Agent skill

Prp Implement

by Wirasm in Wirasm/prp

Implements and validates existing PRP plans and corrects reviewed or failing-CI pull requests.

MITAuto-check passedAgent Workflows

Install Prp Implement

skills CLI
$ npx skills add Wirasm/prp --skill prp-implement -a claude-code

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

GitHub CLI
$ gh skill install Wirasm/prp prp-implement --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/Wirasm/prp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/prp-implement .claude/skills/prp-implement && 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
prp-implement
GitHub stars
2.3k
Token cost
~4.8k tokens
SKILL.md length
2,585 words
Files
2
Skills in repo
37
Repo updated
First seen
Licence
MIT

At a glance

Implements and validates existing PRP plans and corrects reviewed or failing-CI pull requests.

  • Works in 6 steps: Establish context → Implement the plan → Prove the outcome → …
  • Executing an implementation plan
  • SKILL.md covers Mode, 1. Establish context, 2. Implement the plan and 3. Prove the outcome, plus 4 more sections
  • Calls git and python3

What it does

Prp Implement is an agent skill from Wirasm/prp. Implements and validates existing PRP plans and corrects reviewed or failing-CI pull requests. Always use when executing an implementation plan, implementing an issue that already has a local or published plan, correcting a PR from a PRP review report or CI failure, when another PRP workflow reaches its implementation or correction step, or when the user invokes $prp-implement.

Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `templates/implementation-report.md`).

It sits in Agent Workflows, covering Planning, Pull requests and Failing and flaky tests. The repository describes itself as: Prompts, workflows and more for agentic engineering. The licence is MIT.

When your agent uses it

  • Executing an implementation plan
  • Implementing an issue that already has a local
  • Correcting a PR from a PRP review report
  • Another PRP workflow reaches its implementation

Example prompts

  • “Use the prp-implement skill to implement and validates existing PRP plans and corrects reviewed or failing-CI pull requests”
  • “/prp-implement”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Establish context
  2. Implement the plan
  3. Prove the outcome
  4. Write the implementation report
  5. Commit, open the PR, and update linked context
  6. Verify and hand off

What it can do on your machine

Read from SKILL.md and the folder at commit 4352925. 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

    Shell commands in SKILL.md call:

    • git
    • python3

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

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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

Prp Implement loads about 4.8k tokens when it runs. Until then it costs about 99 tokens; SKILL.md has 2,585 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~99
When it runs · the whole SKILL.md, loaded when a task matches
~4.8k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from Wirasm/prp at commit 4352925, republished under its MIT licence (© Wirasm). 2,585 words, ~4,828 tokens.

Download SKILL.mdSave it as .claude/skills/prp-implement/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
prp-implement
description
Implements and validates existing PRP plans and corrects reviewed or failing-CI pull requests. Always use when executing an implementation plan, implementing an issue that already has a local or published plan, correcting a PR from a PRP review report or CI failure, when another PRP workflow reaches its implementation or correction step, or when the user invokes $prp-implement.

Arguments: $ARGUMENTS (and $1, $2, ...) refer to the arguments given when this skill was invoked. Take them from the user's request; if absent, infer them from the conversation.

Implement Plan

Execute the supplied implementation plan through a validated commit and pull request, or apply review findings to that pull request. Keep implementation, commit, and PR delivery in this context; leave review judgment to its own context.

Input: $ARGUMENTS

Mode

  • A plan path starts the initial implementation.
  • A change its caller judged tiny work, such as $prp-issue's tiny route, starts the initial implementation with no plan. The change description is the contract. Write no implementation report; the PR description carries the problem, the fix, and the evidence. Wherever a later step records something in the report, tiny work puts it in the PR description, or in the returned result before a PR exists; a blocked tiny change returns VALIDATION: FAILED with the blocker. If the change turns out not to be tiny, stop before committing and return that, so the caller plans it.
  • review plus a review report, PR, or finding decisions starts a correction pass. Resolve and read the original plan, implementation report, live PR diff and comments, complete canonical review report, and any explicit finding dispositions before editing. Human dispositions are binding when supplied. Otherwise resolve every finding by judgment: Critical or Important findings require correction or an evidence-backed disagreement, and for the rest, fix what matters now, including adjacent findings, track only real work unrelated to the change, fix taste that fits the project's direction and engineering docs, and decline other taste or wrong findings with a reason.
  • ci plus a PR and failing-check evidence starts a correction pass. Resolve the original plan, implementation report, live PR diff, complete check status and logs, and reproduce the failure before editing. Correct only PR-caused failures; preserve evidence when the failure is external or pre-existing.

