adhd

Prüfen · 65
Im Registry indexiert

Parallel divergent ideation — spawns N isolated generator agents under different cognitive frames (regulator, biology, speedrunner, 10-year-old, zero-budget), then a critic pass scores, clusters, prunes traps, and deepens the top 3. Use for open-ended design, architecture, naming

Verified installs0
Stars42
Version1.0.0
Qualität63/100 · Vielversprechend
Vertrauen65/100 · Nur Sandbox
Audit77/100 · Prüfung nötig

Asset-Profil

Coding- und Entwickler-Agents

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

Bereich ansehen

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 darkroomengineering/cc-settings --skill adhd

Wartung

Aktuell

2 Tage seit dem letzten Push

Risiko

Prüfung nötig

Dependency or permission surface needs review

GitHub-Qualität

42

63/100 Qualität · 73/100 Vertrauen

Abdeckungs-Tags

CodingCoding-AgentsDesign und Kreativitätagent-skill

Review-Notizen

Dependency or permission surface needs review · Permission surface may require sandboxing

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

Vielversprechend
63

Useful candidate, but compare it with alternatives before adopting.

Vertrauen

Nur Sandbox
65

Nützlicher Kandidat mit fehlenden oder gemischten Vertrauenssignalen. Bis der Ergebniszyklus die Passung belegt, in einem isolierten Arbeitsbereich verwenden.

Audit

Prüfung nötig
77

Maschinenlesbare 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.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

42 GitHub-Stars

Repository-Aktivität

42 Stars und 3 Forks

Wartung

2 Tage seit dem letzten Push

Lizenz

MIT

Installieren

npx skills add darkroomengineering/cc-settings --skill adhd

Installationssicherheit

Standard-Paket- oder Laufzeit-Installationspfad

Berechtigungsfläche

shell or command execution, network or browser access

Agent-Ergebnisse

Noch keine Agent-Ergebnisdaten

Dokumentation

Starker README/SKILL.md-Kontext

Risikoübersicht

Vor Produktion prüfen

  • Financial research output is not financial advice; require human review before any live investment decision.
  • Low GitHub adoption signal
  • Quality score needs review
  • Permission surface needs review: shell or command execution, network or browser access

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.

JSON öffnen

Geeignete Aufgaben

  • Coding-Agents-Workflows
  • Claude-Code-Teams
  • builders willing to evaluate younger projects
  • Inspect source files

Geeignete Agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Installationsentscheidung

Befehl
npx skills add darkroomengineering/cc-settings --skill adhd
Richtlinie
Prüfen
Menschliche Prüfung
Ja

Vertrauen und Risiko

Vertrauen
65/100
Audit
77/100
Risikoebene
Prüfung nötig

Ergebnis-Loop

Endpoint
/api/agent/outcome
Event-ID
resolve
Ergebnisse
5

Installationsbefehl

npx skills add darkroomengineering/cc-settings --skill adhd

Nicht verwenden, wenn

  • Teams, die ein vom Anbieter unterstütztes SLA benötigen
  • production agents without a repository review
  • Low GitHub adoption signal
  • Hinweise auf Hochrisiko-Berechtigungen: Shell- oder Befehlsausführung
  • Dependency or permission surface needs review

Agent-Sicherheit v2

45/100 · Automatische Installation vermeiden

ExperimentellPrüfen

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

Test manually in an isolated workspace and compare against safer alternatives.

Per API auflösen

Hoch

Shell- oder Befehlsausführung

Die Skill-Metadaten verweisen auf Terminal-, CLI-, Shell-, Subprozess- oder Befehlsausführungs-Workflows.

Mittel

Browser automation

Skill may drive a browser or interact with web pages.

Mittel

Netzwerkzugriff

Die Skill ruft wahrscheinlich Remote-Seiten, APIs, Repositories oder externe Dienste ab.

Mittel

Datenbankzugriff

Die Skill kann Schemata prüfen, Datenbanken abfragen oder mit persistenten Speichern arbeiten.

