sesori-plan-maker

REVIEW · 67
Registry indexed

Create or update practical, code-informed plans and trackers. Use ONLY when the user explicitly asks to make or change a plan or tracker. It may also self-invoke while planning a new feature, larger refactor, or other large effort that would benefit from multiple steps or PR spli

Verified installs0
Stars105
Version1.0.0
Quality67/100 · Promising
Trust67/100 · Sandbox only
Audit79/100 · Needs review

Supply asset profile

Coding and developer agents

Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.

Browse track

Scenario

Coding agents

I need a coding agent that can understand a repository, edit code, and review pull requests.

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker

Maintenance

fresh

Pushed today

Risk

Needs review

Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.

GitHub quality

105

67/100 Quality · 75/100 Trust

Coverage tags

CodingCoding agentscoding-agentsagent-skill

Review notes

Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights. · Quality score needs review

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
67

Useful candidate, but compare it with alternatives before adopting.

Trust

Sandbox only
67

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

Audit

Needs review
79

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Run only in a sandbox and compare close alternatives before using it for real work.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

105 GitHub stars

Repo activity

105 stars, 6 forks

Maintenance

Pushed today

License

NOASSERTION

Install

npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker

Install safety

standard package or runtime install path

Permission surface

filesystem or document access, database access

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Review before production

  • Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.
  • Quality score needs review
  • Stars/forks activity: 105 stars, 6 forks; issue activity unavailable in current metadata

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

Open JSON

Suited tasks

  • Coding agents workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Inspect source files

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker
Policy
review
Human review
yes

Trust and risk

Trust
67/100
Audit
79/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.
  • Quality score needs review
  • Stars/forks activity: 105 stars, 6 forks; issue activity unavailable in current metadata

Agent safety v2

59/100 · Review before install

Reviewed with permission notesreview

Usable candidate, but the agent should surface permission and audit notes before installation.

Require human approval before installing into a real workspace.

Resolve via API

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

medium

Database access

Skill may inspect schemas, query databases, or work with persistent stores.

  • Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.

Install targets

Install this skill in your agent workflow

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install sesori-ai-sesori-plan-maker

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use sesori-plan-maker in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20sesori-plan-maker%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/sesori-ai-sesori-plan-maker/install
Install command: npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use sesori-plan-maker for this task. Review https://www.openagentskill.com/api/skills/sesori-ai-sesori-plan-maker/install, then install with: npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

67/100

Coding agents

Platforms

Claude Code

Audit report

Needs review · 79/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for Coding agents

Prototype with this skill first; keep a fallback candidate ready.

67
Readiness
Prototype
Stage

Role in stack

Fallback candidate

Primary fit

Coding agents

Trust label

Prototype first

Install path

Command ready

Use when

  • Coding agents workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 67/100 quality profile
  • 2 OpenAgentSkill engagement events

review first

  • Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.

Implementation path

  1. 1Install it in a sandbox agent and run one Coding agents task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Sandbox only

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

67
OpenAgentSkill Trust Score

GitHub adoption

INFO

105 GitHub stars

Stars/forks activity

CHECK

105 stars, 6 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

PASS

NOASSERTION

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.
  • Quality score needs review
  • Stars/forks activity: 105 stars, 6 forks; issue activity unavailable in current metadata
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Run only in a sandbox and compare close alternatives before using it for real work.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

67
GitHub stars
105
Freshness
Today
Install ready
Yes
License
NOASSERTION
Review before install: Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

--- name: sesori-plan-maker description: Create or update practical, code-informed plans and trackers. Use ONLY when the user explicitly asks to make or change a plan or tracker. It may also self-invoke while planning a new feature, larger refactor, or other large effort that would benefit from multiple steps or PR splits. Do not self-invoke for routine implementation, small fixes, or ordinary single-step work. ---

# Plan Maker

When this skill is loaded, turn a user's goal into a practical implementation plan grounded in the current codebase. Keep the process proportional to the work. Prefer a short useful plan over a large planning system.

## User Direction

The user has final authority. Do not reject a request merely because it is not planning work or is outside this skill's usual duty.

If a request is clearly outside planning and the user has not already acknowledged that, say so briefly and ask once whether they want you to proceed. If they confirm, or if they already explicitly told you to proceed despite the planning context, do the work without questioning the choice again. This includes implementation, tests, configuration, Git tasks, and plan updates when permitted by the active environment.

Follow the user's latest explicit instruction when it conflicts with an older plan or process preference. Explain concrete risks when useful, but do not use the role, a plan, or a reviewer as a reason to overrule a confirmed decision.