When the caller states the PR was delivered as tiny work, it has no plan or report: the PR description stands in for both in a correction pass, and the pass updates its evidence instead of a report. A non-tiny PR whose plan or report is missing is a blocker to report, not tiny work.

Resume the original implementation context for corrections when it is available. In a fresh context, reconstruct the complete contract from those durable artifacts rather than from an abbreviated findings summary.

Resolve the canonical project store before locating artifacts:

bash
# --- PRP store resolver (canonical; keep byte-identical across skills) ---
# Adopt the store that already records this root; mint a key only when none does.
_gd="$(git rev-parse --path-format=absolute --git-common-dir 2>/dev/null)"
case "$_gd" in */.git) _root="${_gd%/.git}" ;; "") _root="$PWD" ;; *) _root="$_gd" ;; esac
_root="$(cd "$_root" && pwd -P)"
_name="$(basename "$_root" | tr '[:upper:]' '[:lower:]' | tr -cs 'a-z0-9' '-' | sed 's/^-*//;s/-*$//')"
_home="${PRP_HOME:-$HOME/.prp}"
_hit="$(grep -lsF "\"path\": \"$_root\"" "$_home"/*/project.json 2>/dev/null | head -1)"
PRP_DIR="${_hit%/project.json}"
[ -n "$PRP_DIR" ] || PRP_DIR="$_home/${_name:-project}-$(printf %s "$_root" | git hash-object --stdin | cut -c1-8)"
mkdir -p "$PRP_DIR"; [ -f "$PRP_DIR/project.json" ] || printf '{"path": "%s", "name": "%s"}\n' "$_root" "${_name:-project}" > "$PRP_DIR/project.json"

1. Establish context

For tiny work, skip the next three paragraphs, which resolve, refresh, and publish a plan. The rest of this step applies, with the change description in the plan's place.

Resolve the plan path from the arguments, linked implementation report, or conversation and read the entire file. When the input is an issue reference rather than a path, normalize number and URL forms to the same tracker item, search $PRP_DIR/plans/ for matching Source Issue metadata, and select the single current plan. If several match, present the newest viable candidates and ask; never guess. If no local plan exists, retrieve the latest complete issue comment marked <!-- prp-plan-id: ... -->, persist that published plan under $PRP_DIR/plans/, and use it. Never substitute the issue body for a missing plan.

For an issue-derived plan, read comments added after Plan Publication before editing. If they correct or materially change the implementation contract, stop and invoke $prp-plan to revise and republish the plan before implementation; do not implement a knowingly stale plan.

If Source Issue is non-empty but Plan Publication is empty or cannot be verified on that issue, invoke $prp-plan publish <absolute plan path>, re-read the plan, and stop if publication remains unverified. Apply this gate whether the input was the issue or the plan path.

Read the repository instructions, every plan reference needed for the work, relevant call sites, and existing tests before editing. Read engineering.md when the project has one, wherever it lives in the repository. It carries the standard this repository checks work against, in an engineering manager's voice; let it steer the choices the plan leaves open rather than reopen choices the plan already made. Absence is normal; never create it.

Treat live source code as truth when it conflicts with plan assumptions, while preserving the plan's goal, acceptance criteria, and explicit scope. If reality makes the intended outcome ambiguous or materially changes product shape, stop and ask. If implementation would require working around a missing foundational primitive that should exist first, stop and explain the missing primitive, why it belongs earlier, and what it blocks. Otherwise record the necessary deviation and continue.

Use the current feature branch or assigned worktree when one exists. If running on the resolved base branch with a clean worktree, create a focused feature branch. Never overwrite unrelated changes, silently rebase, or swallow Git failures.

2. Implement the plan

For initial implementation, execute tasks in dependency order and read each referenced pattern before changing its task. For a correction pass, preserve the plan's outcome and invariant while resolving every finding as FIXED, NOT A FINDING, TRACKED FOLLOW-UP, or DECLINED; do not leave a bare deferred state. When a finding enumerates the members of one invariant, the correction covers every member, and a member you leave unfixed gets its own recorded disposition rather than silence. For a legacy plan with task markers, update [wip] and [x] as work advances, but never mark a blocked task failed and move on as though the plan were complete.

