Exec Briefing
gtmagents/gtm-agents
A skill your agent uses to craft concise executive updates, agendas, and follow-up logs for QBR/EBR sessions.
Run experiments and papers in a PenguinHarness research organization — fix the harness and the metric first, run an autoresearch-style loop (one editable surface, the same time budget per…
$ npx skills add Prism-Shadow/penguin-harness --skill company-research -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Prism-Shadow/penguin-harness company-research --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/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/agent-company/skills/company-research .claude/skills/company-research && 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 "company-research" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/agent-company/skills/company-research into .claude/skills/company-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "company-research", 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/Prism-Shadow/penguin-harness/tree/main/plugins/agent-company/skills/company-researchType 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 Prism-Shadow/penguin-harness --skill company-research -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Prism-Shadow/penguin-harness company-research --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/agent-company/skills/company-research .agents/skills/company-research && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "company-research" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/agent-company/skills/company-research into .agents/skills/company-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "company-research", 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 Prism-Shadow/penguin-harness --skill company-research -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Prism-Shadow/penguin-harness company-research --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/agent-company/skills/company-research .cursor/skills/company-research && 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 "company-research" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/agent-company/skills/company-research into .cursor/skills/company-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "company-research", 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/Prism-Shadow/penguin-harness.git --path plugins/agent-company/skills/company-research--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 Prism-Shadow/penguin-harness --skill company-research -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Prism-Shadow/penguin-harness company-research --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/agent-company/skills/company-research .gemini/skills/company-research && 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 "company-research" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/agent-company/skills/company-research into .gemini/skills/company-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "company-research", 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 Prism-Shadow/penguin-harness company-researchInstalls 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 Prism-Shadow/penguin-harness --skill company-research -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/agent-company/skills/company-research .github/skills/company-research && 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 "company-research" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/agent-company/skills/company-research into .github/skills/company-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "company-research", 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 Prism-Shadow/penguin-harness --skill company-research -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Prism-Shadow/penguin-harness company-research --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/agent-company/skills/company-research .opencode/skills/company-research && 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 "company-research" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/agent-company/skills/company-research into .opencode/skills/company-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "company-research", 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.
company-researchRun experiments and papers in a PenguinHarness research organization — fix the harness and the metric first, run an autoresearch-style loop (one editable surface, the same time budget per…
Company Research is an agent skill from Prism-Shadow/penguin-harness. Run experiments and papers in a PenguinHarness research organization — fix the harness and the metric first, run an autoresearch-style loop (one editable surface, the same time budget per experiment, a results log, keep only improvements) inside a resource envelope the board approved in the all-hands channel, and put every claim through adversarial review by a reviewer who is not its author.
Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Sales & Support, covering Sales call preparation, Autonomous loops and Internal communications. The repository describes itself as: 🐧 Unified and Stable RSI Platform. The licence is Apache-2.0.
7 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 2604c5d. 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.
Shell commands in SKILL.md call:
gitFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
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.
Company Research loads about 3.4k tokens when it runs. Until then it costs about 103 tokens; SKILL.md has 1,965 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 Prism-Shadow/penguin-harness at commit 2604c5d, republished under its Apache-2.0 licence (© Prism-Shadow). 1,965 words, ~3,443 tokens.
.claude/skills/company-research/SKILL.md (or your agent's skills folder).A research organization produces claims — a number on a metric, a method that beats a baseline, a paper — and a claim is worth exactly what survives an attempt to break it. This skill is what the researchers and the reviewers of such an organization add to company-employee: an experiment loop shaped after autoresearch, a resource envelope the board approves before the loop starts, and a review that is adversarial by design because the reviewer is never the author. The CEO's checklist in company-ceo still applies; this skill says what the tickets of a research stream look like inside it.
If the message only names this skill (e.g. "use company-research skill") without a concrete request, ask what is wanted — an experiment loop to set up, a run to continue, a claim to review, a review to answer. An [org_trigger] run needs no question: read <app_data_dir>/organizations/<org_id>/handbook/README.md and act on what the block says.
The mission asks for research: experiments to run, results to claim, papers to write. Two roles use this skill:
Both are ordinary employees: the desk schedules, ticket sessions do the work, the handbook comes first. A researcher's or reviewer's brief (agent_state/AGENTS.md) names company-research beside company-employee.
