 $0.00225
Haiku 4.5 $0.00003 $0.00113

Measured yesterday against content hash 4e7a39758495, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

landed 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.

.claude/skills/landed/SKILL.md · 117 lines

How it starts

The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Landed

Post-merge lifecycle command. Run this after a PR is merged to verify CI, check deployments, clean up branches, and identify next steps.

Step 1: Detect Merged PR

Identify the PR that was just merged.

  1. Run git branch --show-current to get the current branch

  2. If already on master:

    • Check git reflog --oneline -20 for the previous branch name
    • If no branch found, ask the user for the PR number or branch name

  3. Look up the merged PR:

    gh pr list --state merged --head <branch> --json number,title,mergeCommit -L 1

  4. If no PR found: ask the user for the PR number directly

  5. Display: PR number, title, merge commit SHA

Pre-check: Run gh auth status early. If not authenticated, stop and instruct the user to run gh auth login.

Step 2: Verify Merge CI

Check that CI passed on the merge commit.

  1. List recent runs on master:

    gh run list --branch master -L 20 --json status,conclusion,databaseId,name,headSha

  2. Filter to runs whose headSha matches the merge commit SHA

  3. Evaluate all matched runs:

    • in_progress: watch still-running run(s) with gh run watch <id>
    • success: all matched runs must be completed with conclusion=success to proceed
    • failure: show details via gh run view <id> --log-failed for each failing run
      • Ask: "Is this a recurring issue or specific to this PR?"
      • If recurring: suggest adding to /done validation or pre-merge CI
      • If specific: diagnose inline from the failed log output

Step 3: Deployment Verification (Configurable)

Check for deployment status if configured.

  1. Check if .claude/deploy.json exists
  2. If it exists:
    • Read the file and iterate over configured environments
    • For each environment:
      • Watch the deployment workflow: gh run list --workflow <workflow> --commit <merge-commit-sha> --json status,conclusion,databaseId
      • If health_check URL is configured, fetch it and verify a 200 response
    • Report per-environment status (success/failure/in_progress)
  3. If no config file:
    • Ask the user: "Is there a deployment to verify? (skip if none)"
    • If user says no or skips: mark as "skipped"

Read the full file on GitHub · 117 lines

Changes

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.

  1. yesterday First seen · 117 lines · 26 tokens per session scan A 4e7a39758495

Subscribe to this mod's changes

landed is a skill published in the GitHub repository stranma/claude-code-python-template (2 stars, last pushed 4mo ago), licensed MIT. It adds 26 tokens to every session and 1,126 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 2026-08-31.

Code reviewGit and workflowTestingclaudeclaude-codemonorepoproject-templatepythontdd
Related

Other skills, from other repositories

test-guide

Test-writing guide for Backend.AI — propose success/exception/edge scenarios first, refine them with the user, then implement while reporting per-scenario verification status. Covers fixtures, withtables, mock repositories, pants test, optional TDD cadence.

lablup/backend.ai · 53 tokens

wu5-dev-flow

使用可审计的 SDD、严格 RED-GREEN-REFACTOR TDD 与安全 Git 门禁初始化、开发、修复、重构和交付 Python 项目。用于任何会修改项目源码、测试、规格、依赖或 Git 历史的任务，也用于继续跨 Session 的现有 spec/ 变更、审查代码、验证完成状态、创建提交或准备 GitHub PR。.

WenOwen/wu5-dev-flow · 94 tokens

simulate-contracts

Compile the contract set in your head — simulate how the real application would run from the specs, and disprove (or confirm) it works before any source .py is written.

nullhack/temple8 · 40 tokens

model-data-schema

Author the persistence schema as a binding contract from the captured external shapes and the interview — before any test stub is written.

nullhack/temple8 · 28 tokens

author-test-stubs

Author test stub files (.pyi) whose signatures express the domain, relationships, and compositions, as the first contract surface.

nullhack/temple8 · 30 tokens

derive-source-stubs

Derive the source stub files (.pyi) from the reviewed tests — every type the tests reference gets a definition.

nullhack/temple8 · 29 tokens