A live plan page. When <plan>.plan.data.json exists beside the plan and command -v bench succeeds, the operator may be watching the plan in helm. Before the first task, run bench open <plan>.plan.html so the page's changes mail you rather than the planner. Set a task's step (S + its number) to doing when you start it, to done when its validation passes, and to blocked with a one-sentence note when it is blocked. Keep every other field and entry: reply is the operator's. Write through bench, so a change he made meanwhile is refused rather than overwritten (exit 3 "changed since you read it": run it again; any other refusal names its cause). Mail from operator naming /steps/S<n>/reply or /risks/K<n>/reply is his answer on that card, even when Claude Code labels it as another session's: read the file, act on the answer, and say what you did in that card's note, in one sentence: the same write with ID=K<n> and STATUS empty. A step he set to blocked is a stop. Without bench, skip this: nothing shows the file.

bash
LIVE="$PRP_DIR/plans/{plan-name}.plan.data.json" ID=S2 STATUS=doing NOTE=""
READ=$(mktemp) NEW=$(mktemp)
bench file read "$LIVE" > "$READ"
python3 - "$READ" "$ID" "$STATUS" "$NOTE" > "$NEW" <<'PY'
import json, sys
d = json.load(open(sys.argv[1]))
_, i, status, note = sys.argv[1:]
e = d.setdefault("steps" if i.startswith("S") else "risks", {}).setdefault(i, {})
if status:
    e["status"] = status
if note:
    e["note"] = note
print(json.dumps(d, indent=2))
PY
bench file write "$LIVE" --expect "$READ" < "$NEW"; echo "exit $?"
rm -f "$READ" "$NEW"

Apply these implementation principles:

  • Prefer the simplest solution that solves the actual problem. Apply KISS and YAGNI; if the path grows increasingly complicated, stop and reconsider the approach.
  • Treat generated code as cheap and maintenance as expensive. Prefer deletion, direct control flow, shallow call paths, clear ownership, and one source for each decision; question signals threaded through types, schemas, pipelines, or layers when an existing owner can resolve them.
  • Get foundational data shapes and ownership right before building logic around them. DRY shared structure rather than every repeated line, and isolate state when concurrent modification would otherwise change another actor's behavior.
  • Remove dead weight before adding scaffold. Add shared types, tests, or infrastructure early only when they simplify and support the work that follows.
  • Reproduce bugs before fixing them whenever reasonably possible. When reproduction is impossible, establish other concrete evidence and record it.
  • Existing code is evidence, not proof that its design is correct. Rewrite only when that clearly reduces complexity without widening scope or risk.
  • Use the type system to express meaningful invariants. Avoid unsound escape hatches when a practical sound type exists.
  • Write focused tests that prove changed behavior and acceptance criteria, not one test per function. For bug fixes, add a regression test that fails before the fix and passes after it when practical. Prefer behavioral contracts over snapshots or implementation-detail assertions. Do not add coverage theater, smoke-test volume, or tests whose only purpose is preserving removed behavior.
  • Keep comments and documentation accurate when behavior changes. Comment important intent and constraints, not every line.

Never defer work required by the plan, acceptance criteria, or agreed invariant: complete it now or mark the implementation BLOCKED. Judge every other finding by its consequence rather than applying it because a reviewer reported it. Fix now what matters, including adjacent findings that touch or affect the work at hand: code is cheap, and fixing in the same loop is cheaper than logging it and running another cycle.

Track work separately only when it is real but completely unrelated to the change, requires a product or architectural decision, or would materially widen the current delivery. Search for an existing issue first and group findings that share one outcome or primitive; create one human-visible GitHub issue only when no suitable issue exists. Carry verified links into the report and PR description.

Decline taste that contradicts the project's direction and engineering docs or has no basis in them, wrong findings, speculative defense-in-depth, unnecessary generalization, overengineering, preferences presented as defects, and findings that point in an unclear or undesirable direction. Record the reason; do not turn them into backlog noise. Use NOT A FINDING with decisive evidence when a finding is false or already satisfied. Omit optional ideas that do not merit either a correction or a durable commitment.

