sesori-plan-maker
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
Asset-Profil
Coding- und Entwickler-Agents
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Szenario
Coding-Agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Agent-Fit
Claude Code + CLI + Codex
Geeignet für Codex, Claude Code, Cursor, CLI oder benutzerdefinierte Agents.
Installieren
Bereit
npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker
Wartung
Aktuell
Heute gepusht
Risiko
Prüfung nötig
Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.
GitHub-Qualität
105
67/100 Qualität · 75/100 Vertrauen
Abdeckungs-Tags
Review-Notizen
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-Adoptionskarte
Vertrauen, Audit und Installationsbereitschaft auf einen Blick
Diese Werte kombinieren öffentliche Repository-Metadaten, OpenAgentSkill-Reviewsignale, Wartungsaktualität und Installationsbereitschaft. Sie helfen bei der Vorauswahl, ersetzen aber keine menschliche Prüfung.
Qualität
VielversprechendUseful candidate, but compare it with alternatives before adopting.
Vertrauen
Nur SandboxNützlicher Kandidat mit fehlenden oder gemischten Vertrauenssignalen. Bis der Ergebniszyklus die Passung belegt, in einem isolierten Arbeitsbereich verwenden.
Audit
Prüfung nötigMaschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
OpenAgentSkill Trust Score v5
Menschliche Prüfung vor Installation
Nur in einer Sandbox ausführen und nahe Alternativen vergleichen, bevor sie produktiv eingesetzt wird.
Stars
105 GitHub-Stars
Repository-Aktivität
105 Stars und 6 Forks
Wartung
Heute gepusht
Lizenz
NOASSERTION
Installieren
npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker
Installationssicherheit
Standard-Paket- oder Laufzeit-Installationspfad
Berechtigungsfläche
filesystem or document access, database access
Agent-Ergebnisse
Noch keine Agent-Ergebnisdaten
Dokumentation
Starker README/SKILL.md-Kontext
Risikoübersicht
Vor Produktion prüfen
- 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
Installationsbereitschaft
Installationspfad verfügbar
- Installationspfad ist verfügbar
- Repository-Belege sind verfügbar
- Lizenz ist angegeben
- Noch keine Agent-Proven-Ergebnisbelege
Agent-lesbare Metadaten
Maschinenlesbare Entscheidungsdaten für diesen Skill.
Nutze diesen Block oder das eingebettete JSON, um zu entscheiden, ob ein Agent diesen Skill installieren, eine Alternative wählen oder zuerst menschliche Prüfung anfordern soll.
Geeignete Aufgaben
- Coding-Agents-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
- Inspect source files
Geeignete Agents
Installationsentscheidung
- Befehl
- npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-maker
- Richtlinie
- Prüfen
- Menschliche Prüfung
- Ja
Vertrauen und Risiko
- Vertrauen
- 67/100
- Audit
- 79/100
- Risikoebene
- Prüfung nötig
Ergebnis-Loop
- Endpoint
- /api/agent/outcome
- Event-ID
- resolve
- Ergebnisse
- 5
Installationsbefehl
npx skills add sesori-ai/sesori_apps_monorepo --skill sesori-plan-makerNicht verwenden, wenn
- Teams, die ein vom Anbieter unterstütztes SLA benötigen
- 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
Alternative
Code Review
168.6K Stars
npx skills add mattpocock/skills --skill code-review
Alternative
Grill With Docs
164.7K Stars
npx skills add mattpocock/skills --skill grill-with-docs
Alternative
To Spec
164.7K Stars
npx skills add mattpocock/skills --skill to-spec
Alternative
To Tickets
176.7K Stars
npx skills add mattpocock/skills --skill to-tickets
Agent-Sicherheit v2
59/100 · Vor Installation prüfen
Nutzbarer Kandidat, aber der Agent sollte Berechtigungs- und Auditnotizen vor der Installation anzeigen.
Vor der Installation in einem echten Arbeitsbereich ist menschliche Freigabe erforderlich.
Mittel
Netzwerkzugriff
Die Skill ruft wahrscheinlich Remote-Seiten, APIs, Repositories oder externe Dienste ab.