An experiment loop is the textbook case of what company-employee calls heavy or long compute, so it never starts on your own decision. Before the first experiment of a ticket, ask the board — the organization's creator, @user:<id> from created_by in org_config.toml — in the all-hands channel for the envelope, in one message:
Then block the ticket on the board and end the run, exactly as "Asking the board" in company-employee says:
penguin org channel send -m "@user:alice 2026-09-01-dep-eval is ready for its experiment loop: 5-minute runs on GPU 0 (about 10 GB of VRAM), one at a time, 40 runs or 4 h in total, 6 GB of disk under <app_data_dir>/organizations/co_lab/workspace/experiments/dep-eval/; the dataset is already in the shared root and no paid API is involved. It runs inside the ticket session — stop that session and the loop stops. May I start? Otherwise I stop at 10 runs." --ref-ticket 2026-09-01-dep-eval
penguin org ticket block 2026-09-01-dep-eval --reason "Waiting for the board's resource envelope for the experiment loop" --by user:aliceOnce approved, write the envelope into the ticket's ## Goal (machine, concurrency, total, disk, keys — the numbers as approved) and into the handbook as decisions/<yyyy-mm-dd>-envelope-<ticket_id>.md, so the next session reads it instead of asking again. Inside the envelope the loop runs unattended. Reaching its total ends the loop; exceeding it — more hours, a second GPU, more runs — or needing something new — a dataset that is not there, an API key, a bigger model — is a new ask in the same channel, with the same block-and-end. A resource you lack is something you request, never something you work around: no hunting for a key on the machine, no substituting a dataset the ticket did not name, no quietly running on the CPU when the GPU was refused.
Before anything is tuned, freeze what "better" means:
## Acceptance criteria by full path, and nobody edits them while a loop runs: not the author, not to "fix" a number that looks wrong. A harness or metric that turns out wrong is a new ticket, and every result logged before the fix is re-run or discarded.The experiment directory lives in your workspace partition, one per ticket:
<partition>/experiments/<ticket_id>/
.gitignore # lists results.tsv and logs/
eval.* # the harness — fixed
<surface> # the one editable file — the only file a run commits
results.tsv # one line per experiment — never committed
logs/<tag>.log # each run's output — never committedInitialize it as a git repository (or a sub-tree of one) so that a run is a commit of the surface, never of the record. Write the .gitignore first, listing results.tsv and logs/, and commit it with the harness and the untouched surface as the baseline; from then on results.tsv and logs/ stay untracked. A tracked results.tsv makes the checkout that discards a run abort, and forcing past it — checkout -f, reset --hard — throws away the discard line the reviewer reads. Then, one experiment at a time, in a ticket session:
git checkout -b run-<tag> from the last kept commit; change the surface; commit the surface alone: git add <surface> && git commit -m "<tag>: <hypothesis>".timeout <2×budget> <command> > logs/<tag>.log 2>&1; read the metric from the harness output.results.tsv: commit<TAB>metric<TAB>peak memory<TAB>status<TAB>description, status one of keep, discard, crash.keep only when the metric beats the best kept result so far; the branch then becomes the new base. Otherwise discard: git checkout <last kept commit>, then git branch -D run-<tag> (-D: a discarded run is never merged). The untracked results.tsv and logs/ stay as they are, discard line included.tail -n 60 logs/<tag>.log); fix a bug in your own edit and re-run under a new tag, or discard a hypothesis that cannot be made to run. Three crashes in a row are a stop: write what you learned in a progress line and pick a different direction, or block the ticket.## Result with the best kept commit and its metric, and the hand-off to review below.Run the loop in the foreground of the ticket session — never in the background beyond it, never from the desk. A sweep that finds a loop's session ended without a final progress line reads results.tsv and the log tail before starting the next session.
## Result, and any paper or report, states the claim as the log supports it: the metric before and after, the kept commit, the baseline, the ablations that separate what mattered from what did not, the seeds and how many, the budget every number was produced under, and every path in full — results.tsv, the harness, the deliverable. A number that has no line in results.tsv and no commit is not a result; drop it. Say what was not tried and what could still explain the improvement.
