Markit
shift-labs-ai/markit
Convert files and URLs to Markdown. An agent skill from shift-labs-ai/markit.
Turns a vague, high-level stakeholder ask into a structured set of intelligence requirements for a CTI team, complete with Essential Elements of Information, collection guidance, success criteria…
$ npx skills add TracecatHQ/tracecat --skill intelligence-requirements-builder -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install TracecatHQ/tracecat intelligence-requirements-builder --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/TracecatHQ/tracecat.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tracecat/agent/skill/library/skills/intelligence-requirements-builder .claude/skills/intelligence-requirements-builder && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "intelligence-requirements-builder" agent skill from https://github.com/TracecatHQ/tracecat/tree/main/tracecat/agent/skill/library/skills/intelligence-requirements-builder into .claude/skills/intelligence-requirements-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "intelligence-requirements-builder", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/TracecatHQ/tracecat/tree/main/tracecat/agent/skill/library/skills/intelligence-requirements-builderType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add TracecatHQ/tracecat --skill intelligence-requirements-builder -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install TracecatHQ/tracecat intelligence-requirements-builder --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TracecatHQ/tracecat.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tracecat/agent/skill/library/skills/intelligence-requirements-builder .agents/skills/intelligence-requirements-builder && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "intelligence-requirements-builder" agent skill from https://github.com/TracecatHQ/tracecat/tree/main/tracecat/agent/skill/library/skills/intelligence-requirements-builder into .agents/skills/intelligence-requirements-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "intelligence-requirements-builder", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add TracecatHQ/tracecat --skill intelligence-requirements-builder -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install TracecatHQ/tracecat intelligence-requirements-builder --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TracecatHQ/tracecat.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tracecat/agent/skill/library/skills/intelligence-requirements-builder .cursor/skills/intelligence-requirements-builder && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "intelligence-requirements-builder" agent skill from https://github.com/TracecatHQ/tracecat/tree/main/tracecat/agent/skill/library/skills/intelligence-requirements-builder into .cursor/skills/intelligence-requirements-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "intelligence-requirements-builder", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/TracecatHQ/tracecat.git --path tracecat/agent/skill/library/skills/intelligence-requirements-builder--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add TracecatHQ/tracecat --skill intelligence-requirements-builder -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install TracecatHQ/tracecat intelligence-requirements-builder --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TracecatHQ/tracecat.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tracecat/agent/skill/library/skills/intelligence-requirements-builder .gemini/skills/intelligence-requirements-builder && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "intelligence-requirements-builder" agent skill from https://github.com/TracecatHQ/tracecat/tree/main/tracecat/agent/skill/library/skills/intelligence-requirements-builder into .gemini/skills/intelligence-requirements-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "intelligence-requirements-builder", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install TracecatHQ/tracecat intelligence-requirements-builderInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add TracecatHQ/tracecat --skill intelligence-requirements-builder -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/TracecatHQ/tracecat.git skills-src && mkdir -p .github/skills && cp -r skills-src/tracecat/agent/skill/library/skills/intelligence-requirements-builder .github/skills/intelligence-requirements-builder && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "intelligence-requirements-builder" agent skill from https://github.com/TracecatHQ/tracecat/tree/main/tracecat/agent/skill/library/skills/intelligence-requirements-builder into .github/skills/intelligence-requirements-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "intelligence-requirements-builder", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add TracecatHQ/tracecat --skill intelligence-requirements-builder -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install TracecatHQ/tracecat intelligence-requirements-builder --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TracecatHQ/tracecat.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tracecat/agent/skill/library/skills/intelligence-requirements-builder .opencode/skills/intelligence-requirements-builder && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "intelligence-requirements-builder" agent skill from https://github.com/TracecatHQ/tracecat/tree/main/tracecat/agent/skill/library/skills/intelligence-requirements-builder into .opencode/skills/intelligence-requirements-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "intelligence-requirements-builder", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
intelligence-requirements-builderTurns a vague, high-level stakeholder ask into a structured set of intelligence requirements for a CTI team, complete with Essential Elements of Information, collection guidance, success criteria…
Intelligence Requirements Builder is an agent skill from TracecatHQ/tracecat. Turns a vague, high-level stakeholder ask into a structured set of intelligence requirements for a CTI team, complete with Essential Elements of Information, collection guidance, success criteria, deliverables, and a criticality rating, producing the intelligence requirements set as markdown, as a Word document, and as a CSV intelligence requirements list. Use this skill whenever the user mentions intelligence requirements, IRs, PIRs, GIRs, SIRs, stakeholder requests, RFIs, requirements gathering, or says things…
Its SKILL.md is about 6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files and assets (for example `assets/requirements-template.md`, `references/feedly-integration.md` and `references/ir-definitions.md`).