Mittel
Dateisystemzugriff
Die Skill kann Projektdateien, Dokumente, generierte Artefakte oder den lokalen Arbeitsbereich lesen oder schreiben.
Mittel
Datenbankzugriff
Die Skill kann Schemata prüfen, Datenbanken abfragen oder mit persistenten Speichern arbeiten.
- Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.
Installationsziele
Diesen Skill im Agent-Workflow installieren
Über den öffentlichen Endpunkt erhältst du Befehl, Sicherheitscheckliste, Ziel-Prompts und kanonische Links.
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-makerAgent-Auflösungsplan
Lass einen Agent die Eignung vor der Installation prüfen.
Die Resolve API liefert die beste Skill, Alternativen, Sicherheitsrichtlinien, Auditnotizen, Installationsziel und einen direkt nutzbaren Prompt.
JSON öffnen
/api/agent/resolve?task=Use%20sesori-plan-maker%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve-Text
/api/agent/resolve?task=Use%20sesori-plan-maker%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Installationsübergabe
/api/skills/sesori-ai-sesori-plan-maker/install
Agent sollte prüfen
- 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.
Prompt kopieren
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-Übergabe
Gib dem Agent den Installationspfad, nicht noch ein Verzeichnis.
Über den öffentlichen Endpunkt erhältst du Befehl, Sicherheitscheckliste, Ziel-Prompts und kanonische Links.
Installationsübergabe
/api/skills/sesori-ai-sesori-plan-maker/install
LLM-Textformat
/api/skills/sesori-ai-sesori-plan-maker/install?format=text
Alternativen finden
/api/skills/search?q=sesori-plan-maker&limit=3
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-makerRegistry-Metadaten
Agent-lesbares Profil für die automatische Skill-Auswahl.
Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.
Manifest
/api/registry/manifest/sesori-ai-sesori-plan-maker
LLM-Text
/api/registry/manifest/sesori-ai-sesori-plan-maker?format=text
Installationsalias
/api/registry/install/sesori-ai-sesori-plan-maker
Empfehlen
/api/registry/recommend?task=Use%20sesori-plan-maker%20in%20an%20agent%20workflow&limit=3
Agent-Fit
Coding-Agents
Use-Case-Tags
Plattformen
Claude Code
Audit-Bericht
Prüfung nötig · 79/100
Maschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
Agent-Entscheidungspanel
Fallback candidate for Coding agents
Prototype with this skill first; keep a fallback candidate ready.
Rolle im Stack
Fallback-Kandidat
Primäre Eignung
Coding-Agents
Vertrauenslabel
Zuerst prototypisieren
Installationspfad
Befehl bereit
Verwenden wenn
- Coding-Agents-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
Evidenz
- recent repository activity
- install command or GitHub repo available
- Qualitätsprofil 67/100
- 2 OpenAgentSkill-Interaktionen
zuerst prüfen
- Repository license is detected as NOASSERTION, meaning no clear open-source license is identified. This creates ambiguity about usage and redistribution rights.
Implementierungspfad
- 1Installieren Sie es in einem Sandbox-Agent und führen Sie eine Coding-Agents-Aufgabe vollständig aus.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Vertrauensprofil
Nur Sandbox
Nützlicher Kandidat mit fehlenden oder gemischten Vertrauenssignalen. Bis der Ergebniszyklus die Passung belegt, in einem isolierten Arbeitsbereich verwenden.
GitHub-Akzeptanz
Info105 GitHub-Stars
Star-/Fork-Aktivität
Prüfen105 Stars und 6 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbar
Aktuelle Wartung
BestandenHeute gepusht
Lizenzklarheit
BestandenNOASSERTION
Positive Signale
- KI-Prüfung genehmigt
- Installationspfad ist verfügbar
- Repository-Belege sind verfügbar
- Kürzlich gewartetes Repository
- Der Installationsbefehl weist kein offensichtliches Hochrisikomuster auf
- Ergebniszyklus ist bereit, benötigt aber den ersten echten Agent-Lauf
Vor Installation prüfen
- 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
- Noch keine echten Agent-Ergebnisberichte
- Vor unbeaufsichtigter Installation ist menschliche Prüfung erforderlich
Empfohlene Aktion
Nur in einer Sandbox ausführen und nahe Alternativen vergleichen, bevor sie produktiv eingesetzt wird.
