canonical/charm-tech is a shared repository that stores the common standards, decisions, specifications, style guides, and templates used across Canonical Charm Tech repositories. The Charm Tech team uses it as a central source so its repositories can reference or reuse the same documentation, Python and Go conventions, security policies, and CI configuration.
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 canonical/charm-tech --skill iterate-prgit clone --depth 1 https://github.com/canonical/charm-techWrote 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/canonical/charm-tech/iterate-pr)<a href="https://agentmods.dev/skills/canonical/charm-tech/iterate-pr"><img src="https://agentmods.dev/badge/skills/canonical/charm-tech/iterate-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/canonical/charm-tech/iterate-pr"><img src="https://agentmods.dev/badge/skills/canonical/charm-tech/iterate-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00047 | $0.01878 |
| Opus 5.5 | $0.00019 | $0.00751 |
| Sonnet 5.5 | $0.00009 | $0.00376 |
| Haiku 4.5 | $0.00005 | $0.00188 |
Grade A, and why
iterate-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 1mo 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.
This is a copy
88% identical to iterate-pr — 30 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iterate on PR Until CI Passes
Continuously iterate on the current branch until all CI checks pass and review feedback is addressed.
Requires: GitHub CLI (gh) authenticated.
Important: Run all scripts from the repository root (where .git lives), not from the skill directory. Use the full path to each script — substitute <skill-dir> in the commands below with this skill's actual install path.
Bundled Scripts
scripts/fetch_pr_checks.py
Fetches CI check status and extracts failure snippets from logs.
uv run <skill-dir>/scripts/fetch_pr_checks.py [--pr NUMBER]
Returns JSON:
{
"pr": {"number": 123, "branch": "feat/foo"},
"summary": {"total": 5, "passed": 3, "failed": 2, "pending": 0},
"checks": [
{"name": "tests", "status": "fail", "log_snippet": "...", "run_id": 123},
{"name": "lint", "status": "pass"}
]
}
scripts/fetch_pr_feedback.py
Fetches and categorizes PR review feedback using the LOGAF scale.
uv run <skill-dir>/scripts/fetch_pr_feedback.py [--pr NUMBER]
Returns JSON with feedback categorized as:
high- Must address before merge (h:, blocker, changes requested)medium- Should address (m:, standard feedback)low- Optional (l:, nit, style, suggestion)bot- Informational automated comments (Codecov, Dependabot, etc.)resolved- Already resolved threads
Review bot feedback (from Sentry, Warden, Cursor, Bugbot, CodeQL, etc.) appears in high/medium/low with review_bot: true — it is NOT placed in the bot bucket.
Each feedback item may also include:
thread_id- GraphQL node ID for inline review comments (used for replies)
Workflow
1. Identify PR
gh pr view --json number,url,headRefName
Stop if no PR exists for the current branch.
2. Gather Review Feedback
Run <skill-dir>/scripts/fetch_pr_feedback.py to get categorized feedback already posted on the PR.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 184 lines · 47 tokens per session scan A e1323d71b181
iterate-pr is a skill published in the GitHub repository canonical/charm-tech (4 stars, last pushed 7d ago), licensed Apache-2.0. It adds 47 tokens to every session and 1,878 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to iterate-pr, differing in 30 lines, and is treated as a copy.
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