It sits in Documents & Office, covering Requirements gathering, Word documents and CSV and tabular files. It works with Microsoft Word. The repository describes itself as: Open-source security automation platform for teams and AI agents. The licence is MIT.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 383b245. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Intelligence Requirements Builder loads about 6k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 205 tokens; SKILL.md has 3,433 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from TracecatHQ/tracecat at commit 383b245, republished under its MIT licence (© TracecatHQ). 3,433 words, ~5,990 tokens.
.claude/skills/intelligence-requirements-builder/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.This skill helps a CTI analyst convert an informal stakeholder request into formally codified intelligence requirements. The output should be precise enough that a CTI team could begin collection against it immediately, structured enough to slot into an existing intelligence requirements list, and concise enough that a busy analyst can scan it in under a minute. Favor brevity throughout: short phrases over full sentences in structured fields, and no filler.
Before producing requirements, read references/ir-definitions.md to ensure correct terminology, and read references/requirement-quality.md to ensure every requirement is written to the quality standard. Intelligence requirements have specific meanings inherited from military and government intelligence practice, and using the wrong term (for example, conflating prioritized intelligence requirements with Priority Intelligence Requirements) undermines the credibility of the output. Equally, a requirement that asks two questions at once, or asks a question that collection cannot answer, undermines the credibility of the whole set.
Follow these six stages in order. Do not skip the clarification stage unless the user's request already contains the answers.
Parse the stakeholder ask and extract what is already known:
State your initial read back to the user in two or three sentences before asking anything. This shows the analyst what you have inferred and lets them correct you early.
Ask only the questions you cannot answer from the request itself, and ask no more than five. Prioritize in this order:
If the user cannot answer a question, proceed with a documented assumption rather than blocking. Mark every assumption clearly in the final output so it can be validated with the stakeholder later.
Determine whether the need is enduring or priority, and say which explicitly:
Apply this test: if the requirement still matters in 90 days in roughly its current form, it is likely enduring. If it expires once a specific decision is made or event passes, it is likely a PIR.
Do not label an enduring requirement as a PIR simply because the stakeholder is senior or the topic feels urgent. A team that ranks its enduring requirements has prioritized intelligence requirements; that is a different concept from a PIR, and the distinction matters. See references/ir-definitions.md for the full explanation.
Decompose the ask into candidate requirements, then keep the final set small. If the stakeholder asked a single question, fully enrich only the 2 to 4 requirements most load-bearing for the decision identified in Stage 1, and never more than 6. If the stakeholder asked more than one distinct question, keep all the resulting requirements in one combined set and fully enrich 1 to 3 requirements per question; this budget applies per question, so three questions may yield up to nine enriched requirements in the single set. The questions stay distinct inside that one set: each requirement records which stakeholder question and decision it traces to (via its linked-decision field and, in the CSV, its Stakeholder and Decision_Supported columns), so context is never lost even though everything lives in one place. List any remaining valid questions in a single "Candidate requirements" section at the end of the set so the team can promote them later if capacity allows. Candidates get the question and a one-line rationale only, with no EEIs, collection guidance, or criticality scores, and they never enter the CSV ledger. Before writing any requirement, read references/requirement-quality.md for the full standard and worked examples of bad questions rewritten into good ones.