## Planning

- Inspect relevant repository instructions, code, tests, history, and external references before making assumptions. - Ask only questions that materially affect the result and cannot be answered from available context. Avoid exhaustive interviews and arbitrary checklists. - Make scope, current behavior, proposed changes, ownership/data flow, important compatibility concerns, and verification concrete enough to implement. - Scale detail to the task. A small change may need only a concise plan in chat; a multi-step effort may benefit from durable files under `.plan/active/<slug>/`. - When updating an existing plan, preserve its useful structure rather than forcing a new schema. Keep its tracker or execution state in sync when needed. - Do not invent stages, waves, PR boundaries, worktrees, or process artifacts unless they help the current work or the user asks for them. - When intentionally splitting any task across multiple PRs, require every PR title to use `<emoji> [<slug>] <description> [step <x>/<y>]`. For durable planned work, `<slug>` is exactly the plan directory name under `.plan`; do not invent a separate series slug. Without a durable plan, choose one stable, lowercase kebab-case slug. Fix the step order/total for the whole series, including each step's complexity emoji, and do not apply the slug/step wrapper to a single-PR task. - Target no more than 1,500 changed lines per PR as a soft cap, counting additions plus deletions, generated code, and tests. Prefer a coherent split before exceeding it; when a smaller independently valid PR is not practical, record the reason for the expected overage in the plan. - For durable planned work, the first PR step always raises the plan under `.plan/active/<slug>/` before implementation begins. The penultimate step reconciles and completes the affected feature documents under `docs/regression/`. The final step runs the level and matrix already recorded in `PLAN.md`, records the result, and retires the plan by moving it to `.plan/completed/<slug>/` only after that coverage passes. Include all three lifecycle steps in the fixed step total.

For a new durable plan, `PLAN.md` should normally capture the goal, scope, relevant current behavior, concrete implementation steps, verification, and material risks or decisions. Add a lightweight `TRACKER.md` or step files only when they will help execution.

The plan must identify affected regression feature documents, the highest coverage level needed for the delivered behavior, and any required plugin, platform, client, packaged, or external-service matrix. Follow the proof-boundary and retirement rules in `docs/regression/README.md`; choose enough coverage to prove every materially delivered behavior through its complete authoritative boundary, never a lower level merely because it is cheaper. Any reduction to the recorded matrix requires explicit user acceptance in `PLAN.md` before retirement.

## Evidence And Proportionality

### Prefer Elegant, Low-State Designs

- Before adding persistence or coordination, inspect existing fields, event shapes, and relevant Git history. Reuse a semantically adequate signal and narrow the product claim when needed rather than duplicating state solely to manufacture perfect provenance for a low-impact heuristic. - Treat every new mutable field, map, queue, registry, timer, subscription, dedupe set, pending state, and lifecycle hook as a new failure point with an ongoing maintenance cost. Count mutable parts explicitly before accepting a design, not only changed lines or PR size. - First find the narrowest existing owner that already knows the authoritative outcome. Prefer one post-success write at that seam over reconstructing intent later from events, payload shapes, timing, or backend-specific classifiers. - A backend-neutral behavior should not require custom production logic in each plugin unless the behavior genuinely depends on backend semantics. If a plan touches every plugin to infer the same product fact, treat that as a design alarm: look for a bridge-core action or normalized contract that already owns the fact, or narrow the promised behavior. - Prefer an honest product limitation over machinery that guesses unobservable provenance. Supporting fewer authoritative flows cleanly is better than claiming broad support through dedupe caches, correlation state, reconnect reconciliation, and plugin-specific heuristics. - Before finalizing a plan, include a complexity budget: name the new persistent and in-memory mutable parts, justify each one, and state which tempting pieces are deliberately not being added. If the feature's coordination machinery is larger than its primary behavior, redesign or ask the user before proceeding. - When review feedback adds mutable coordination one edge case at a time, stop and reconsider the root seam instead of accumulating guards. Do not let a sequence of locally valid findings turn a simple behavior change into a state machine without explicit user approval.

- Classify each planned safeguard as addressing an observed failure, an ordinary reachable user flow, or a theoretical interleaving. A reviewer suggestion or a test that can synthetically force a race is not by itself product evidence. - Before adding coordination, state the concrete flow, user/data consequence, and what happens if nothing changes. Account for existing ordering, retries, recovery, idempotency, and refresh behavior instead of assuming every transient state must be made impossible. - Require observed evidence or a plausible ordinary flow with meaningful impact before adding locks, lanes, registries, provisional states, lifecycle owners, compatibility paths, or exhaustive cross-repository filtering. Explicitly accept bounded transient or self-healing behavior when its impact is minor. - Prefer the coarsest simple mechanism that preserves the required invariant. Do not add per-resource concurrency, parallelism, or bypass closure when a small serialized domain boundary is sufficient and throughput is unproven. - Treat cross-cutting coordination as a scope alarm. If an unobserved safeguard grows into shared state across several owners/layers, materially exceeds its estimate, or becomes comparable in size to the primary feature, stop and ask the user whether that risk justifies the complexity before planning or applying more fixes. - Re-run this proportionality check when architecture review or PR feedback expands scope. Apply findings that protect the approved core behavior, but do not treat architectural completeness as a reason to implement increasingly defensive machinery around a low-impact theoretical edge. - For durable plans, record both the evidence level and any intentionally accepted risk. This keeps later reviewers from reopening a declined theoretical concern without new evidence.

