Agent skill

Rpi

by boshu2 in boshu2/agentops

Drive one accepted change through implementation and checks to done, with one fresh review only where a mistake is costly.

Apache-2.0Auto-check passed

Install Rpi

skills CLI
$ npx skills add boshu2/agentops --skill rpi -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install boshu2/agentops rpi --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/boshu2/agentops.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/rpi .claude/skills/rpi && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
rpi
GitHub stars
448
Used in
1 other repo
Token cost
~1.9k tokens
SKILL.md length
877 words
Files
5 (incl. scripts, references)
Skills in repo
31
Repo updated
First seen
Licence
Apache-2.0

At a glance

Drive one accepted change through implementation and checks to done, with one fresh review only where a mistake is costly.

  • Works in 7 steps: Use the existing accepted outcome, scope… → Take the smallest acceptance-advancing… → Implement and repair ordinary known… → …
  • : selected by name
  • SKILL.md covers Operating charter, Delegation and handoffs, Causal stall and bounds and Evidence and boundaries, plus 1 more section
  • Runs Shell scripts from its folder

What it does

Rpi is an agent skill from boshu2/agentops. Drive one accepted change through implementation and checks to done, with one fresh review only where a mistake is costly. Use when: selected by name.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/boundaries.md` and `references/outer-goal.md`).

The repository describes itself as: DevOps discipline for AI coding agents: shape the work, track it as a graph, and get each change judged by a context that didn't write it. The licence is Apache-2.0.

When your agent uses it

  • : selected by name

Example prompts

  • “/rpi”

Requirements

  • A Bash shell

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Use the existing accepted outcome, scope and real bounds. A clear change
  2. Take the smallest acceptance-advancing action. Plan
  3. Implement and repair ordinary known defects directly.
  4. Use focused checks during edits and complete required integration checks
  5. Spend validation where a mistake is costly. For an ordinary change the
  6. One round. Give the validator the accepted criteria, the exact subject and
  7. Stop at completed acceptance, cancellation, refusal, a spent real bound or

What it can do on your machine

Read from SKILL.md and the folder at commit bc79a29. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Shell), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Rpi loads about 1.9k tokens when it runs, and up to ~3.7k if it reads all its reference files. Until then it costs about 39 tokens; SKILL.md has 877 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from boshu2/agentops at commit bc79a29, republished under its Apache-2.0 licence (© boshu2). 877 words, ~1,898 tokens.

Download SKILL.mdSave it as .claude/skills/rpi/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
rpi
description
Drive one accepted change through implementation and checks to done, with one fresh review only where a mistake is costly. Use when: selected by name.
practices
bdd-gherkin, tdd, design-by-contract
hexagonal_role
domain
consumes
plan, implement, validate
produces
rpi-report.v1
skill_api_version
1
user-invocable
true
disable-model-invocation
true
metadata.graph_root
true
metadata.tier
meta
metadata.dependencies
plan, implement, validate

RPI

Own the authorized outcome through finish. Use the native coding agent and shell. BD or the caller's tracker owns work and handoffs; Git owns content and delivery. AgentOps supplies a small charter and one fresh judgment where a mistake is costly, not a scheduler.

Operating charter

  1. Use the existing accepted outcome, scope and real bounds. A clear change needs no Plan, Recall or Learn worksheet. Resolve uncertainty only when it could change the implementation or acceptance decision.
  2. Take the smallest acceptance-advancing action. Plan shapes missing intent or revises a disproved approach. Once an implementer can act and a validator can judge, implement; do not keep improving the plan. Approach revisions preserve acceptance and authorized scope. Acceptance changes need caller authority.
  3. Implement and repair ordinary known defects directly. A known test failure needs a fix and a discriminating check, not another planning phase, council or helper.
  4. Use focused checks during edits and complete required integration checks before finishing. Reuse valid exact-input receipts; rerun affected checks after changes. Reserve capacity for integration and repair. Keep a subject unchanged while it is being judged.
  5. Spend validation where a mistake is costly. For an ordinary change the checks and CI are the gate: finish. Obtain Validate from one fresh author-distinct context only when the caller asks, when a mistake cannot be cheaply undone after it lands (a published release or instructions users will follow, a security boundary, destroying data or tracker state, deleting a check that protects the product), or when no deterministic check covers the changed behavior. Use the author's model family unless the caller selects additional legs; explicitly required reviewers remain required.
  6. One round. Give the validator the accepted criteria, the exact subject and one question written before it starts, never the author's confidence or desired verdict; it does not re-run the checks. Repair what fails the accepted behavior or would mislead a user, break install or the CLI, or remove protection for the product; treat the rest as optional notes. Confirm each repair with a check and finish. A repair does not start another review, and NOT_PROVEN is reported with its gaps, not chased. Keep review cost a fraction of the cost of the work; when it approaches that cost, stop and report what is unchecked.
  7. Stop at completed acceptance, cancellation, refusal, a spent real bound or an unresolved causal stall after the help below. Adjacent improvements are not permission to expand the goal. Report them briefly only when useful; do not turn them into another work batch.

Delegation and handoffs

When delegation is authorized and useful, select the runtime's task-only dispatch option for independent work; a short prompt in a full-history fork still carries the full history. Supply accepted intent and scope, the exact subject, relevant evidence, remaining bounds, the result's consumer and check ownership. Resume an author for direct repair when useful. Observe actual dispatch settings: prompt wording proves neither isolation nor smaller inherited context. At completion, verify the expected subject and required results; a quiet or partial status is not success.