A claim goes through the board's review column, and the reviewer's job is to try to break it. The move itself starts nothing — notify hears only of a ticket's close, only the owner may open a session on the ticket, and the owner's sweep starts sessions only for in_progress tickets — so on every round the author starts the review itself, right after moving the ticket to review with ## Result complete:
penguin org ticket move 2026-09-01-dep-eval --to review
penguin org ticket start 2026-09-01-dep-eval --agent-id co_lab_reviewer -m "Review round 1: the claim is in ## Result; results.tsv, logs/ and the harness are in <app_data_dir>/organizations/co_lab/workspace/co_lab_researcher/experiments/2026-09-01-dep-eval/"That session runs as the reviewer, in the reviewer's own partition, with the whole ticket and the note as its first message: name the round and the experiment directory by full path. No mention is needed — it would only wake a desk that cannot open the session.
The reviewer works in that session — or, at the CEO's request, in a session on a review ticket of its own — and works down this list, writing what it finds:
git clone <experiment directory> reviews/<ticket_id>/repro), check out the kept commit there and run the harness within the envelope's re-run allowance (one run per headline number is the default; more is a new ask); compare against the author's results.tsv and logs, read in place by full path — nothing is checked out or run in the author's directory;## Result or the paper have a line in the log behind it? does the novelty stand against the prior work the author cites — and the prior work it does not?The review is a file in the reviewer's partition, reviews/<ticket_id>/round-<n>.md, named in a progress line by full path: a score (accept, minor revision, major revision or reject), the evidence for each finding, and a numbered list of required changes. Then the verdict on the board:
accept — a progress line saying so; the CEO (or whoever the handbook names as the closer) moves the ticket to done on that line, and a research ticket is never closed without a reviewer's accept in its history;minor or major — penguin org ticket move <id> --to in_progress with a progress line naming the round and the review file; the author's desk picks it up in its next sweep;reject — a recommendation, not a move: rejecting someone else's ticket is the CEO's proposal to the board. Say so in the stream's channel, @-mentioning the CEO with the review's path.The author answers point by point in reviews/<ticket_id>/round-<n>-response.md beside its deliverable — each required change either done (with the commit or the path) or rebutted (with the evidence) — revises, moves the ticket back to review and starts the next round's review session the same way. Silence on a point is agreement to fix it.
The cap: three rounds. If the third review is not an accept, the reviewer blocks the ticket on the CEO — penguin org ticket block <id> --reason "Not accepted after three review rounds: <the open points>" --by agent:<org_id>_ceo — and says so in the stream's channel. The CEO decides — accept as it stands, one more round, or split the claim — or takes it to the board in the all-hands channel; the board's word is final.
history.penguin org finance shows what a claim cost, and a loop that spends twenty runs without a keep is finance's finding as much as yours.© Prism-Shadow, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in plugins/agent-company/skills/company-research of Prism-Shadow/penguin-harness.
Open the folder on GitHubat commit 2604c5d
Company Research 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 |
|---|---|---|---|---|---|---|
| Company Research this skillPrism-Shadow/penguin-harness | 2.5k | — | ~3.4k | Automated safety check: Pass | Apache-2.0 | |
| Exec Briefinggtmagents/gtm-agents | 414 | 1 repos | ~333 | Automated safety check: Pass | Apache-2.0 | |
| Luopan Company Researchzhangxiaoqiang1991/luopan | 389 | — | ~998 | Automated safety check: Pass | MIT | |
| Company Researchstophobia/deerflow2.0-enhanced | 822 | — | ~845 | Automated safety check: Pass | MIT | |
| Meeting Prep BriefBrianRWagner/ai-marketing-claude-code-skills | 441 | — | ~922 | Automated safety check: Pass | None | |
| SdtStopDisTrain/sdt-skills | 309 | — | ~535 | Automated safety check: Pass | MIT |
gtmagents/gtm-agents
A skill your agent uses to craft concise executive updates, agendas, and follow-up logs for QBR/EBR sessions.
zhangxiaoqiang1991/luopan
罗盘的公司研究子模式。研究具体上市或非上市公司的财务增长、商业模式、 竞争生态位、治理与组织信号,并分别生成投资初筛和求职初筛。
stophobia/deerflow2.0-enhanced
综合企业背景调研技能,用于尽职调查、合作伙伴评估、投资分析、市场研究等场景。支持公司工商信息、财务数据、法律风险、舆情分析、竞品对比等多维度调研,自动生成Markdown和HTML格式的专业调研报告。触发条件:用户提及"企业调研"、"公司背景"、"尽职调查"、"合作伙伴评估"、"投资分析"、"市场研究"等关键词。
BrianRWagner/ai-marketing-claude-code-skills
Builds a pre-meeting brief from your Obsidian vault: participant research, past notes, open commitments, a prioritized agenda and sharp questions.