## PR Complexity and Communication

Assign every planned or opened PR one implementation-complexity level represented by its fixed emoji:

- `🌱` — trivial: isolated documentation, copy, or mechanical work; - `🌿` — straightforward: localized implementation with a small blast radius; - `⚙️` — moderate: several files or layers, meaningful state, or notable edge cases; - `🚧` — complex: cross-layer flow, persistence, concurrency, lifecycle, compatibility, or security-sensitive behavior; and - `🚨` — very complex: several coupled high-complexity concerns or a broad, high-stakes migration.

Complexity describes implementation and review difficulty, not risk by itself. Choose it from the actual coupling, state transitions, migration/codegen, concurrency, compatibility, privacy/security, and verification burden; do not rate every PR in a series identically by default.

For a single-PR task, prefix the normal title with `<emoji>`. For a multi-PR task, place the emoji first: `<emoji> [<slug>] <description> [step <x>/<y>]`. Treat the emoji as part of the fixed exact title. If implementation evidence changes the estimate before the PR opens, update the plan/tracker title rather than knowingly publishing a stale rating.

Make every planned PR concrete enough that its eventual PR body can briefly and clearly state:

- **Complexity:** level plus a one-sentence rationale; - **What:** what the PR changes; - **Why:** why that change is needed now; - **Risk and test focus:** risk level, potentially impacted flows, screens, data, integrations, or functionality, and the highest-value checks; and - **Expected result:** what a reviewer should observe after running it, explicitly covering user-visible behavior, persisted/database changes, and pure internal/refactor effects as applicable.

Use an explicit `None` or `No user-visible/database change` rather than omitting a category. Keep these summaries proportional; they are an operational review aid, not a duplicate design document.

Whenever you create or materially update a PR yourself, render those categories as `## Complexity`, `## What`, `## Why`, `## Risk and test focus`, and `## Expected result`, followed by the relevant verification section. Use real multiline Markdown through `--body-file` or stdin.

## Cleanup Assessment

For every feature plan, actively inspect what the new behavior makes obsolete. Consider calculations and data generation, model fields, database columns, transport fields, caches, flags/settings, jobs/watchers/listeners, compatibility paths, UI state, tests, and documentation. Look for causal cleanup such as data that no longer needs to be generated, persisted, transported, or rendered.

Record one honest outcome in the plan:

- include small, safe, directly caused cleanup in the appropriate feature PR; - place a larger but valuable cleanup in its own coherent planned PR; - defer cleanup when migration, compatibility, rollout, or risk requires it and state the reason; or - state that no relevant cleanup was found.

Do not keep obsolete artifacts solely for auditing when Git history already preserves them. Cleanup is still not permission for speculative scope growth: preserve required wire/data compatibility, and explain approximate size and ask the user before planning a considerable refactor.

## Plan Review

Use `architecture-plan-review` only for architecture-bearing production plans, as defined by repository instructions. Ask a sub-agent to perform the review using the skill. Apply valid findings directly and do

Technical details

Version
1.0.0
License
NOASSERTION
Last updated
Aug 23, 2026
Published
Aug 23, 2026

Decision snapshot

Fallback candidate

67
Ready
Prototype
Stage

recent repository activity

Audit

Install review

Install and adoption review

79
Needs review
Security
80/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for sesori-plan-maker, ready for a manual X post.

Curator note
sesori-plan-maker: Create or update practical, code-informed plans and trackers. Use ONLY when the user explicit...

105 stars

https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker?ref=x
Open X draft
Optional reply with install command
Listing + install path for sesori-plan-maker:
https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker?ref=x

Install: npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker

Listing source

Registry indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Creator
sesori-ai
Indexed by
OpenAgentSkill community index

Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.

Claim this skill

Owner claim

Claim this skill listing

This Registry indexed listing is attributed to sesori-ai but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

Creator backlink kit

Add the evidence badges to your README

Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/sesori-ai-sesori-plan-maker?metric=listed&label=Listed)](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/sesori-ai-sesori-plan-maker?metric=trust&label=Trust)](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/sesori-ai-sesori-plan-maker?metric=audit&label=Audit)](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/sesori-ai-sesori-plan-maker?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker)

Author

S

sesori-ai

@sesori-ai

Platform fit

Health signals

GitHub stars
105
Quality score
37/100
Last GitHub push
Aug 23, 2026
Framework hints
Unknown
OpenAgentSkill views
2
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Sandbox only

67
  • GitHub adoption105 GitHub starsINFO
  • Stars/forks activity105 stars, 6 forks; issue activity unavailable in current metadataCHECK
  • Recent maintenancePushed todayPASS
  • License clarityNOASSERTIONPASS
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime riskdatabase surfacePASS