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research-intelligence

Use when a Hermes agent must perform public-source research, source scouting, evidence grading, competitor/tool comparison, community-signal analysis, or decision-ready brief writing without private data or credentials.

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Overview

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Research Intelligence

Overview

This skill turns a Hermes agent into a careful public-source researcher. It is optimized for comparing tools, vendors, repositories, products, papers, market signals, community pain, and public claims.

The operating rule is simple: collect enough evidence for the decision, not enough links to look busy. Facts, weak signals, hypotheses, and interpretation must be kept separate when the answer affects money, risk, implementation, reputation, or public claims.

When to Use

Use for:

tool, repository, model, vendor, or product comparison;
public GitHub/project traction checks;
official docs/changelog/release research;
community pain and adoption scouting;
public OSINT-style due diligence on companies, products, or public claims;
research briefs for implementation, purchase, positioning, or watch/reject decisions;
recurring watchlist design, but only when the user explicitly asks for monitoring.

Do not use for:

private-account scraping;
bypassing login walls, paywalls, CAPTCHAs, or access controls;
collecting secrets, credentials, private exports, or personal data;
legal/medical/financial conclusions without qualified review;
public posting, registration, payment, joining, following, liking, DMing, or emailing without explicit approval.

Core Research Loop

Frame the decision. Name the user decision: adopt, buy, compare, reject, watch, implement, contact, investigate, or hand off.
Choose the mode. Use references/research-modes.md to select quick_fact, deep_research, repo_tool, community_pain, live_visual, or monitoring_design.
Build the source ladder. Pick source classes before searching.
Ingest documents when needed. For public PDFs, DOCX, PPTX, spreadsheets, HTML, EPUB, or inspected trusted document bundles, create a Markdown analysis copy with markitdown-document-ingestion before summarizing.
Collect dated facts. Include timestamps for volatile data like stars, downloads, prices, package versions, and community metrics.
Group source lineages. Copies, syndications, mirrors, and posts repeating one announcement count as one evidence lineage until independent reporting or data is shown.
Triangulate important claims. Decision-relevant claims need primary or structured evidence plus another independent lineage when safely available.
Seek a counterexample. Check failure terms, negative cases, limitations, and evidence that could change the decision.
Classify signal strength. Use fact / claim / weak signal / hypothesis / interpretation.
Browser-check the shortlist. Use a real browser, DOM extraction, or vision for dynamic/social/visual pages when live state matters.
Run the evidence gate. Fix gaps or label limitations before final answer.
Return the next move. A good brief ends with a practical action.

Mode Router

Choose the smallest sufficient research mode before collecting sources. A one-fact check must not grow into a deep report, while a consequential or disputed decision must not be answered from snippets. The full mode contract and stop rules live in references/research-modes.md.

Source Lineage and Counterexamples

Source count is not evidence count. Treat these as one lineage unless they add independently collected facts:

articles copying the same press release;
mirrors and syndicated posts;
social posts linking the same announcement;
multiple pages citing one benchmark without new measurements.

For every decision-relevant claim:

identify the primary or structured source;
identify another independent lineage when safely available;
look for a concrete counterexample or record where you searched and found none;
lower confidence when access gaps prevent that check.

Use tools/evidence_lineage_check.py with a research-run/v1 JSON artifact when a deep result must be repeatable or independently reviewed.

python3 tools/evidence_lineage_check.py examples/research-run-source-lineage.json --json

Source Ladder

Use the highest-value source classes first:

Primary sources: official docs, repositories, changelogs, release notes, pricing pages, standards, papers, product pages, government/regulator pages.
Structured public data and documents: GitHub API, npm/PyPI metadata, Docker tags, package registries, RSS/Atom, public JSON endpoints, datasets, PDFs, DOCX/PPTX/XLSX, CSVs, and inspected trusted document bundles converted to Markdown when useful.
Community ground truth: Hacker News, Reddit, GitHub issues/discussions, forums, Stack Exchange, public Discord/Telegram mirrors only when accessible without login.
Search pivots: exact phrases, domain searches, local-language terms, author names, repo names, company IDs, package names, error strings, quoted claims.
Browser verification: real page state, dates, author identity, comments, visible metrics, UI, login wall, blocked state, screenshots, current context.

Safe Source-Reach Stack

Default source reach is zero-secret and read-only. Use public layers first and label degraded access instead of reaching for cookies.