  • Hinweise auf Hochrisiko-Berechtigungen: Shell- oder Befehlsausführung
  • Dependency or permission surface needs review

Installationsziele

Diesen Skill im Agent-Workflow installieren

Über den öffentlichen Endpunkt erhältst du Befehl, Sicherheitscheckliste, Ziel-Prompts und kanonische Links.

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 darkroomengineering-adhd

Agent-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.

Textplan öffnen

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 adhd in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20adhd%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/darkroomengineering-adhd/install
Install command: npx skills add darkroomengineering/cc-settings --skill adhd
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.

Installations-API öffnen

Agent-Prompt

Use adhd for this task. Review https://www.openagentskill.com/api/skills/darkroomengineering-adhd/install, then install with: npx skills add darkroomengineering/cc-settings --skill adhd

Registry-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 öffnen

Agent-Fit

63/100

Coding-Agents

Plattformen

Claude Code

Audit-Bericht

Prüfung nötig · 77/100

Maschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.

Audit-Bericht ansehenEval-Bericht ansehen

Agent-Entscheidungspanel

Fallback candidate for Coding agents

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

63
Bereitschaft
Prototyp
Phase

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 63/100
  • 3 OpenAgentSkill-Interaktionen

zuerst prüfen

  • Low GitHub adoption signal

Implementierungspfad

  1. 1Installieren Sie es in einem Sandbox-Agent und führen Sie eine Coding-Agents-Aufgabe vollständig aus.
  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.

Vertrauensprofil

Nur Sandbox

Nützlicher Kandidat mit fehlenden oder gemischten Vertrauenssignalen. Bis der Ergebniszyklus die Passung belegt, in einem isolierten Arbeitsbereich verwenden.

65
OpenAgentSkill Trust Score

GitHub-Akzeptanz

Prüfen

42 GitHub-Stars

Star-/Fork-Aktivität

Prüfen

42 Stars und 3 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbar

Aktuelle Wartung

Bestanden

2 Tage seit dem letzten Push

Lizenzklarheit

Bestanden

MIT

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

  • Financial research output is not financial advice; require human review before any live investment decision.
  • Low GitHub adoption signal
  • Quality score needs review
  • Permission surface needs review: shell or command execution, network or browser access
  • GitHub adoption: 42 GitHub stars
  • Stars/forks activity: 42 stars, 3 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, network or browser surface
  • Permission surface: shell or command execution, network or browser access
  • 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.

63
GitHub-Stars
42
Aktualität
vor 2 Tagen
Installationsbereit
Ja
Lizenz
MIT
Vor Installation prüfen: Low GitHub adoption signal

Workflow-Eignung

Diese Skill in diesen Szenarien nutzen

Workflow-Eignung

Zum vollständigen Workflow hinzufügen

Alternativen-Shortlist

Vor Installation vergleichen

Similar skills that may fit this task.

Alle vergleichen

Übersicht

--- name: adhd argument-hint: "[problem]" description: Parallel divergent ideation — spawns N isolated generator agents under different cognitive frames (regulator, biology, speedrunner, 10-year-old, zero-budget), then a critic pass scores, clusters, prunes traps, and deepens the top 3. Use for open-ended design, architecture, naming, API/SDK surface, and fuzzy debugging where the obvious answer is expensive to get wrong. Triggers "/adhd", "adhd mode", "brainstorm", "ideate", "widen the option space", "divergent ideas", "we keep landing on the same idea". Skip for lookups, syntax, bugs with a known root cause, or closed phrasing ("quick", "standard", "canonical", "textbook"). Use /oracle compare to evaluate options you already have — adhd generates the option space; use /plan-ceo-review to challenge whether to build at all. context: main license: MIT ---

# ADHD

Stop picking the textbook answer. The first three answers the model would give are the answers a senior engineer would give in thirty seconds. Correct. Forgettable. The interesting answers live past number three, in the awkward middle nobody walks into. This skill makes the model walk there.

