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 agentmods add commands/generous-corp/pulp/cigit clone --depth 1 https://github.com/Generous-Corp/pulpWrote 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/commands/generous-corp/pulp/ci)<a href="https://agentmods.dev/commands/generous-corp/pulp/ci"><img src="https://agentmods.dev/badge/commands/generous-corp/pulp/ci.svg" alt="Measured on agentmods" 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 | $0.00014 | $0.00336 |
| Opus 5 | $0.00007 | $0.00168 |
| Sonnet 5 | $0.00003 | $0.00067 |
| Haiku 4.5 | $0.00001 | $0.00034 |
Grade A, and why
ci 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 today.
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.
What it actually says
Use the ci skill for all CI workflows. This is the primary gate for landing code on main.
Ship (branch → PR → CI → merge)
Use Shipyard-managed PR flow:
shipyard pr
shipyard pr is the canonical path for PR creation, tracking, validation,
and merge-on-green. It runs the repo gates, pushes the branch, opens the PR,
records Shipyard state for the macOS GUI / shipyard ship-state, validates
macOS + Linux + Windows, and merges only after all required evidence is green.
Do not use gh pr create for normal work. Treat direct GitHub PR creation as
an explicit emergency/manual bypass only; if it is used, report the Shipyard
tracking gap and reconcile by resuming or re-shipping through Shipyard where
possible.
pulp pr defaults to this same Shipyard path. Humans can opt out locally with
pulp config set pr.workflow github or manual, but agents should only use
those workflows when the user explicitly asks for that bypass. pulp status
shows the effective workflow and whether its local tool is available.
Just validate (no PR)
shipyard run
shipyard run --smoke
Check status
shipyard ship-state list
Cloud CI
shipyard cloud run build <branch>
See .agents/skills/ci/SKILL.md for the full workflow reference.
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.
- today First seen · 51 lines · 14 tokens per session scan A 7d955be17b3b
ci is a command published in the GitHub repository Generous-Corp/pulp (16 stars, last pushed today), licensed MIT. It adds 14 tokens to every session and 336 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 2026-09-04.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.