Allowed by default:

Hermes web search/extract for ordinary pages, docs, feeds, and public APIs.
Browser verification for live page state, visible metrics, comments, UI, visual context, and blocked/login-wall evidence.
Jina Reader URL mode for readable public pages.
yt-dlp metadata and public subtitle/transcript checks without cookies.
GitHub public web/API checks; authenticated gh is optional, not required.
Reddit public page/search/JSON when available, then old Reddit/Jina/search fallback.
RSS, Atom, public JSON, package registries, public documents, and official unauthenticated endpoints.

Approval-gated:

GitHub login/token setup.
X/Twitter, Reddit OAuth/login/cookies, XHS, Bili, LinkedIn, WeChat, Weibo, Douyin, or similar account/session tools.
Exa/API-key tools, paid APIs, trials, payments, MCP registration, or local agent config mutation.
Browser cookie/profile extraction.
Join/follow/like/comment/post/reply/DM or any other account action.

YouTube research

Load youtube-research-pack when the task depends on YouTube search, channels, playlists, video metadata, subtitles, comments, visual framing, or a persistent YouTube radar. Use tools/youtube_research.py for bounded quick work. The optional public youtube-intelligence-stack companion is the deep collector; it is not installed or authenticated automatically.

Keep metadata, transcripts, comments, visuals, and external corroboration as separate evidence layers. Missing subtitles or comments lower coverage but do not invalidate independently verified metadata. Public comments may contain personal identifiers: collect only when necessary and summarize themes without exporting author IDs or profile URLs.

For brittle public/social surfaces, include this block in the final brief when relevant:

Source reach:
- source classes used: web/browser/API/Jina/yt-dlp/RSS/etc.
- access state: public / degraded / blocked / login_required
- coverage gaps: transcripts/comments/search/API/rate-limit
- approval needed: yes/no + exact reason
- confidence impact: what evidence remains weak because of access limits

Before serious research, run the optional local doctor from the repository root:

python3 tools/source_reach_doctor.py

Document ingestion

For public research files, use the companion markitdown-document-ingestion skill when available. Convert documents into Markdown analysis copies before extracting claims or writing a brief.

Good targets:

public PDFs, reports, papers, policy documents and manuals;
DOCX / PPTX / XLSX files supplied as public evidence;
HTML, CSV, JSON, XML, EPUB, and trusted small ZIP bundles only after size/file-count/path inspection.

After conversion, record the ingestion state:

Document ingestion:
- original: <file/source>
- converted copy: <path if saved>
- status: complete / partial / OCR-needed / degraded
- caveat: <tables/pages/images/comments that may be missing>

The original document remains source-of-truth. The Markdown copy is only for analysis. Scanned PDFs may need OCR; archives need provenance and path/size inspection; label those gaps instead of pretending full extraction succeeded.

Search Tactics

Search by use case and symptom, not only product names.
Use exact phrases from claims, README snippets, error messages, and pricing language.
Search negative terms: not working, refund, scam, issue, complaint, lawsuit, broken, expensive, blocked.
Search local-language variants where relevant.
For GitHub projects, check README, license, releases, issues, recent commits, package registries, and forks before judging traction.
For popularity claims, caveat stars/downloads as proxies, not proof of usage.

Evidence Gate

Before finalizing, answer:

What decision does this research support?
Which source classes were checked?
Which sources are primary or high-signal?
How many independent source lineages support the decision?
Did copied announcements or mirrors get collapsed into one lineage?
What is the strongest counterexample, or where was it sought?
What is fact vs interpretation?
Is the data fresh enough?
What is the main caveat?
What would change the answer?
What is the next move?

Verdicts:

PASS — enough evidence for the decision.
PASS_AFTER_FIX — usable after small fixes already made.
BLOCKED — required evidence unavailable or coverage too weak.
N/A — not evidence-sensitive.

Output Templates

For quick answers:

Verdict:
- <one-line decision>

Evidence:
- <source/date/fact>
- <source/date/fact>

Source lineages:
- <which pages belong to the same underlying source>

Counterexample:
- <strongest contrary case, or where it was sought>

Interpretation:
- <what it means>

Caveat:
- <main limitation>

Next move:
- <practical action>

For deeper work, use templates/research-brief.md and templates/source-ledger.md.