## When to use vs siblings

- `/adhd` — **generate** the option space when you don't have candidates yet. - `/oracle` (compare mode) — **evaluate** options you already have. - `/plan-ceo-review` — challenge whether the thing should be built at all. - `/verify` — adversarially check a conclusion you've already reached.

They compose: `/adhd` to widen, `/oracle` to weigh the shortlist.

## Pre-flight (run before Phase 1)

This skill is expensive. About 10 Agent calls, 30 to 90 seconds wall clock, 5 to 10x a single answer. Do not pay that cost when a direct answer is better. Run this gate before Phase 1.

**Step 1. Explicit invocation check.**

If the user typed `/adhd` or explicitly asked for ADHD mode, "use the adhd skill", or "run ADHD on this", **SKIP the rest of this section and go straight to Phase 1**. The user opted in. Do not second-guess.

**Step 2. Self-judge (only if Step 1 did not match).**

Ask yourself three questions. If the answer to any is no, ABORT.

1. **Open-ended?** Would a senior engineer give multiple viable answers here, or is there one canonical answer? If canonical, abort. 2. **High-stakes?** Is the cost of the obvious answer being wrong actually high? Architecture decisions, public API surfaces, naming a real product, fuzzy bugs with no known root cause, schema design = yes. Side project at 11pm = no. 3. **Open phrasing?** Did the user avoid words like "quick", "standard", "canonical", "textbook", "just", "one-line"? If they used any of those, they want the direct answer. Abort.

If all three checks pass, proceed to Phase 1.

If any fails, ABORT and answer the question directly. Optionally append one sentence: *"If you want a wider exploration under parallel cognitive frames with explicit trap detection, run `/adhd <your problem>`."*

## The loop

Two strict phases. Mixing them kills idea quality, because the critic strangles the generator.

### Phase 1 — Diverge (no critic)

For the problem P:

1. Pick 5 cognitive frames from the table below. Bias toward engineering tags when the problem is code-shaped. Always include at least one wild frame to keep range.

2. Spawn 5 **parallel** Agent tool calls in ONE message. One per frame. Each Agent gets only: - the problem P - any context the user provided - the chosen frame's vantage prompt - a system instruction that forbids evaluation

The exact instruction to give each Agent:

> You are in DIVERGENT mode. You are a generator, not a critic. > Generate 6 short distinct ideas under this frame. Each idea is one > phrase or one sentence. Do not evaluate. Do not rank. Do not hedge. > The first three obvious answers everyone would give are banned. > Push past them into the awkward middle. > Output a JSON array only. No prose before or after. > `[{"text": "...", "rationale": "..."}, ...]`

3. **Critical invariant.** The Agent calls must be parallel and isolated. Do NOT serialize them. Do NOT pass one branch's output as context to another. Branches that see each other anchor each other and the whole method collapses to a wider single thought.

### Phase 2 — Focus (critic on)

After all branches return:

1. **Score.** Rate each idea on three axes 0 to 10: novelty (distance from the obvious default), viability (could it actually ship), fit (does it address the stated problem). For any idea that looks attractive but is a trap (hidden cost, false economy, will not scale, premature abstraction), flag it with a one-line reason.

2. **Cluster.** Group ideas into 3 to 6 clusters by their underlying angle, not by surface keywords. Label clusters by angle: "remove the server plays", "cache-shaped plays", "batched-window plays", "race-multiple- backends plays".

3. **Deepen the top 3.** Rank by weighted score (novelty 0.35 + viability 0.40 + fit 0.25), exclude traps, take top 3. For each, spawn one Agent call that produces: - a 4 to 8 sentence sketch of how the idea works - the load-bearing risk - the first concrete step a builder would take - 3 to 5 child ideas (variations, hybrids, unlocks)

Deepen Agent instruction:

> You are in FOCUS mode. Take one promising idea and connect dots. > Sketch how it would actually work in 4 to 8 sentences. Name the > load-bearing risk. Name the first concrete step a coder would take. > Then generate 3 to 5 sub-ideas that branch off (variations, > combinations with other domains, things this unlocks). > Output JSON only.