Every requirement must pass all five tests below. If it fails any one, rewrite or split it before moving on. According to CTI best practices, a strong requirement is singular, atomic, decision-centric, and timely: it asks only one question, focuses on a specific fact, event, or activity, and supports a single decision. This skill adds a fifth test, answerability, which commercial CTI practice makes necessary.
In all cases, a requirement is phrased as a question. "Monitor ransomware" is an activity, not a requirement.
Under each requirement, list 2 to 4 Essential Elements of Information (EEIs): the individual facts an analyst must establish before the requirement can be considered answered. EEIs are what make a requirement collectible. Keep each EEI to a short noun phrase of roughly 5 to 10 words, not a full sentence. For example, for the requirement "Which threat actors have targeted our sector in LATAM in the past 12 months?", EEIs might be: "Actors with confirmed sector victims in LATAM"; "Their observed targeting of new market entrants"; "Attribution confidence per actor".
Where the user indicates a mature team, also suggest Collection Requirements (what data sources need to be tasked) and Production Requirements (what intelligence products will be created and on what cadence). Do not force these on users who only need the requirements themselves.
For each requirement, provide four things:
Collection guidance. Name specific source types and, where possible, specific starting points. "Monitor the dark web" is not collection guidance. "Track affiliate recruitment posts on RAMP and Exploit forums, ransomware leak sites for victims in [sector], and vendor reporting on [actor] campaigns" is. Prefer free and open sources where they exist, and say which sources require paid access.
Success criteria. Define how the stakeholder will know the requirement has been answered. Phrase as observable outcomes: "The stakeholder can name the three most likely initial access techniques and has a prioritized mitigation list" rather than "stakeholder is informed."
Deliverable and cadence. Name the intelligence product or service that will answer the requirement (for example, a one-time briefing, a monthly threat landscape report, a quarterly stakeholder review, or an alerting service) and how often it will be delivered (One-time, Ad hoc, Weekly, Monthly, Quarterly, or Continuous). Base this on the Stage 2 answer about what a useful output looks like to the stakeholder. PIRs usually take a one-time or ad hoc deliverable; enduring requirements usually take a recurring one.
Criticality rating. Score the requirement using the matrix below and assign Critical, High, Medium, or Low. Show the component scores so the rating is defensible rather than asserted.
Score each dimension from 1 (low) to 5 (high), then average:
| Dimension | What to assess |
|---|---|
| Decision impact | How consequential is the decision this supports? Revenue, safety, regulatory, or executive-level decisions score high. |
| Time sensitivity | How soon must the decision be made? Imminent deadlines score high. |
| Stakeholder breadth | Do multiple teams ask similar or overlapping questions? Requirements serving several stakeholders score high. |
| Feasibility | Can the team realistically answer this with current skills, data access, and tooling? Easily answerable scores high. |
Mapping: average 4.0 or above = Critical; 3.0 to 3.9 = High; 2.0 to 2.9 = Medium; below 2.0 = Low.
Feasibility is included deliberately. Early in an intelligence program, demonstrating value quickly matters, so a moderately impactful requirement the team can answer this week may rank above a highly impactful one it cannot answer at all.
Produce exactly three files in total: one markdown record, one Word document, and one CSV list. This holds no matter how many questions the stakeholder asked. Do not split the output into a separate file, set, or trio per question. Where the environment supports file creation, write all three as actual downloadable files; otherwise render the markdown in the conversation and say so.