Public OSINT Boundaries

Allowed by default:

public web pages;
official APIs and feeds;
public repositories and package metadata;
public forums and comments visible without login;
public PDFs, documents, datasets, registries, and inspected trusted document bundles within the archive rules above;
browser verification of public pages.

Requires explicit user approval:

login, signup, email/phone verification, 2FA, joining groups, following accounts;
paid access, trials, purchases, subscriptions;
using private cookies, private exports, private channel logs, or account sessions;
posting, commenting, liking, DMing, contacting people;
collecting sensitive personal data.

Forbidden:

bypassing access controls;
credential harvesting;
stealth scraping behind authentication;
doxxing or invasive personal profiling;
presenting unverified allegations as facts.

Common Pitfalls

Link dumps. A list of links is not research. Synthesize into a decision.
Community post as truth. Treat forums as experience signals until corroborated.
Stars as adoption proof. Stars are attention, not usage.
No source freshness. Volatile numbers need a collection date.
Ignoring blocked coverage. Say Reddit blocked, GitHub API rate-limited, or login wall instead of hiding it.
**

View source ↗

File metadata

name: research-intelligence
description: Use when a Hermes agent must perform public-source research, source scouting, evidence grading, competitor/tool comparison, community-signal analysis, or decision-ready brief writing without private data or credentials.
version: 1.4.0
author: Aleksei Ulianov / Sprut_AI
license: MIT
metadata:
  hermes:
    tags: [research, public-source, evidence, osint-public, source-scouting, decision-briefs]
    related_skills: [youtube-research-pack]

View original text

---
name: research-intelligence
description: Use when a Hermes agent must perform public-source research, source scouting, evidence grading, competitor/tool comparison, community-signal analysis, or decision-ready brief writing without private data or credentials.
version: 1.4.0
author: Aleksei Ulianov / Sprut_AI
license: MIT
metadata:
  hermes:
    tags: [research, public-source, evidence, osint-public, source-scouting, decision-briefs]
    related_skills: [youtube-research-pack]
---

# Research Intelligence

## Overview

This skill turns a Hermes agent into a careful public-source researcher. It is optimized for comparing tools, vendors, repositories, products, papers, market signals, community pain, and public claims.

The operating rule is simple: collect enough evidence for the decision, not enough links to look busy. Facts, weak signals, hypotheses, and interpretation must be kept separate when the answer affects money, risk, implementation, reputation, or public claims.

## When to Use

Use for:

- tool, repository, model, vendor, or product comparison;
- public GitHub/project traction checks;
- official docs/changelog/release research;
- community pain and adoption scouting;
- public OSINT-style due diligence on companies, products, or public claims;
- research briefs for implementation, purchase, positioning, or watch/reject decisions;
- recurring watchlist design, but only when the user explicitly asks for monitoring.

Do not use for:

- private-account scraping;
- bypassing login walls, paywalls, CAPTCHAs, or access controls;
- collecting secrets, credentials, private exports, or personal data;
- legal/medical/financial conclusions without qualified review;
- public posting, registration, payment, joining, following, liking, DMing, or emailing without explicit approval.

## Core Research Loop

1. **Frame the decision.** Name the user decision: adopt, buy, compare, reject, watch, implement, contact, investigate, or hand off.
2. **Choose the mode.** Use `references/research-modes.md` to select `quick_fact`, `deep_research`, `repo_tool`, `community_pain`, `live_visual`, or `monitoring_design`.
3. **Build the source ladder.** Pick source classes before searching.
4. **Ingest documents when needed.** For public PDFs, DOCX, PPTX, spreadsheets, HTML, EPUB, or inspected trusted document bundles, create a Markdown analysis copy with `markitdown-document-ingestion` before summarizing.
5. **Collect dated facts.** Include timestamps for volatile data like stars, downloads, prices, package versions, and community metrics.
6. **Group source lineages.** Copies, syndications, mirrors, and posts repeating one announcement count as one evidence lineage until independent reporting or data is shown.
7. **Triangulate important claims.** Decision-relevant claims need primary or structured evidence plus another independent lineage when safely available.
8. **Seek a counterexample.** Check failure terms, negative cases, limitations, and evidence that could change the decision.
9. **Classify signal strength.** Use fact / claim / weak signal / hypothesis / interpretation.
10. **Browser-check the shortlist.** Use a real browser, DOM extraction, or vision for dynamic/social/visual pages when live state matters.
11. **Run the evidence gate.** Fix gaps or label limitations before final answer.
12. **Return the next move.** A good brief ends with a practical action.

