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 dceoy/ai-coding-agent-skills --skill github-productivitygit clone --depth 1 https://github.com/dceoy/ai-coding-agent-skillsWrote 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/dceoy/ai-coding-agent-skills/github-productivity)<a href="https://agentmods.dev/skills/dceoy/ai-coding-agent-skills/github-productivity"><img src="https://agentmods.dev/badge/skills/dceoy/ai-coding-agent-skills/github-productivity/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/dceoy/ai-coding-agent-skills/github-productivity"><img src="https://agentmods.dev/badge/skills/dceoy/ai-coding-agent-skills/github-productivity.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 | $0.00058 | $0.02318 |
| Opus 5.5 | $0.00023 | $0.00927 |
| Sonnet 5.5 | $0.00012 | $0.00464 |
| Haiku 4.5 | $0.00006 | $0.00232 |
Grade A, and why
github-productivity 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
11 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.
- references/methodology.md 20 KB
- references/metrics.md 17 KB
- scripts/__init__.py 57 B runs code
- scripts/aggregate.py 48 KB runs code
- scripts/analyze.py 29 KB runs code
- scripts/collect.py 44 KB runs code
- scripts/ghapi.py 10.0 KB runs code
- scripts/normalize.py 44 KB runs code
- scripts/productivity.py 11 KB runs code
- scripts/report.py 29 KB runs code
- scripts/workdir.py 24 KB runs code
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 Changed fe72bb345849
- 1mo ago Changed 1ede3439b91c
- 1mo ago First seen · 127 lines · 58 tokens per session scan A 0dcdf5e5e5b1
github-productivity is a skill published in the GitHub repository dceoy/ai-coding-agent-skills (4 stars, last pushed 2d ago), licensed AGPL-3.0. It adds 58 tokens to every session and 2,318 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-05.
Other skills, from other repositories
investigating-repository-history
Investigate GitHub repository history before risky code changes using git blame/log, GitHub PRs, review comments, squash/rebase/cherry-pick/rename heuristics, and cited evidence. Use when asking why code exists, whether a change is safe, what PR introduced behavior, or before editing API, compatibility, security…
repo-activity-summary
Summarize a repository's recent engineering activity from git history — technologies, work types, churn hotspots, contributor patterns, and velocity. Use when asking "what has this repo been working on", "is this project active", "who contributes what", "where are the hotspots", or before onboarding onto an unfamiliar…
finishing-a-development-branch
Use when development work is complete and you need to prepare for merge. Guides through cleanup, testing, documentation, and creating a well-structured pull request.
git-workflow
Run git status / log / diff and list pull requests for the session's repo via the opendray gateway. Use whenever the user asks about "what's changed", "status", "diff", "open PRs", or wants to commit / push.
worktree-pr
Decide whether a task deserves its own git worktree and, if chosen, run it end to end: branch from the integration branch rather than the current tree, keep the main working copy untouched while the task runs, compare the result against the untouched baseline before declaring it done, and prepare it for a pull request…
intuitive-port-worktree
Port commits or patches between worktrees when explicitly requested; preserve the target branch and unrelated edits.