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rpi

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

Install / Use

npx skills add boshu2/agentops --skill rpi

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

84/100

Category

Legal

Supported Platforms

Universal

Our assessment of rpi

rpi scores 84/100 on our quality scale, 149th of 214 Legal skills we index.

Its SKILL.md is 7.4 KB long, split into 6 sections with 1 code example: a thorough specification that gives an agent plenty to work with.

It has 445 GitHub stars, a meaningful sign that others use it.

Substance
29/30
Structure
15/20
Description
15/15
Adoption
11/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 22 days ago, so rpi is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-10-16. Automated pattern scan on 2026-10-16. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

rpi compared with similar skills

All 4 of these similar skills score higher than rpi; compare them before choosing.

SkillScoreStarsUpdatedFormat
rpi (this skill)by boshu28444522d agoSKILL.md
Agent-Reachby Panniantong10097.8ktodayCLAUDE.md
taste-skillby Leonxlnx10097.5k1d agoCLAUDE.md
headroomby headroomlabs-ai10075.5ktodayCLAUDE.md
Scraplingby D4Vinci10087.9ktodayMCP Server

Frequently asked questions

How do I install rpi?
Run npx skills add boshu2/agentops --skill rpi. The install tabs above show the steps for each supported agent.
Which AI agents does rpi work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is rpi safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is rpi still maintained?
The repository was last updated 22 days ago, so rpi is actively maintained.

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 context_rel:
  • kind: customer-of with: plan
  • kind: customer-of with: implement
  • kind: customer-of with: validate skill_api_version: 1 user-invocable: true disable-model-invocation: true metadata: graph_root: true tier: meta dependencies: [plan, implement, validate] capabilities: [own_authorized_outcome, report] effects: [dispatch_core_phases] canonical_status: canonical disposition: keep_strategy output_contract: 'concise human-readable result; optional rpi-report.v1 when a caller or declared consumer requests machine-readable evidence'

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.

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.

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.

Related Skills

View on GitHub
GitHub Stars445
CategoryLegal
Updated22d ago
Forks41

Languages

Go

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions