demand-first-review

STRONG · 83
Registry indexed

Use when reviewing a PR, API, IPC channel, endpoint, parameter, type, config, or architectural extension point that adds or expands shared surface area, especially when consumers are absent, exports are unused or speculative, existing consumers are hack-heavy, forward compatibili

Verified installs0
Stars50.9K
Version1.0.0
Quality94/100 · Excellent
Trust83/100 · Review then install
Audit91/100 · Safe to try

Supply asset profile

Research and knowledge work

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Browse track

Scenario

Research agents

I need my agent to research a topic, compare sources, and produce a concise report.

Agent fit

Claude Code + CLI + Codex

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

Install

Ready

npx skills add CherryHQ/cherry-studio --skill demand-first-review

Maintenance

fresh

Pushed today

Risk

Safe to try

No major risk signals from available metadata

GitHub quality

51K

94/100 Quality · 86/100 Trust

Coverage tags

ResearchResearch agentsagent-skill

Review notes

No major risk signals from available metadata

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

Excellent
94

High-confidence pick with strong adoption and healthy maintenance signals.

Trust

Review then install
83

Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.

Audit

Safe to try
91

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

OpenAgentSkill Trust Score v5

Agent install candidate

Use as the primary candidate after human or sandbox review.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

51K GitHub stars

Repo activity

51K stars, 4.8K forks

Maintenance

Pushed today

License

AGPL-3.0

Install

npx skills add CherryHQ/cherry-studio --skill demand-first-review

Install safety

standard package or runtime install path

Permission surface

network or browser access, database access

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Low metadata risk

  • No major trust warnings detected from available 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

  • Research agents workflows
  • Claude Code teams
  • teams that value GitHub adoption signals
  • Search sources

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add CherryHQ/cherry-studio --skill demand-first-review
Policy
allow
Human review
no

Trust and risk

Trust
83/100
Audit
91/100
Risk level
Safe to try

Outcome loop

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

Install command

npx skills add CherryHQ/cherry-studio --skill demand-first-review

Do not use when

  • teams that need a vendor-supported SLA
  • high-compliance environments without internal security review
  • No major risk signals from current metadata
  • No major trust warnings detected from available metadata
  • Production credentials, payments, or irreversible account changes without explicit human review

Agent safety v2

83/100 · Safe to install with normal review

Reviewedallow

Good audit and safety signals with no high-risk permission hints in public metadata.

Review the audit page, then allow agent install in a sandboxed workflow.

Resolve via API

medium

Network access

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

medium

Database access

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

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 cherryhq-demand-first-review

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 demand-first-review in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20demand-first-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/cherryhq-demand-first-review/install
Install command: npx skills add CherryHQ/cherry-studio --skill demand-first-review
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 demand-first-review for this task. Review https://www.openagentskill.com/api/skills/cherryhq-demand-first-review/install, then install with: npx skills add CherryHQ/cherry-studio --skill demand-first-review

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

100/100

Research agents

Platforms

Claude Code

Audit report

Safe to try · 91/100

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

View audit reportView eval report

Agent decision cockpit

Primary pick for Research agents

Use this as a leading candidate, then validate the README and install path in your own agent stack.

100
Readiness
Adopt
Stage

Role in stack

Primary pick

Primary fit

Research agents

Trust label

Production-ready

Install path

Command ready

Use when

  • Research agents workflows
  • Claude Code teams
  • teams that value GitHub adoption signals

Evidence

  • 50,908 GitHub stars
  • recent repository activity
  • install command or GitHub repo available
  • 94/100 quality profile
  • 6 OpenAgentSkill engagement events

review first

  • No major risk signals from current metadata

Implementation path

  1. 1Install it in a sandbox agent and run one Research 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

Review then install

Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.

83
OpenAgentSkill Trust Score

GitHub adoption

PASS

51K GitHub stars

Stars/forks activity

PASS

51K stars, 4.8K forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

PASS

AGPL-3.0

Good signals

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

Review before install

  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Use as the primary candidate after human or sandbox review.