## Mode Router

Choose the smallest sufficient research mode before collecting sources. A one-fact check must not grow into a deep report, while a consequential or disputed decision must not be answered from snippets. The full mode contract and stop rules live in `references/research-modes.md`.

## Source Lineage and Counterexamples

Source count is not evidence count. Treat these as one lineage unless they add independently collected facts:

- articles copying the same press release;
- mirrors and syndicated posts;
- social posts linking the same announcement;
- multiple pages citing one benchmark without new measurements.

For every decision-relevant claim:

1. identify the primary or structured source;
2. identify another independent lineage when safely available;
3. look for a concrete counterexample or record where you searched and found none;
4. lower confidence when access gaps prevent that check.

Use `tools/evidence_lineage_check.py` with a `research-run/v1` JSON artifact when a deep result must be repeatable or independently reviewed.

```bash
python3 tools/evidence_lineage_check.py examples/research-run-source-lineage.json --json
```

## Source Ladder

Use the highest-value source classes first:

1. **Primary sources:** official docs, repositories, changelogs, release notes, pricing pages, standards, papers, product pages, government/regulator pages.
2. **Structured public data and documents:** GitHub API, npm/PyPI metadata, Docker tags, package registries, RSS/Atom, public JSON endpoints, datasets, PDFs, DOCX/PPTX/XLSX, CSVs, and inspected trusted document bundles converted to Markdown when useful.
3. **Community ground truth:** Hacker News, Reddit, GitHub issues/discussions, forums, Stack Exchange, public Discord/Telegram mirrors only when accessible without login.
4. **Search pivots:** exact phrases, domain searches, local-language terms, author names, repo names, company IDs, package names, error strings, quoted claims.
5. **Browser verification:** real page state, dates, author identity, comments, visible metrics, UI, login wall, blocked state, screenshots, current context.

## Safe Source-Reach Stack

Default source reach is zero-secret and read-only. Use public layers first and label degraded access instead of reaching for cookies.

Allowed by default:

- Hermes web search/extract for ordinary pages, docs, feeds, and public APIs.
- Browser verification for live page state, visible metrics, comments, UI, visual context, and blocked/login-wall evidence.
- Jina Reader URL mode for readable public pages.
- `yt-dlp` metadata and public subtitle/transcript checks without cookies.
- GitHub public web/API checks; authenticated `gh` is optional, not required.
- Reddit public page/search/JSON when available, then old Reddit/Jina/search fallback.
- RSS, Atom, public JSON, package registries, public documents, and official unauthenticated endpoints.

Approval-gated:

- GitHub login/token setup.
- X/Twitter, Reddit OAuth/login/cookies, XHS, Bili, LinkedIn, WeChat, Weibo, Douyin, or similar account/session tools.
- Exa/API-key tools, paid APIs, trials, payments, MCP registration, or local agent config mutation.
- Browser cookie/profile extraction.
- Join/follow/like/comment/post/reply/DM or any other account action.

## YouTube research

Load `youtube-research-pack` when the task depends on YouTube search, channels,
playlists, video metadata, subtitles, comments, visual framing, or a persistent
YouTube radar. Use `tools/youtube_research.py` for bounded quick work. The
optional public `youtube-intelligence-stack` companion is the deep collector;
it is not installed or authenticated automatically.

Keep metadata, transcripts, comments, visuals, and external corroboration as
separate evidence layers. Missing subtitles or comments lower coverage but do
not invalidate independently verified metadata. Public comments may contain
personal identifiers: collect only when necessary and summarize themes without
exporting author IDs or profile URLs.

For brittle public/social surfaces, include this block in the final brief when relevant:

```text
Source reach:
- source classes used: web/browser/API/Jina/yt-dlp/RSS/etc.
- access state: public / degraded / blocked / login_required
- coverage gaps: transcripts/comments/search/API/rate-limit
- approval needed: yes/no + exact reason
- confidence impact: what evidence remains weak because of access limits
```

Before serious research, run the optional local doctor from the repository root:

```bash
python3 tools/source_reach_doctor.py
```

## Document ingestion

For public research files, use the companion `markitdown-document-ingestion` skill when available. Convert documents into Markdown analysis copies before extracting claims or writing a brief.