When the stakeholder asked more than one distinct question, all of those questions and their requirements go into the same three files. Use one shared summary block at the top that covers the whole set, then list every enriched requirement together under a single Requirements section, and put every row in the one CSV. The distinct questions do not fragment the output; they are preserved inside it. Give the summary block a single row for the requesting stakeholder(s) and a "Questions addressed" row that lists each stakeholder question with the decision it supports, so a reader sees at a glance which decisions the set serves. Every requirement then carries its own context through its linked-decision field (naming which of those questions and decisions it answers), and every CSV row carries it through the Stakeholder and Decision_Supported columns. Keep IDs unique across the whole set and grouped in a contiguous run per originating question (see the ID rule below) so a reader can still tell which question a requirement came from without needing separate files.
Output 1: The intelligence requirements set (markdown). Use the template in assets/requirements-template.md. This is the analytical record and must include:
Keep the prose tight. Collection guidance and success criteria should be one short bullet each per point; do not pad fields to fill the template.
Output 2: The intelligence requirements set (Word document). The same content as the markdown record, produced as a single .docx file covering the whole set, for analysts who need to circulate the requirements to stakeholders or attach them to an approval workflow. Mirror the markdown structure: one shared summary table, then one section per requirement across all questions, assumptions at the end. Where .docx creation is not available in the environment, say so and offer the markdown file as the alternative.
Output 3: The intelligence requirements list (CSV). Use the column structure in assets/requirements-list-template.csv. This is the working file the team appends to their existing intelligence requirements list, uses to guide operations, and imports into security tooling. Rules:
IR_ID, Type, Requirement, Stakeholder, Decision_Supported, Criticality, Status, EEIs, Intelligence_Deliverable, Delivery_Cadence, Success_Criteria, Owner, Date_Created, Review_Date.Use requirement IDs in the format IR-YYYY-NNN for enduring requirements and PIR-YYYY-NNN for priority requirements, and keep IDs identical across all three files so they stay traceable to each other. When one ask covers multiple questions, all IDs still belong to the same set and must be unique across it; group each question's IDs in a contiguous run (for example IR-2026-101 to 104 for the first question, IR-2026-111 to 113 for the second) so a reader can tell at a glance which question an ID came from without needing separate files.
This stage runs only if the Feedly MCP server is connected. Detect it by checking whether Feedly MCP tools (for example search_entities, get_threat_actor_relationships, search_ttps) are available in the session. If they are not, skip this stage entirely; the three Stage 6 files are the complete output, and no collection or monitoring queries are emitted in any form.
If Feedly is connected, use it to begin answering the requirements immediately rather than leaving collection as a manual follow-up. Read references/feedly-integration.md for the tool-routing table and operating rules before running anything.
Run this stage in two steps.
Step 1: Assess, route, and get sign-off. For each fully enriched requirement (candidates are excluded), read its EEIs, not just its headline, and classify what it is asking for: named-actor attribution, technique or TTP, malware or targeting relationships, vulnerability or CVE, sector or region landscape, base rate or scale, or current-activity grounding. Select the single best-fit Feedly tool for that class, plus at most one pivot where a relationship expansion is warranted; do not run every tool against every requirement. Present the routing plan in the chat as a table and stop for the user to verify and approve it. Every row must show the requirement code together with its full requirement question (not the code alone), the requirement's EEIs, what the lookup will help answer, and the tool chosen. Do not run any lookups until the user OKs the plan.
Step 2: Collect, report, and offer to enrich. Once the user approves the plan, run the lookups. Resolve names to entity IDs with search_entities before entity-based tools, and match each tool's time period to the timeframe stated in the requirement. Then present, in the chat, both the approved routing plan and the findings. Format the findings as parent bullets with indented sub-bullets under a subheading per requirement, where the subheading is the requirement code plus its full requirement question. Use a parent bullet for each point and indented sub-bullets for its supporting detail; for example, a parent bullet stating that attribution points to a named cluster, with each associated actor listed as its own indented sub-bullet beneath it. For each requirement, state the date range of the Feedly data used (for example, "Data range: past 12 months"), give the findings as nested bullets with inline article citations, and flag any EEI the lookups could not answer as an open collection gap rather than inventing an answer. Finally, offer to fold the results back into the three Stage 6 files in place: seed EEIs with the named entities found, add entity-backed sources to collection guidance, and revise criticality where current activity justifies it. Update the files only if the user accepts.