Scoring, clustering, and the final synthesis stay in the main session — that is judgment work and belongs on the session's top-tier model. The generator and deepen agents are fan-out subagents and inherit `CLAUDE_CODE_SUBAGENT_MODEL` (Sonnet), which fits the quota doctrine: roomy pools carry volume, the scarce pool does the judging.

## Frames

Pick 5 per run.

| Frame | Vantage prompt | Tags | |---|---|---| | **hardware engineer** | You think in latency, memory layout, and physical constraints. Re-ask this as a hardware/firmware problem. What does the bus topology, cache, timing budget tell you? | code, wild | | **regulator** | You audit systems for compliance and failure modes. What must be provable, traceable, or refusable here? | design, general | | **10-year-old** | You are a curious 10 year old who has never seen software. Describe naive but unencumbered approaches. Ignore convention. | general, wild | | **competitor trying to break it** | You are a hostile competitor or attacker. Generate approaches that exploit, fail, or sabotage the obvious solution. Then invert into ideas. | code, design | | **biology** | Transplant a mechanism from biology (immune systems, neural plasticity, cell signaling, evolution, gut flora). Force-fit it onto this engineering problem. | code, wild | | **logistics** | Steal mechanisms from logistics: queues, batching, just-in-time, hub-and-spoke, returns, last-mile. Apply them literally. | code, design | | **game design** | Approach this as a game designer. What are the loops, rewards, friction, save-states, speedrun tricks? Treat the user as a player. | design, general | | **markets** | Treat the problem as a market. Buyers, sellers, market-makers. What does an auction, a futures contract, a clearing house look like here? | design, wild | | **inversion** | Ask the OPPOSITE question. If goal is X, brainstorm how to guarantee NOT X. Then negate each answer back. | code, design, general | | **extreme: $0 budget, 1 hour** | No money, no team, one hour. What is the crudest version that still does the load-bearing thing? | code, general | | **extreme: infinite budget, 10 years** | Infinite compute, infinite engineers, a decade. What is the maximalist version? | design, wild | | **remove the load-bearing assumption** | Name the thing everyone treats as fixed (framework, database, request-response model, network). Imagine it is gone. What is possible? | code, design, wild | | **speedrunner** | You are a speedrunner. Find glitches, skips, out-of-bounds tricks, frame-perfect shortcuts. What is the abusive-but-legal path? | code, wild | | **ant colony** | No central planner. Many dumb agents, local rules, pheromone trails. How does the problem solve itself emergently? | code, wild | | **3am on-call** | You are the on-call engineer woken at 3am when this breaks. What design would let you not get paged? | code, design |

### Picking frames

For code-shaped problems: pick 4 frames tagged `code` or `design`, plus 1 tagged `wild`. For open product or strategy problems: a mix from all tags. Vary the picks across sessions so the same problem produces different candidate sets when re-run.

## Output shape

After Phase 2, render in this order. Do not collapse it into a wall of prose. The structure is the point.

1. **Brief.** One or two lines confirming the problem and any reframe used. 2. **Wide set.** Full pool grouped by cluster. Each cluster labeled by underlying angle. Each idea is one short phrase. Show score chips like `[N7 V8 F9]` next to each. 3. **Converge.** A 2 to 4 idea shortlist. State why each is on the list. Mark the non-obvious-but-viable pick explicitly with ★. List traps separately, each with the one-line reason it is a trap. 4. **Focus.** The 3 deepened branches. For each: the sketch, the load- bearing risk, the first concrete step, and the child ideas. 5. **Provocation.** One wildcard question or idea that opens a new direction the user can push into if nothing landed.

## Anti-patterns

These are how this skill goes wrong. Watch for them.