Good targets:

- public PDFs, reports, papers, policy documents and manuals;
- DOCX / PPTX / XLSX files supplied as public evidence;
- HTML, CSV, JSON, XML, EPUB, and trusted small ZIP bundles only after size/file-count/path inspection.

After conversion, record the ingestion state:

```text
Document ingestion:
- original: <file/source>
- converted copy: <path if saved>
- status: complete / partial / OCR-needed / degraded
- caveat: <tables/pages/images/comments that may be missing>
```

The original document remains source-of-truth. The Markdown copy is only for analysis. Scanned PDFs may need OCR; archives need provenance and path/size inspection; label those gaps instead of pretending full extraction succeeded.

## Search Tactics

- Search by use case and symptom, not only product names.
- Use exact phrases from claims, README snippets, error messages, and pricing language.
- Search negative terms: `not working`, `refund`, `scam`, `issue`, `complaint`, `lawsuit`, `broken`, `expensive`, `blocked`.
- Search local-language variants where relevant.
- For GitHub projects, check README, license, releases, issues, recent commits, package registries, and forks before judging traction.
- For popularity claims, caveat stars/downloads as proxies, not proof of usage.

## Evidence Gate

Before finalizing, answer:

- What decision does this research support?
- Which source classes were checked?
- Which sources are primary or high-signal?
- How many independent source lineages support the decision?
- Did copied announcements or mirrors get collapsed into one lineage?
- What is the strongest counterexample, or where was it sought?
- What is fact vs interpretation?
- Is the data fresh enough?
- What is the main caveat?
- What would change the answer?
- What is the next move?

Verdicts:

- `PASS` — enough evidence for the decision.
- `PASS_AFTER_FIX` — usable after small fixes already made.
- `BLOCKED` — required evidence unavailable or coverage too weak.
- `N/A` — not evidence-sensitive.

## Output Templates

For quick answers:

```text
Verdict:
- <one-line decision>

Evidence:
- <source/date/fact>
- <source/date/fact>

Source lineages:
- <which pages belong to the same underlying source>

Counterexample:
- <strongest contrary case, or where it was sought>

Interpretation:
- <what it means>

Caveat:
- <main limitation>

Next move:
- <practical action>
```

For deeper work, use `templates/research-brief.md` and `templates/source-ledger.md`.

## Public OSINT Boundaries

Allowed by default:

- public web pages;
- official APIs and feeds;
- public repositories and package metadata;
- public forums and comments visible without login;
- public PDFs, documents, datasets, registries, and inspected trusted document bundles within the archive rules above;
- browser verification of public pages.

Requires explicit user approval:

- login, signup, email/phone verification, 2FA, joining groups, following accounts;
- paid access, trials, purchases, subscriptions;
- using private cookies, private exports, private channel logs, or account sessions;
- posting, commenting, liking, DMing, contacting people;
- collecting sensitive personal data.

Forbidden:

- bypassing access controls;
- credential harvesting;
- stealth scraping behind authentication;
- doxxing or invasive personal profiling;
- presenting unverified allegations as facts.

## Common Pitfalls

1. **Link dumps.** A list of links is not research. Synthesize into a decision.
2. **Community post as truth.** Treat forums as experience signals until corroborated.
3. **Stars as adoption proof.** Stars are attention, not usage.
4. **No source freshness.** Volatile numbers need a collection date.
5. **Ignoring blocked coverage.** Say `Reddit blocked`, `GitHub API rate-limited`, or `login wall` instead of hiding it.
6. **

Review the source

Price & running costs

Get the skill: Price unconfirmed
Run it: Requirements have not been confirmed. Check the source for agent, API and service charges.
License: MIT
Price unconfirmed: We have not confirmed a price for this skill. Existing source and install links remain available.

Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →

Source needs review

The tracked source changed or could not be synchronized. Review the current source before installing.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
The skill references the 'markitdown-document-ingestion' skill as a dependency but it is not included in the submitted repository; agents may need it installed separately.
The evidence_lineage_check.py tool is mentioned but its source was not fully reviewed in this automated pass.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 51 GitHub stars
Stars/forks activity: 51 stars, 6 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: AlekseiUL/hermes-researcher-agent
License: MIT
Version: 1.4.0
Last GitHub push: Sep 4, 2026
Registry updated: Sep 9, 2026
Instruction path: skills/research-intelligence/SKILL.md @ d6fe32e0d53f

Version reported in registry metadata; check source releases before relying on it.