If the connected Feedly MCP also exposes a feed-creation tool, additionally offer, after reporting, to create one monitoring feed per requirement. Confirm feed names and logic before creating anything, and never create more feeds than requirements.
Input (vague ask): "Our CISO came out of a board meeting and wants to know how worried we should be about NPM supply chain attacks."
Stage 1 read-back: Stakeholder is the CISO, audience is likely the board. The decision is probably about software supply chain risk posture and potential control investment, but the timeframe, the trigger for the ask, and the organization's actual NPM exposure are unclear.
Stage 2 questions asked: What decision is leadership weighing (control investment, risk acceptance, awareness only)? When does the CISO report back? How exposed is the engineering practice to NPM?
Sample output (abbreviated):
IR-2026-021 (Enduring, GIR) Requirement: Which threat actors and campaigns have compromised NPM packages to target enterprises in the past 12 months? EEIs: Campaigns with confirmed enterprise victims, past 12 months; attribution confidence per campaign; opportunistic versus sector-targeted pattern. Collection guidance: npm/GitHub security advisories and the GitHub Advisory Database (free); vendor research on registry compromises (Socket, ReversingLabs, Snyk, Sonatype, free); CISA advisories (free); OSV.dev and the OpenSSF malicious packages repository (free). Success criteria: CISO can name the campaigns active against the NPM ecosystem and state whether targeting appears opportunistic or directed at our sector, in board-ready language. Criticality: High (decision impact 4, time sensitivity 3, stakeholder breadth 3, feasibility 5; average 3.75).
IR-2026-022 (Enduring, GIR) Requirement: Which techniques are being used to compromise NPM packages (account takeover, typosquatting, dependency confusion, malicious install scripts) in the past 12 months? EEIs: Prevalence of each technique in observed incidents; emerging techniques outside the known categories; techniques observed defeating common registry controls. Note: Split from IR-2026-021 because "who is attacking" and "how they compromise packages" are two facts, collected from different sources and answered on different timelines. The parenthetical enumerates categories of a single fact; it does not make the question compound.
PIR-2026-007 (Priority) Requirement: Is there evidence from the past 12 months of malicious NPM packages defeating controls equivalent to ours (lockfiles, registry pinning, SCA scanning)? Note: Time-bound to the next board cycle; expires once the board endorses a posture. The board's actual question ("how worried should we be?") is a risk judgment, not an intelligence requirement; this PIR collects the control-efficacy evidence that judgment needs, and the requirement set states that the worry level is the CISO's synthesis of IR-2026-021, IR-2026-022, and this evidence.
Candidate requirements (not enriched): At which points do NPM packages currently enter our products and build pipeline? Valid and collectible from internal sources, but deprioritized to keep the initial set focused; promote if the team has capacity to task engineering sources.
© TracecatHQ, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 6 other files (references, assets) in tracecat/agent/skill/library/skills/intelligence-requirements-builder of TracecatHQ/tracecat.
Open the folder on GitHubat commit 383b245
Intelligence Requirements Builder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Intelligence Requirements Builder this skillTracecatHQ/tracecat | 3.8k | — | ~6k | Automated safety check: Pass | MIT | |
| Markitshift-labs-ai/markit | 1.3k | — | ~299 | Automated safety check: Pass | MIT | |
| File Intelearlyaidopters/second-brain | 194 | — | ~481 | Automated safety check: Pass | None | |
| File ReadingWide-Moat/open-computer-use | 126 | 1 repos | ~3.1k | Automated safety check: Pass | Proprietary | |
| Eval Generatormicrosoft/eval-guide | 138 | — | ~7.4k | Automated safety check: Pass | MIT | |
| Markdown Converterintellectronica/agent-skills | 295 | 4 repos | ~492 | Automated safety check: Pass | CC0-1.0 |
shift-labs-ai/markit
Convert files and URLs to Markdown. An agent skill from shift-labs-ai/markit.
earlyaidopters/second-brain
Run the Gemini file processor on any folder — extracts content from PDF, PPTX, XLSX, DOCX, CSV, JSON, and any text format, then generates Obsidian-ready summaries.