Qualitätsprofil
Vielversprechend Kandidat für Agent-Workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow-Eignung
Diese Skill in diesen Szenarien nutzen
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Search private knowledge
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Parse messy files
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Workflow-Eignung
Zum vollständigen Workflow hinzufügen
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
RAG knowledge base
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternativen-Shortlist
Vor Installation vergleichen
Similar skills that may fit this task.
Code Review
Review a branch or diff against repository standards and the originating spec in two independent analysis passes.
Grill With Docs
A relentless interview that pressure-tests a plan against the codebase, sharpens domain language, and updates CONTEXT.md and ADRs when decisions become durable.
To Spec
Turn the current conversation and codebase context into a structured implementation spec, then publish it to the configured project issue tracker.
To Tickets
Break a plan, spec, or conversation into independently actionable tracer-bullet tickets with explicit blocking relationships.
Übersicht
--- 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
Technische Details
- Version
- 1.0.0
- Lizenz
- NOASSERTION
- Letzte Aktualisierung
- 23. Aug. 2026
- Veröffentlicht
- 23. Aug. 2026
Entscheidungsübersicht
Fallback-Kandidat
recent repository activity
Audit
Installationsprüfung
Installations- und Adoptionsprüfung
- Sicherheit
- 80/100
- Wartung
- 100/100
- Installieren
- 92/100
Von Agent belegte Evidenz
Von Agent belegte Evidenz
Ergebnisberichte nach Resolve, Prüfung, Installation und einem begrenzten Lauf.
- Erfolgsrate
- —
- Letzter Fehler
- —
- Ergebnisse
- 0
- Ausgabequalität
- —
- Fehlgeschlagen
- 0
- Nicht relevant
- 0
- Installationen
- 0
- Durch Risiko blockiert
- 0
- Einrichtung erforderlich
- 0
- Produktion
- 0
Noch keine Agent-Ergebnisdaten. Der erste Lauf kann Erfolg, Einrichtungsbedarf, Risikoblockaden, Fehler oder Irrelevanz über /api/agent/outcome melden.
Installieren
Zum Agent-Workflow hinzufügen
Kostenlos und Open Source. Bericht vor der Installation in Produktions-Agents prüfen.
Wachstums-Loop
Share-Kit
Szenariobasierter Entwurf für sesori-plan-maker, bereit für einen manuellen X-Post.
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
Optionale Antwort mit Installationsbefehl
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
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- sesori-ai
- Indexiert von
- OpenAgentSkill Community-Index
Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.
Diesen Skill beanspruchenEigentümeranspruch
Diesen Skill-Eintrag beanspruchen
Dieser Registry-indexiert-Eintrag wird sesori-ai zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.
Creator-Backlink-Kit
Evidenz-Badges in deine README einfügen
Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.
[](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker)
[](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker)
[](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker/audit)
[](https://www.openagentskill.com/skills/sesori-ai-sesori-plan-maker)Autor
sesori-ai
@sesori-ai
Tags
Plattform-Fit
Gesundheitssignale
- GitHub-Stars
- 105
- Qualitätswert
- 37/100
- Letzter GitHub-Push
- 23. Aug. 2026
- Framework-Hinweise
- Unbekannt
- OpenAgentSkill-Aufrufe
- 2
- Installationskopien
- 0
- Externe Klicks
- 0
Community-Signal
Teile mit, ob dieser Skill für deinen Agent-Workflow nützlich ist. Zusammengefasstes Feedback verbessert das Ranking im Laufe der Zeit.
Vertrauen & Sicherheit
Nur Sandbox
- GitHub-Akzeptanz105 GitHub-StarsInfo
- Star-/Fork-Aktivität105 Stars und 6 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbarPrüfen
- Aktuelle WartungHeute gepushtBestanden
- LizenzklarheitNOASSERTIONBestanden
- README/SKILL.md-VollständigkeitMetadaten enthalten ausreichend Nutzungs- und Workflow-KontextBestanden
- Abhängigkeits-/Laufzeitrisikodatabase surfaceBestanden
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