Quality

61/100

Promising

Trust

53/100

Do not auto-install

Audit

69/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
The skill references the 'markitdown-document-ingestion' skill as a dependency but it is not included in the submitted repository; agents may need it installed separately.
The evidence_lineage_check.py tool is mentioned but its source was not fully reviewed in this automated pass.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 51 GitHub stars
Stars/forks activity: 51 stars, 6 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Outcomes: —

Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.

Agent access

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 install API LLM text format Open manifest Auto-resolve Plan

More details

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      }
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    "handoff_url": "https://www.openagentskill.com/api/skills/alekseiul-research-intelligence/install",
    "manifest_url": "https://www.openagentskill.com/api/registry/manifest/alekseiul-research-intelligence"
  },
  "trust": {
    "score": 61,
    "label": "Manual review",
    "version": "trust-score-v4",
    "install_policy": "block",
    "evidence": {
      "stars": "51 GitHub stars",
      "repoActivity": "51 stars, 6 forks",
      "lastPushed": "1mo since push",
      "license": "MIT",
      "repository": "https://github.com/AlekseiUL/hermes-researcher-agent/tree/main/skills/research-intelligence",
      "install": "The tracked source changed or could not be synchronized. Review the current source before installing.",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
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      "recent_success_rate": null,
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      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
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      "agent-skill"
    ],
    "known_risks": [
      "The skill references the 'markitdown-document-ingestion' skill as a dependency but it is not included in the submitted repository; agents may need it installed separately.",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 51 GitHub stars",
      "Stars/forks activity: 51 stars, 6 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
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  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
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      "successfulOutcomes": 0,
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      "installAttempts": 0,
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      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 69,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "The skill references the 'markitdown-document-ingestion' skill as a dependency but it is not included in the submitted repository; agents may need it installed separately.",
      "The evidence_lineage_check.py tool is mentioned but its source was not fully reviewed in this automated pass.",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 61,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "mvanhorn-last30days-skill",
      "name": "Last30days Skill",
      "url": "https://www.openagentskill.com/skills/mvanhorn-last30days-skill",
      "stars": 63666,
      "install_command": "",
      "trust_score": 94,
      "audit_score": 95
    },
    {
      "slug": "yanliudesign-mono-color-skill",
      "name": "mono-color",
      "url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
      "stars": 1919,
      "install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
      "trust_score": 83,
      "audit_score": 90
    }
  ],
  "do_not_use_when": [
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    "production agents without a repository review",
    "The skill references the 'markitdown-document-ingestion' skill as a dependency but it is not included in the submitted repository; agents may need it installed separately.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "The evidence_lineage_check.py tool is mentioned but its source was not fully reviewed in this automated pass."
  ],
  "agent_contract": {
    "task_input": "Use research-intelligence in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 61/100 Manual review",
      "Audit: 69/100 Needs review",
      "Safety: 25/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "alekseiul-research-intelligence (research-intelligence)",
      "install_command": "",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
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    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "alekseiul-research-intelligence",
      "task": "Use research-intelligence in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
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      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
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  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/alekseiul-research-intelligence",
    "api": "https://www.openagentskill.com/api/agent/skills/alekseiul-research-intelligence",
    "audit": "https://www.openagentskill.com/skills/alekseiul-research-intelligence/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=alekseiul-research-intelligence&task=Use%20research-intelligence%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20research-intelligence%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20research-intelligence%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/alekseiul-research-intelligence/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/alekseiul-research-intelligence"
  }
}

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Creator: Aleksei Ulianov / Sprut_AI
Source: AlekseiUL/hermes-researcher-agent
Indexed by: OpenAgentSkill community index

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Overview

Research Intelligence

Overview

When to Use

Core Research Loop

Mode Router

Source Lineage and Counterexamples

Source Ladder

Safe Source-Reach Stack

YouTube research

Document ingestion

Search Tactics

Evidence Gate

Output Templates

Public OSINT Boundaries

Common Pitfalls

Review the source

Price & running costs

Start with one small task

Source & usage notes

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