Return concise findings, check facts and evidence references in the existing handoff, and disclose missing or truncated evidence. Identify the combined subject at the integration or judgment boundary; unjudged worker increments supply content identity and check facts, not duplicate evidence bundles.

Show full SKILL.md (325 more words)Show less

Causal stall and bounds

Unknown cause, recurrence, no progress or a wrong objective admits at most one bounded fresh helper for that incident within authority and bounds. Give it the failed assumption, evidence and one discriminating question. Resume only with a different testable approach; an unhelpful answer ends the attempt. Do not chain helpers or rename the incident. Known failures get direct repair. Cancellation, refusal and spent hard time/cost/quota skip help.

Respect actual caller/native limits, including explicit repair-round bounds. Retries, compaction, helpers and new subjects never renew them; retry count alone is not a spent budget. If interruption threatens evidence, preserve accepted intent, exact subject, useful receipts, unresolved cause, bounds and helper use in the native handoff. Prompt text proves no native enforcement. Outer-goal guidance remains optional.

Evidence and boundaries

When a validator is used, bind accepted intent, complete changed paths, exact subject and factual receipts for it; disclose affected orphaned acceptance evidence. Use existing provenance helpers rather than a new evidence format. Requested proof uses caller-selected protected external non-Git storage; preserve legacy .agents/ evidence. For a requested binding verdict, missing identity, freshness or proof means NOT_PROVEN; proven failed acceptance or scope violation means FAIL; PASS needs every criterion verified and empty not_checked. Authors cannot issue binding PASS.

Memory, specialists and runtime adapters are on demand; no-match and no-change are valid. Read boundaries when authority, scope, evidence or delivery is at issue. Do not invent a runtime, hidden machine artifact or workflow to finish an ordinary change.

Closeout

Report in this shape. Plans, activity, reviews and saved pages earn no capability credit, and an unchecked item is reported, not a reason to keep validating. Machine evidence such as rpi-report.v1 or verdict.v2 is optional unless a caller or declared consumer requires it. When no machine artifact is requested or required, return the result without creating one.

text
Result:      done | stopped: <cancelled, refused, bound spent or stalled> | NOT_PLANNED | NOT_BUILT
Subject:     <commit, branch or diff identity>
Acceptance:  <criterion> -> <evidence: check, receipt or ref>, one line each
Checked:     <checks run on the final subject, with results>
Not checked: <what was not run or not covered, and why> | none
Judgment:    none (checks and CI gate an ordinary change) | <validator context id>: PASS | FAIL | NOT_PROVEN
Limits:      <material gaps; adjacent work noticed but not done> | none

NOT_PLANNED (stopped before an actionable slice existed) and NOT_BUILT (stopped before a candidate change existed) describe progress, not semantic verdicts.

© boshu2, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in skills/rpi of boshu2/agentops.

  • SKILL.md
  • agents/openai.yaml
  • references/boundaries.md
  • references/outer-goal.md
  • scripts/validate.sh

Open the folder on GitHubat commit bc79a29

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in boshu2/agentops, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Rpi next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Rpi compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Rpi this skillboshu2/agentops4481 repos~1.9kAutomated safety check: PassApache-2.0
Acceptance Evidence for Deliverieslobehub/lobehub83k—~9.7kAutomated safety check: PassApache-2.0
QA Acceptancepaperclipai/paperclip99k—~964Automated safety check: PassMIT
Feishu Driveopenclaw/openclaw392k—~375Automated safety check: PassMIT
Acceptance Orchestratorsickn33/agentic-awesome-skills47k2 repos~943Automated safety check: PassMIT
Dos Verify Done Claimssickn33/agentic-awesome-skills47k1 repos~2.1kAutomated safety check: PassMIT

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Questions about Rpi

What does Rpi do?

Drive one accepted change through implementation and checks to done, with one fresh review only where a mistake is costly. Rpi is an agent skill from boshu2/agentops. Drive one accepted change through implementation and checks to done, with one fresh review only where a mistake is costly.

When should I use Rpi?

Rpi fits situations like: : selected by name.

How do I install Rpi in Claude Code?

Run `npx skills add boshu2/agentops --skill rpi -a claude-code`. Or copy the skill folder (skills/rpi in boshu2/agentops) into .claude/skills/rpi in your project. Claude Code loads it when a task matches its description.

How do I install Rpi in Codex?

Run `npx skills add boshu2/agentops --skill rpi -a codex`. Or copy the skill folder (skills/rpi in boshu2/agentops) into .agents/skills/rpi in your project. Codex loads it when a task matches its description.

Can I use Rpi in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add boshu2/agentops --skill rpi -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rpi, .gemini/skills/rpi, .github/skills/rpi and .opencode/skills/rpi in your project.

What does Rpi need to run?

Going by SKILL.md and its folder, Rpi needs a shell for the scripts in its folder. Our summary lists: A Bash shell.

Does Rpi access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Rpi safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Rpi use?

Rpi is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Rpi use?

About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.8k tokens, read only when the agent opens those files.

What are the alternatives to Rpi?

Skills that share tags, products or a category with Rpi: Acceptance Evidence for Deliveries (lobehub/lobehub, 83k stars), QA Acceptance (paperclipai/paperclip, 99k stars), Feishu Drive (openclaw/openclaw, 392k stars) and Acceptance Orchestrator (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Rpi?

boshu2 (a GitHub user) maintains it in boshu2/agentops, which has 448 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on October 10, 2026.

Source: boshu2/agentops on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.