Skill: aipoch-ai/open-access-scout (version 1.0.0)

--- name: open-access-scout description: Use when finding open access journals, checking journal policies, or identifying predatory publishers. Helps researchers locate legitimate open access venues and avoid publication scams. license: MIT skill-author: AIPOCH --- # Open Access Journal Scout Find legitimate open access journals, verify publisher credibility, and avoid predatory publication traps. ## When to Use - Use this skill when the task needs Use when finding open access journals, checking journal policies, or identifying predatory publishers. Helps researchers locate legitimate open access venues and avoid publication scams. - Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format. - Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence. ## Key Features - Scope-focused workflow aligned to: Use when finding open access journals, checking journal policies, or identifying predatory publishers. Helps researchers locate legitimate open access venues and avoid publication scams. - Packaged executable path(s): `scripts/main.py`. - Reference material available in `references/` for task-specific guidance. - Structured execution path designed to keep outputs consistent and reviewable. ## Dependencies - `Python`: `3.10+`. Repository baseline for current packaged skills. - `Third-party packages`: `not explicitly version-pinned in this skill package`. Add pinned versions if this skill needs stricter environment control. ## Example Usage ```bash cd "20260318/scientific-skills/Evidence Insight/open-access-scout" python -m py_compile scripts/main.py python scripts/main.py --help ``` Example run plan: 1. Confirm the user input, output path, and any required config values. 2. Edit the in-file `CONFIG` block or documented parameters if the script uses fixed settings. 3. Run `python scripts/main.py` with the validated inputs. 4. Review the generated output and return the final artifact with any assumptions called out. ## Implementation Details See `## Workflow` above for related details. - Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable. - Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script. - Primary implementation surface: `scripts/main.py`. - Reference guidance: `references/` contains supporting rules, prompts, or checklists. - Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints. - Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects. ## Quick Check Use this command to verify that the packaged script entry point can be parsed before deeper execution. ```bash python -m py_compile scripts/main.py ``` ## Audit-Ready Commands Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths. ```bash python -m py_compile scripts/main.py python scripts/main.py --help ``` ## Workflow 1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions. 3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available. 4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items. 5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion. ## Quick Start ```python from scripts.oa_scout import OpenAccessScout scout = OpenAccessScout() # Find journals journals = scout.find_journals( subject="oncology", impact_range=(2, 5), max_apc=2000 ) ``` ## Core Capabilities ### 1. Journal Search ```python results = scout.search( keywords=["immunotherapy", "cancer"], filters={ "indexed_in": ["PubMed", "Scopus"], "peer_review": "double_blind", "apc_max": 2500 } ) ``` ### 2. Predatory Check ```python assessment = scout.assess_journal("Journal of Medical Advances") print(f"Trust score: {assessment.score}/100") print(f"Red flags: {assessment.red_flags}") ``` **Warning Signs:** - No clear editorial board - Rapid review promises (<2 weeks) - Excessive APCs (>$3000) - Not indexed in major databases - Spam email invitations ### 3. APC Comparison ```python comparison = scout.compare_apc( journals=["Journal A", "Journal B"], currency="USD" ) ``` ## CLI Usage ```text python scripts/oa_scout.py --search "oncology immunotherapy" --max-apc 2000 ``` --- **Skill ID**: 210 | **Version**: 1.0 | **License**: MIT ## Output Requirements Every final response should make these items explicit when they are relevant: - Objective or requested deliverable - Inputs used and assumptions introduced - Workflow or decision path - Core result, recommendation, or artifact - Constraints, risks, caveats, or validation needs - Unresolved items and next-step checks ## Error Handling - If required inputs are missing, state exactly which fields are missing and request only the minimum additional information. - If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment. - If `scripts/main.py` fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback. - Do not fabricate files, citations, data, search results, or execution outcomes. ## Input Validation This skill accepts requests that match the documented purpose of `open-access-scout` and include enough context to complete the workflow safely. Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond: > `open-access-scout` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill. ## References - [references/audit-reference.md](references/audit-reference.md) - Supported scope, audit commands, and fallback boundaries ## Response Template Use the following fixed structure for non-trivial requests: 1. Objective 2. Inputs Received 3. Assumptions 4. Workflow 5. Deliverable 6. Risks and Limits 7. Next Checks If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)