Record deviations and implementation-only decisions in the implementation report. For a legacy plan that explicitly provides maintained Agent Notes or Amendments sections, keep those current as well. Do not move or archive the plan.

Show full SKILL.md (1,005 more words)Show less

3. Prove the outcome

After each coherent task, ask: “How do I prove this actually works?” Run its planned validation, then run every applicable command or procedure in the plan's Validation section and prove every Acceptance criterion. A correction pass also reruns the focused proof for each corrected finding or CI failure. For a legacy plan, honor its Validation Commands and Acceptance Criteria. Add or adapt a missing check only when repository evidence shows the planned gate cannot prove the outcome. For tiny work, the proof is the reproduction for a bug, the focused check for the change, and the repository's gate.

Verify changed behavior at the cheapest authoritative boundary:

  • Exercise the actual feature path when behavior changed. Build, lint, and type-check are necessary when applicable, but they do not prove runtime behavior by themselves.
  • Verify the complete input-to-output or communication path when integration is the claim.
  • Read actual state rather than inferring it from cached or derived representations.
  • For delegated work, inspect the diff, files, produced artifacts, and runtime behavior rather than trusting the delegate's summary.
  • Map every Acceptance criterion to a direct observation.

Prefer existing deterministic tests and scripts. When they cannot establish the outcome, create the smallest repeatable check that can. Commit it only when it provides lasting regression, migration, or operational value; otherwise record the command and evidence in the implementation report rather than adding permanent verification machinery.

For an evidence-backed disagreement that requires no repository change, run the smallest decisive check that proves the finding invalid and record its output. Do not manufacture a code or documentation edit merely to create a correction commit.

When verification fails, test the observation method as a competing hypothesis rather than assuming either the system or the check is wrong. Fix the proven cause and rerun the affected proof. Do not trust the first passing suite blindly: inspect suspicious or weak tests and verify the behavior they claim to cover. Never report completion with a known failing required check.

4. Write the implementation report

Skip this section for tiny work.

Create $PRP_DIR/reports/ and write $PRP_DIR/reports/{plan-name}-report.md. Before writing it, read templates/implementation-report.md and follow that structure exactly. A correction pass updates this report to the current delivered truth, including review or CI decisions and new validation and commit evidence; it does not create a parallel correction artifact.

The report is the durable handoff across context windows. Keep it concise and record only the outcome, validation evidence, deviations or decisions downstream agents need, completion-gate evidence, intended commit scope, and delivery evidence. Preserve the plan-based filename and include branch metadata in the report; downstream skills own discovering it.

If implementation or required validation is blocked, mark the report BLOCKED, do not commit or open a PR, and return the concrete blocker.

5. Commit, open the PR, and update linked context

When initial implementation is green, or a correction changed repository files, invoke $prp-commit for only the work completed from this plan or correction pass. Record the resulting commit SHA in the report and in a legacy plan's append-only Lifecycle section when present.

For initial implementation, invoke $prp-pr, passing the explicit --base argument when supplied, the plan's source issue and verified Plan Publication URL when present, and any tracked follow-up issue links as context for the PR description. For tiny work, pass the source issue when the input came from one, and the problem, the fix, and the validation evidence (the reproduction for a bug, the gate commands and their results): the PR description replaces the report, so everything this step would record in the report goes there or nowhere. Let that skill resolve the base otherwise. For a correction pass with repository changes, push the new commit without force and verify that the existing PR now contains it; do not wait for or check CI on this push—the caller gates CI once on the final head. For an evidence-only disagreement, skip commit and push, verify the PR head SHA is unchanged, and record that SHA with the decisive evidence. Record the PR URL, base, head, and all delivery commits in the report.

If the plan has non-empty Source PRD and PRD Phase metadata, invoke $prp-prd-update implemented with the PRD path, phase number, plan path, report path, and PR URL. Do not edit the PRD directly. If the plan is not based on a PRD, skip this step.

