data-acquisition-feasibility
Use when the user wants to know whether a dataset/source is worth pursuing, compare routes, score feasibility, identify trapdoors, classify Green/Yellow/Red…
Skill by Pranjay-kumar
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/pranjay-kumar-data-acquisition-feasibility
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: data
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
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- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/data-acquisition-feasibility
What's inside
- Skill name
data-acquisition-feasibility- Description
- Use when the user wants to know whether a dataset/source is worth pursuing, compare routes, score feasibility, identify trapdoors, classify Green/Yellow/Red, or decide whether to stop, sample, narrow, license, use owned-session access, or build a pipeline.
- License
- MIT
- Instructions
- 50 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Data Acquisition Feasibility Act as the feasibility analyst. Be direct about what works, what is partial, what requires authorization, and what should stop. Default to evidence-backed feasibility. If a public source URL or target site can be safely probed, run a bounded probe ladder before writing the final score. Do not produce a purely speculative feasibility report unless probing is impossible, disallowed by the user, or blocked by compliance boundaries. ## Shared Core Read from `../data-acquisition-core/references/`: - `source-access.md` - `feasibility-scoring.md` - `source-strategies.md` - `compliance-boundaries.md` - `output-contracts.md` - `workflow.md` ## Output Return: - `ModeSelection` - `SourceAccessClass` - `SourcePlan` - `ProbeResults` when probes were run - `FeasibilityScorecard` - `DataAcquisitionMemo` - `FeasibilityReport` - `ApprovalGate` Never approve full execution without explicit user approval. ## Required Pre-Report Probe Ladder For public web datasets, attempt these steps in order and record the result in `ProbeResults`: 1. Public boundary check: robots/sitemaps/public docs or obvious terms/access boundaries. 2. Cold HTTP check: one public seed URL plus obvious sitemap/feed/metadata URLs where applicable. 3. Static page metadata check: status, final URL, title, canonical, meta description, JSON-LD, embedded app state, visible listing/product hints. 4. Browser check: use Playwright or Patchright for a tiny rendered sample if the data is user-visible but cold probes fail or omit the rows. 5. Patchright non-headless check: when headless returns a block page but a normal visible browser context may load the page, run one visible Patchright probe with a persistent local profile. Mark as `owned_session` if local cookies/storage are required. 6. Page-only check: when the user says no API, disable endpoint discovery/replay and extract only DOM/JSON-LD/meta/visible row data. Bounds: 1 to 3 URLs, 20 rows maximum, 2 minutes per probe unless the user asks for more. No broad crawl before approval. Stop and score Red when the next step would require CAPTCHA solving, fingerprint spoofing/evasion, auth bypass, private third-party data, or rate-limit bypass. If a probe succeeds only in Patchright non-headless, classify the pipeline route as browser-context/page-load dependent. Lower stability and runtime scores, and call out that the route may not be publishable as a public unauthenticated pipeline.
From Pranjay-kumar/universal-data-acquisition-pipeline-skill, MIT.
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Signals
- Stars
- 2
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Jun 10, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from Pranjay-kumar
- 0.512score
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SkillCFreshness: C★ 2Claude Code +9 - 0.512score
data-acquisition-core
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Shared core for the data acquisition skill tree.
SkillCFreshness: C★ 2Claude Code +9 - 0.508score
universal-data-acquisition-pipeline
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Trigger when the user wants to collect, structure, evaluate, crawl, extract, refresh, or build reusable data acquisition pipelines from websites, APIs…
SkillCFreshness: C★ 2Claude Code +9 - 0.507score
data-acquisition-design
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SkillCFreshness: C★ 2Claude Code +9 - 0.507score
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Cite this page
data-acquisition-feasibility skill. Agent Mart. https://agentmart.dev/skills/pranjay-kumar-data-acquisition-feasibility (last updated 2026-10-24)