Wide-Moat/open-computer-use
A skill your agent uses when a file has been uploaded but its content is NOT in your context — only its path at /mnt/user-data/uploads/ is listed in an uploadedfiles block.
microsoft/eval-guide
Generate standalone — turns the populated Eval Suite Planning workbook (output of /eval-suite-planner) into concrete capability eval sets and trust & safety eval sets.
intellectronica/agent-skills
Convert documents and files to Markdown using markitdown. An agent skill from intellectronica/agent-skills.
maziyarpanahi/openmed
Detects and extracts tabular laboratory panels from PDFs, scans, and images into structured rows ready for OpenMed and FHIR.
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TracecatHQ/tracecat
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Rates a threat against the OWASP Risk Rating Methodology, then rates it again counting only the mitigations that are implemented and verified, and again counting dated commitments, and shows the…
TracecatHQ/tracecat
Cut a stable GitHub release or prerelease directly from a Tracecat release branch, including the version bump, tag, image verification, and categorized release notes.
TracecatHQ/tracecat
Create, retitle, or label a pull request for the current branch.
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Investigate AWS credential compromise, STS session abuse, and API breaches; produce an evidence-backed timeline, containment plan, and incident handoff.
Works with
Categories
Turns a vague, high-level stakeholder ask into a structured set of intelligence requirements for a CTI team, complete with Essential Elements of Information, collection guidance, success criteria…. Intelligence Requirements Builder is an agent skill from TracecatHQ/tracecat. Turns a vague, high-level stakeholder ask into a structured set of intelligence requirements for a CTI team, complete with Essential Elements of Information, collection guidance, success criteria, deliverables, and a criticality rating, producing the intelligence requirements set as markdown, as a Word document, and as a CSV intelligence requirements list.
Intelligence Requirements Builder fits situations like: the user mentions intelligence requirements; stakeholder requests; requirements gathering; says things like my CISO asked me to look into X.
Run `npx skills add TracecatHQ/tracecat --skill intelligence-requirements-builder -a claude-code`. Or copy the skill folder (tracecat/agent/skill/library/skills/intelligence-requirements-builder in TracecatHQ/tracecat) into .claude/skills/intelligence-requirements-builder in your project. Claude Code loads it when a task matches its description.
Run `npx skills add TracecatHQ/tracecat --skill intelligence-requirements-builder -a codex`. Or copy the skill folder (tracecat/agent/skill/library/skills/intelligence-requirements-builder in TracecatHQ/tracecat) into .agents/skills/intelligence-requirements-builder in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add TracecatHQ/tracecat --skill intelligence-requirements-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/intelligence-requirements-builder, .gemini/skills/intelligence-requirements-builder, .github/skills/intelligence-requirements-builder and .opencode/skills/intelligence-requirements-builder in your project.
SKILL.md names no scripts, command-line tools or credentials: Intelligence Requirements Builder is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Intelligence Requirements Builder is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.
About 6k tokens (SKILL.md is roughly 24k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Intelligence Requirements Builder: Markit (shift-labs-ai/markit, 1.3k stars), File Intel (earlyaidopters/second-brain, 194 stars), File Reading (Wide-Moat/open-computer-use, 126 stars) and Eval Generator (microsoft/eval-guide, 138 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
TracecatHQ (a GitHub organization) maintains it in TracecatHQ/tracecat, which has 3,830 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on October 9, 2026.
Source: TracecatHQ/tracecat on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.