Quality profile

Excellent candidate for agent workflows

High-confidence pick with strong adoption and healthy maintenance signals.

94
GitHub stars
51K
Freshness
Today
Install ready
Yes
License
AGPL-3.0

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: demand-first-review description: Use when reviewing a PR, API, IPC channel, endpoint, parameter, type, config, or architectural extension point that adds or expands shared surface area, especially when consumers are absent, exports are unused or speculative, existing consumers are hack-heavy, forward compatibility is claimed, or multiple similar APIs may express one demand. ---

# Demand-First Review

## Principle

Audit this causal chain before implementation quality:

`root outcome or invariant → normalized demand → owning layer → contract → consumer`

A call site proves usage, not legitimacy or shape. No call site raises the burden of proof, not an automatic rejection. Real demand may still have the wrong consumer or abstraction.

## Workflow

Apply every step to each added API, channel, parameter, type, field, config, or extension point.

### 1. Reconstruct the demand

List every new surface and exact consumed dimension; one valid consumer does not justify unused fields. Trace current and linked consumers to their user outcome, business rule, or system invariant. Inspect adjacent implementations, then ask: without the current API and history, would the demand remain and would this contract still be natural? Do not rely only on the PR description.

### 2. Audit consumer legitimacy

- **Legitimate**: uses the correct owner and boundary. - **Compensating**: uses the nearest API and adds workarounds because the right capability is missing. - **Legacy-shaped**: reflects obsolete formats, transitional architecture, or history. - **Misplaced**: serves real demand in the wrong layer.

Parsing, retries, sequencing, duplicated state, check-then-act, or cross-layer access signal compensation. Treat these as unmet upstream demand, never endorsement of the current surface.

### 3. Normalize related demands

Strip names, historical formats, and workarounds from demand statements. Cluster by outcome, source of truth, owner, transaction, security, and lifecycle. Consolidate historical or caller-specific differences; keep contracts separate for genuine ownership, permission, atomicity, lifecycle, side-effect, or failure differences. Prefer a stable core with thin adapters, not duplicated workflows or a lowest-common-denominator API.

### 4. Classify evidence

- **Direct**: a legitimate current consumer uses the dimension. - **Committed**: a concrete consumer exists in the same change or linked near-term work. - **Architectural**: a minimal seam must precede consumers to protect a concrete invariant. - **Unsupported speculation**: only a possible future is named, without a concrete scenario, owner, or omission cost.

Direct consumption proves pressure, not placement or shape.

### 5. Test architectural demand

For a surface without a legitimate current consumer, require all five:

1. A concrete consumer class or extension scenario; 2. The owning layer and protected invariant; 3. A causal omission cost, such as boundary violations, duplicated mechanisms, incompatible implementations, security gaps, or migration lock-in; 4. Why the seam must exist before its first consumer; and 5. The smallest stable mechanism that protects the invariant.

Reject "future features", "flexibility", "centralization", "technical constraints", or "migration risk" without linked evidence and a causal failure. If the test fails, defer or remove. If it passes, preserve only the minimal paved road and remove guessed dimensions.

### 6. Check responsibility and overlap

Place behavior by ownership, not line count. Centralize security, permissions, transactions, invariants, and shared policy; leave presentation and caller-specific composition in consumers. Prefer try-the-operation when the owner can enforce atomically.

Compare contracts by semantics, owner, permissions, exposure, atomicity, lifecycle, failure model, and cost. Shared data alone does not prove duplication; reuse only when these are equivalent.

### 7. Decide, then review implementation

Choose one outcome per surface or normalized group:

- **Keep**: demand and shape are justified. - **Narrow**: remove unsupported dimensions. - **Split**: separate a valid core from unrelated concerns. - **Consolidate**: merge surfaces expressing one demand. - **Replace**: keep the demand, change consumer, owner, or abstraction. - **Defer**: do not commit a possible demand yet. - **Remove**: no demand remains or an equivalent contract owns it.