- **Convergence disguised as divergence.** Ten minor variations of one idea is not breadth. If every candidate shares the same underlying assumption, you have not diverged. You have decorated. - **Weird-for-weird's-sake with no convergence.** A pile of 30 unsorted absurdities is as useless as one safe answer. Always converge. - **Walls of equally-weighted prose.** Cluster, label, pull out the best. Structure is half the value. - **Refusing to commit.** After diverging, take a position on what is actually promising. "Here are 20 ideas, you decide" is a cop-out. Generate wide, but converge with a real opinion. - **Skipping the isolation invariant.** If you simulate parallel branches by writing them sequentially in one context, you have not done ADHD. You have done a wider single thought. The Agent tool gives each branch a fresh context. Use it.

## Calibration

- **How many ideas?** Scale to stakes. Quick "name this function" = 3 frames × 4 ideas. "How should I position this product" = 5 frames × 8 ideas. Default is 5 × 6 = 30. - **How weird?** Read the room. Serious strategy work: flag the wild cards clearly so they do not read as unserious. Open brainstorming or play: let it run loose. Absurd ideas earn their place by seeding viable ones. - **When to stop diverging?** Stop when new candidates start repeating the shape of existing ones. The space is mapped. Do not pad to hit a number.

## Cost

5 diverge + 1 score + 1 cluster + 3 deepen ≈ 10 Agent calls per run. About 5 to 10x a single-shot answer. Not for every keystroke. For decision points where the cost of the obvious answer is high. Diverge/deepen agents run on the Sonnet subagent pool, so the Opus/Fable cost of a run is one synthesis pass.

## Attribution

Ported from [`UditAkhourii/adhd`](https://github.com/UditAkhourii/adhd) (MIT). Upstream ships the same loop as an npm CLI (`adhd-agent`) plus evals and a source spec on divergent ideation; this port keeps the skill-only form (no install) and adds the cc-settings sibling-skill routing and subagent model notes.

Technische Details

Version
1.0.0
Lizenz
MIT
Letzte Aktualisierung
20. Aug. 2026
Veröffentlicht
20. Aug. 2026

Entscheidungsübersicht

Fallback-Kandidat

63
Bereit
Prototyp
Phase

recent repository activity

Audit

Installationsprüfung

Installations- und Adoptionsprüfung

77
Prüfung nötig
Sicherheit
78/100
Wartung
100/100
Installieren
92/100
Vollständiges Audit öffnenEval-Bericht ansehen

Von Agent belegte Evidenz

Von Agent belegte Evidenz

Ergebnisberichte nach Resolve, Prüfung, Installation und einem begrenzten Lauf.

0
Belegt
Needs first agent runAuto-Installation: zuerst prüfenLetzter: Unbekannt
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

X

Szenariobasierter Entwurf für adhd, bereit für einen manuellen X-Post.

Kuratorenhinweis
adhd: Parallel divergent ideation — spawns N isolated generator agents under different cognitive fr...

42 stars

https://www.openagentskill.com/skills/darkroomengineering-adhd?ref=x
X-Entwurf öffnen
Optionale Antwort mit Installationsbefehl
Listing + install path for adhd:
https://www.openagentskill.com/skills/darkroomengineering-adhd?ref=x

Install: npx skills add darkroomengineering/cc-settings --skill adhd
Antwortentwurf öffnen

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

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 beanspruchen

Eigentümeranspruch

Diesen Skill-Eintrag beanspruchen

Dieser Registry-indexiert-Eintrag wird darkroomengineering 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.

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

Autor

D

darkroomengineering

@darkroomengineering

Plattform-Fit

Gesundheitssignale

GitHub-Stars
42
Qualitätswert
35/100
Letzter GitHub-Push
20. Aug. 2026
Framework-Hinweise
Unbekannt
OpenAgentSkill-Aufrufe
3
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

65
  • GitHub-Akzeptanz42 GitHub-StarsPrüfen
  • Star-/Fork-Aktivität42 Stars und 3 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbarPrüfen
  • Aktuelle Wartung2 Tage seit dem letzten PushBestanden
  • LizenzklarheitMITBestanden
  • README/SKILL.md-VollständigkeitMetadaten enthalten ausreichend Nutzungs- und Workflow-KontextBestanden
  • Abhängigkeits-/Laufzeitrisikocommand execution surface, network or browser surfacePrüfen