StopDisTrain/sdt-skills
SDT 内容生产工具箱的总入口:根据当前任务选择最合适的 sdt- 模块,既能完成标题、开头等单一步骤,也能组织从账号研究到发布的完整流程。用户不知道该用哪个 SDT Skill,或希望一站式完成内容生产时使用。
extruct-ai/gtm-skills
Deep-research a single target account into a decision-ready dossier: the entity tree, the buying units and decision-makers, live signals (open/closed roles, leadership moves, news, tech stack), and…
Prism-Shadow/penguin-harness
Make a reply easier to read and act on with rich blocks inside ordinary Markdown — a choice the user picks from, a form that collects several answers, a procedure as steps with warnings in place, a…
Prism-Shadow/penguin-harness
A skill your agent uses when developing PenguinHarness itself — changing packages/{core,server,web,cli,desktop,landing,docs,skills}, the built-in model catalog, the installers or the release…
Prism-Shadow/penguin-harness
Create and edit Bento presentations — self-contained .bento.html decks whose document is JSON.
Prism-Shadow/penguin-harness
A skill your agent uses when standing PenguinHarness up to try a change by hand — launching the Web App, the desktop shell, the landing page, the docs site or the component gallery to click through…
Prism-Shadow/penguin-harness
Drive the PenguinHarness agent browser — the desktop app's built-in browser or the user's own Chrome — from the shell with penguin browser: open pages, read them as simplified HTML or text, act with…
Prism-Shadow/penguin-harness
A skill your agent uses when changing the PenguinHarness Web App (packages/web) or the shared UI package — adding or restyling any UI, picking a status colour, adding an icon, laying out a row or a…
Run experiments and papers in a PenguinHarness research organization — fix the harness and the metric first, run an autoresearch-style loop (one editable surface, the same time budget per…. Company Research is an agent skill from Prism-Shadow/penguin-harness. Run experiments and papers in a PenguinHarness research organization — fix the harness and the metric first, run an autoresearch-style loop (one editable surface, the same time budget per experiment, a results log, keep only improvements) inside a resource envelope the board approved in the all-hands channel, and put every claim through adversarial review by a reviewer who is not its author.
Company Research fits situations like: tasks that involve Sales call preparation; tasks that involve Autonomous loops; tasks that involve Internal communications.
Run `npx skills add Prism-Shadow/penguin-harness --skill company-research -a claude-code`. Or copy the skill folder (plugins/agent-company/skills/company-research in Prism-Shadow/penguin-harness) into .claude/skills/company-research in your project. Claude Code loads it when a task matches its description.
Run `npx skills add Prism-Shadow/penguin-harness --skill company-research -a codex`. Or copy the skill folder (plugins/agent-company/skills/company-research in Prism-Shadow/penguin-harness) into .agents/skills/company-research 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 Prism-Shadow/penguin-harness --skill company-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/company-research, .gemini/skills/company-research, .github/skills/company-research and .opencode/skills/company-research in your project.
Going by SKILL.md and its folder, Company Research needs the command-line tools its instructions call (git).
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. 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.
Company Research is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Company Research: Exec Briefing (gtmagents/gtm-agents, 414 stars), Luopan Company Research (zhangxiaoqiang1991/luopan, 389 stars), Company Research (stophobia/deerflow2.0-enhanced, 822 stars) and Meeting Prep Brief (BrianRWagner/ai-marketing-claude-code-skills, 441 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Prism-Shadow (a GitHub organization) maintains it in Prism-Shadow/penguin-harness, which has 2,469 GitHub stars. The repository holds 30 skills in this directory. The repository was last updated on October 10, 2026.
Source: Prism-Shadow/penguin-harness on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.