Report root outcome, evidence, consumer legitimacy, essential differences, owner, alternatives, and decision first. Review implementation quality only for survivors.

## Rationalization Guards

| Claim | Response | |---|---| | "The API is clean; the types are elegant." | Quality cannot justify existence. | | "It has consumers." | Verify legitimacy and exact consumption; workarounds endorse nothing. | | "It has no consumers." | Run the five-part architectural test; absence alone decides nothing. | | "The export is unused; add a test." | Tests verify behavior; they do not create demand. | | "The architecture will need it." | Name the invariant, causal omission cost, consumer class, why now, and minimal seam. | | "A technical constraint requires it." | Trace the constraint to root demand; constraints are not axioms. | | "Existence is the architect's call." | Authority neither exempts demand review nor reduces it to a nit. | | "The caller can compute it in one line." | Place policy by ownership and invariants, not code length. | | "The existing API returns the same data." | Compare full semantics before declaring duplication. | | "These consumers differ slightly." | Prove differences are semantic, not historical or caller-specific. | | "It is forward-compatible or additive." | Keep only concrete needs; additive contracts carry permanent cost. |

## Red Flags

Pause and restart from Step 1 when:

- Implementation comments accumulate before stating root demand, evidence, and legitimate consumers. - A call site is treated as proof that the contract belongs here or has the right shape. - Zero current consumption is treated as automatic rejection or permission to accept an architectural claim. - A compensating or hack-heavy consumer is used to freeze its workaround into the shared contract.

## Calibration

- Linked independent modules would otherwise import privileged internals: **keep the minimal registration seam; remove guessed knobs**. - A renderer parses raw errors and retries because no atomic operation exists: **replace the abstraction rather than expand the error taxonomy**.

Technical details

Version
1.0.0
License
AGPL-3.0
Last updated
Aug 22, 2026
Published
Aug 20, 2026

Decision snapshot

Primary pick

100
Ready
Adopt
Stage

50,908 GitHub stars

Audit

Install review

Install and adoption review

91
Safe to try
Security
85/100
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 demand-first-review, ready for a manual X post.

Curator note
demand-first-review: Use when reviewing a PR, API, IPC channel, endpoint, parameter, type, config, or architectura...

50.9K stars

https://www.openagentskill.com/skills/cherryhq-demand-first-review?ref=x
Open X draft
Optional reply with install command
Listing + install path for demand-first-review:
https://www.openagentskill.com/skills/cherryhq-demand-first-review?ref=x

Install: npx skills add CherryHQ/cherry-studio --skill demand-first-review

Listing source

Registry indexed

Claimable

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

Creator
CherryHQ
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 CherryHQ 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/cherryhq-demand-first-review?metric=listed&label=Listed)](https://www.openagentskill.com/skills/cherryhq-demand-first-review)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/cherryhq-demand-first-review?metric=trust&label=Trust)](https://www.openagentskill.com/skills/cherryhq-demand-first-review)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/cherryhq-demand-first-review?metric=audit&label=Audit)](https://www.openagentskill.com/skills/cherryhq-demand-first-review/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/cherryhq-demand-first-review?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/cherryhq-demand-first-review)

Author

C

CherryHQ

@cherryhq

Platform fit

Health signals

GitHub stars
50.9K
Quality score
57/100
Last GitHub push
Aug 22, 2026
Framework hints
Unknown
OpenAgentSkill views
6
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

Review then install

83
  • GitHub adoption51K GitHub starsPASS
  • Stars/forks activity51K stars, 4.8K forks; issue activity unavailable in current metadataPASS
  • Recent maintenancePushed todayPASS
  • License clarityAGPL-3.0PASS
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime risknetwork or browser surface, database surfaceINFO