opening-prs
Open a GitHub PR via API as a flowing graph — branch + push + create_pr + mergeable poll, with structural protection against pushing to main/master/etc. Use when a Claude Code or Claude.ai container needs to land changes on GitHub via the API (no git CLI required) and the prose "create branch, push, open PR, wait for mergeable" workflow keeps drifting under context pressure.
What this skill does
# Opening PRs
A `flowing` graph that turns the imperative "create branch, push files,
open a PR, poll mergeable_state" workflow into a structural DAG. The
"NEVER push directly to main" rule is encoded as a `validate=` gate that
physically can't be skipped.
```python
from opening_prs import open_pr
result = open_pr(
repo="owner/repo",
branch_name="feat/cool-thing",
title="Add cool thing",
body="## Summary\n\n...",
files=[
("src/cool.py", "<file content>"),
("docs/cool.md", "<file content>"),
],
base="main", # default; protected names are also rejected
)
print(result["pr_url"]) # https://github.com/.../pull/N
print(result["mergeable_state"]) # clean | dirty | unstable | behind | blocked
```
## What this fixes
The `gh pr create` workflow (or hand-rolled API calls) has five steps in
prose form:
1. Determine the branch name
2. Get the base branch's HEAD SHA
3. Create the branch
4. Push files to the branch
5. Create the PR
6. Poll `mergeable_state` until GitHub finishes computing it
Each step has known failure modes:
- **Step 1**: Accidentally pushing to `main` — the diagnosed pattern that
motivated github-procedures §6 in the first place.
- **Step 6**: GitHub returns `mergeable_state: null` immediately after
creation. Need to poll. "Wait a few seconds and check" is prose, not a
procedure — so it gets skipped under context pressure.
This skill encodes both as flowing primitives:
```
determine_branch ──▶ guard ──▶ get_base_head ──▶ create_branch
│ │
│ ▼
│ push_files
│ │
│ ▼
│ create_pr
│ / \
│ ▼ ▼
│ wait_mergeable present_pr [terminal]
│
└─ validate=must_not_be_base_branch
```
- **`validate=must_not_be_base_branch`** rejects `branch_name in
{"main", "master", "trunk", "production", "prod"}` — case-insensitive
— and the configured `base` itself. The body of `create_branch` never
fires for a protected name. No GitHub API call happens for an
invalid branch name.
- **`retry_until=lambda r: r["mergeable_state"] in SETTLED_STATES`**
consumes the retry budget while GitHub computes the merge result.
`unknown` and `null` keep polling; settled states (`clean`, `dirty`,
`unstable`, `behind`, `blocked`) stop. Exhaustion is soft: the PR is
still presented with the last observed state.
## Auth
Requires `GH_TOKEN` (or `GITHUB_TOKEN`) in the environment. Classic PAT
or fine-grained PAT with `repo` scope.
```python
import os
os.environ["GH_TOKEN"] = "ghp_..." # or load from a .env file
```
The skill sends `User-Agent: opening-prs` on every API call. (GitHub
returns 401 "Bad credentials" without a UA, regardless of token
validity. Common trap.)
## Result shape
```python
{
"pr_url": "https://github.com/owner/repo/pull/N",
"pr_number": 42,
"pr_state": "open",
"branch": "feat/cool-thing",
"base": "main",
"head_sha": "abc123...",
"mergeable_state": "clean",
"files_pushed": ["src/cool.py", "docs/cool.md"],
"detached_failures": [],
}
```
Raises `RuntimeError` only if the main DAG fails (validate, branch
creation, file push, or PR creation). Mergeable polling exhaustion is a
soft failure — the PR exists, the field just isn't computed yet.
## Tuning the mergeable poll
```python
open_pr(
...,
mergeable_poll_retries=8, # default 8
mergeable_poll_base_ms=2000, # default 2s
mergeable_poll_max_ms=8000, # default 8s (cap on exponential backoff)
)
```
## When NOT to use
- Pushing many large files (the GitHub Contents API is one-file-per-PUT
with base64 encoding — slow above ~10 files). Use `git push` if you
have the CLI.
- Needing to amend commits or rewrite history. This skill creates one
commit per file via the contents API.
- Anything involving force-push, branch deletion, or PR review-state
manipulation. Out of scope; use a different tool.
## See also
- `flowing` — the DAG runner this skill is built on
- `closing-issues` — the symmetric "close + synthesize" flow
- The `accessing-github-repos` skill for byte-layer GitHub access patterns
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.