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BF iterate-pr

Iterate on a PR until CI passes. Use when you need to fix CI failures, address review feedback, or continuously push fixes until all checks are green. Automates the feedback-fix-push-wait cycle.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 1 file body ≈ 1 891 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Iterate on a PR until CI passes.

As a process F 58/100 · Will not run — References files that are not bundled: scripts/fetch_pr_checks.py, scripts/fetch_pr_feedback.py

ProcedureGitHubSoftware developmentAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
F
58/100
Will not run
References files that are not bundled: scripts/fetch_pr_checks.py, scripts/fetch_pr_feedback.py
Tools and files w 18
0
When it triggers w 12
20
Result and completion w 14
40
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: iterate-pr (sickn33/agentic-awesome-skills)

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/fetch_pr_checks.py
  • warning missing-ref reference to a missing file: scripts/fetch_pr_feedback.py
  • note frontmatter-key unknown frontmatter key "risk"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "date_added"

Process rating: all ten parameters 58/100

Will not run. References files that are not bundled: scripts/fetch_pr_checks.py, scripts/fetch_pr_feedback.py
  • 0Tools and files. 2 referenced file(s) missing: scripts/fetch_pr_checks.py, scripts/fetch_pr_feedback.py
  • 20When it triggers. No condition that starts the skill
  • 40Result and completion. Does not say what the result is
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 41 steps, 3 vague phrases
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1891 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 194: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 41 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.