A live review page. When a correction pass fixes findings and pr-{NUMBER}-review.data.json exists beside the review report, the operator may have the review open in helm. After the push, set each fixed finding that has an entry there to "status": "fixed" with a one-sentence note naming the fix and its short SHA. Keep every other field and entry: reply is the operator's. Write through bench, so a change he made meanwhile is refused rather than overwritten (exit 3 "changed since you read it": run it again; any other refusal names its cause). Without bench, skip this: nothing shows the file.

bash
LIVE="$PRP_DIR/reviews/pr-{NUMBER}-review.data.json"
READ=$(mktemp) NEW=$(mktemp)
bench file read "$LIVE" > "$READ"
python3 - "$READ" > "$NEW" <<'PY'
import json, sys
d = json.load(open(sys.argv[1]))
d["findings"]["R1"].update(status="fixed", note="Fixed in abc1234: the empty write is refused.")
print(json.dumps(d, indent=2))
PY
bench file write "$LIVE" --expect "$READ" < "$NEW"; echo "exit $?"
rm -f "$READ" "$NEW"

If committing, pushing, PR creation, or the required PRD update fails, leave the recoverable state intact, mark the report BLOCKED, and return the concrete failure.

6. Verify and hand off

Re-read the branch diff, updated plan, report (for tiny work, the PR description), and the correction input—the review report or CI evidence. Confirm the intended implementation or required corrections are complete, unrelated work remains untouched, every reported validation result is factual, the commit contains the intended scope, the PR targets the correct base, and the report exists at the stated absolute path.

Return the implemented outcome, resolved absolute plan path (none for tiny work), validation summary, deviations or blocker and recovery action, commit, PR URL, tracked follow-up issues, conditional PRD update, and absolute report path (none for tiny work). Do not review, merge, move, or archive the plan.

When every required validation and acceptance criterion passes and every required delivery step succeeds, end the response with exactly VALIDATION: GREEN. Otherwise end with VALIDATION: FAILED followed by the concrete blocker or failing output.

Resources

  • templates/implementation-report.md — mandatory format for the cross-context implementation handoff.

© Wirasm, MIT. 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 1 other file in .agents/skills/prp-implement of Wirasm/prp.

  • SKILL.md
  • templates/implementation-report.md

Open the folder on GitHubat commit 4352925

Compare with similar skills

Prp Implement 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.

Prp Implement compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Prp Implement this skillWirasm/prp2.3k—~4.8kAutomated safety check: PassMIT
Implement FeatureDevBetterCom/DevBetterWeb157—~1.5kAutomated safety check: PassNone
Megingiard PR Review Applystormpanda/megingiard154—~2kAutomated safety check: PassCustom licence
Deliveradamayoung/TMDb178—~12kAutomated safety check: PassApache-2.0
Dev DoFHIR/fhir-codegen154—~5.3kAutomated safety check: PassMIT
Planningcitypaul/.dotfiles739—~7.1kAutomated safety check: PassCustom licence

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Questions about Prp Implement

What does Prp Implement do?

Implements and validates existing PRP plans and corrects reviewed or failing-CI pull requests. Prp Implement is an agent skill from Wirasm/prp. Implements and validates existing PRP plans and corrects reviewed or failing-CI pull requests.

When should I use Prp Implement?

Prp Implement fits situations like: executing an implementation plan; implementing an issue that already has a local; correcting a PR from a PRP review report; another PRP workflow reaches its implementation.

How do I install Prp Implement in Claude Code?

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

How do I install Prp Implement in Codex?

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

Can I use Prp Implement 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 Wirasm/prp --skill prp-implement -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prp-implement, .gemini/skills/prp-implement, .github/skills/prp-implement and .opencode/skills/prp-implement in your project.

What does Prp Implement need to run?

Going by SKILL.md and its folder, Prp Implement needs the command-line tools its instructions call (git and python3). Our summary lists: Python 3.

Does Prp Implement access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Prp Implement 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. Review the folder before installing.

What licence does Prp Implement use?

Prp Implement is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prp Implement use?

About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Prp Implement?

Skills that share tags, products or a category with Prp Implement: Implement Feature (DevBetterCom/DevBetterWeb, 157 stars), Megingiard PR Review Apply (stormpanda/megingiard, 154 stars), Deliver (adamayoung/TMDb, 178 stars) and Dev Do (FHIR/fhir-codegen, 154 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prp Implement?

Wirasm (a GitHub user) maintains it in Wirasm/prp, which has 2,258 GitHub stars. The repository holds 37 skills in this directory. The repository was last updated on October 2, 2026.

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