The protocol behind every investigation skill. Use when AI research must proceed without you: search-plan gate, Fact/Inference/Assumption labels, confidence stacking, diffable outputs.
npx skills add https://github.com/deanpeters/Product-Manager-Skills --skill autonomous-investigation
Provide the canonical contract for investigation skills — research the AI performs in the world (web
search, published data, public filings) while you review the evidence instead of feeding it context.
Where workshop-facilitation governs skills that ask you questions one at a time, this protocol governs
skills that *proceed without you*: they budget their questions, show their plan, label every claim, and
produce output stable enough to diff against last quarter's run. That last property is the payoff — an
investigation honoring this contract can run as an agent task, in a loop, or on a schedule.
Nothing required — this skill defines the protocol other investigation skills follow.
Also useful when invoked standalone: the target of the investigation and, above all, **the decision
the research should support**. Research without a decision is a hobby; every investigation skill asks
for the decision because it determines what "just enough" means.
Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an
appended ARGUMENTS: line — counts as answers already given. Use it, credit it against the question
budget, and don't re-ask.
Arriving empty-handed? That works too. The protocol's whole design is to proceed on best-available
evidence with labeled assumptions when nobody answers questions. When another skill references this
protocol, that skill's Input section governs what to provide.
Example invocation: `Run an autonomous investigation on [TARGET]'s move into workflow automation —
this supports our Q3 roadmap bet on the same space.`
| | workshop-facilitation | autonomous-investigation |
|---|---|---|
| Who holds the context | The user | The world (public sources) |
| Interaction shape | One question per turn | Question budget, then proceed |
| Blocked by silence? | Yes — waits for answers | No — labels assumptions and continues |
| Schedulable? | No | Yes — that's the point |
Every investigation skill honors all seven clauses. They are not a menu.
nobody answers, proceed with labeled assumptions. This is what makes investigations schedulable:
an unattended run degrades gracefully instead of stalling.
types, how you'll separate fact from inference. Continue unless the user revises it. *Why it
teaches:* reviewing a plan takes 10 seconds; reviewing a wrong report takes 10 minutes. The gate is
the cheapest correction point in the whole workflow.
Keep labels short. Things you *couldn't find* are not a fourth label — they go in an explicit gaps
list. *Why it teaches:* most competitive "facts" in strategy decks are unlabeled inference. Three-level
honesty is the habit that separates intelligence from confident storytelling.
(competitors, pricing, market share, patent contents, customer wins...) and forbids inventing them.
Real, checkable URLs only; a claim without a source and date is an opinion wearing a badge.
*Why it teaches:* the list tells the human exactly what to verify first.
decision. Verbose Mode exists only on request. Research value is decision support, not page count.
run N+1 are diffable. Delta monitoring, scheduled refreshes, and "what changed since last quarter"
all depend on this clause.
to run, assumptions to validate). Accept 1, 1 and 3, Verbose Mode, or a custom path.
Labels grade individual claims; stacking grades the *story*. When signals arrive from independent
collection channels (see intelligence-collection-disciplines):
~~~
1 channel flags it → Watch item. Log it, do nothing.
2 channels agree → Working hypothesis. Assign someone to probe.
3+ channels agree → Actionable intelligence. Brief leadership, adjust plans.
Channels conflict → The most interesting case. Someone is bluffing. Dig.
~~~
One corollary that generalizes everywhere: **treat announcements as intent until funding, procurement,
hiring, or contracts corroborate them.** Ambition shows up in press releases; commitment shows up in
filings, job posts, and purchase orders.
All collection under this protocol is legal, ethical, open-source work:
specifically to extract a former employer's secrets, scraping in violation of terms you accepted.
The rule of thumb, borrowed from the competitive-intelligence profession (SCIP Code of Ethics): **if
you'd be uncomfortable explaining your method on stage at the target's user conference, don't use the
method.**
Assumption.
not just the signals.
scheduled run), file the output and stop.
A copy/paste investigation brief — the contract's seven clauses as fill-in decisions, for
briefing an agent or designing a new investigation skill — lives in template.md.
Opening of a protocol-honoring run (user gave target + decision inline, so no questions spent):
> Search plan (say "revise" to change it):
> - Search [TARGET]'s pricing pages, release notes, and last two earnings transcripts
> - Source mix: company site, filings, credible press, review sites
> - Facts get URLs; interpretations get labeled Inference; gaps become Assumptions to validate
>
> *(research happens)*
>
> Key finding: [TARGET] removed its mid-tier plan in May — Fact
> (pricing page diff, May 12). Packaging is consolidating toward
> enterprise — Inference (tier removal + two enterprise-only features shipped since April).
> They will raise the entry price within two quarters — Assumption (pattern-based; validate
> against their next pricing-page change).
>
> Final Step — reply 1, 2, 3, 4, a combination, or "Verbose Mode":
> 1. Build the battle card from these findings
> 2. Executive comparison matrix
> 3. Risks/opportunities for the next 2 quarters
> 4. Discovery questions to validate the assumptions
A scheduled run with no human present: the same skill runs quarterly from a saved invocation. The
question budget is already spent (zero questions — context was inline), the plan gate auto-continues,
and the output diffs cleanly against last quarter because the schema didn't move. The delta — not the
report — is what the team reads.
See examples/protocol-in-action.md for a full worked run
(fictional) showing every clause under load — including a user revising the search plan at the
gate and an honest gaps list where the do-not-invent list held.
examples/protocol-in-action-industrial.md shows the
conflict case: four channels agree, one disagrees, and the dig changes the strategic response.
labeled findings, twenty pages is a defect. Just Enough Mode is the contract, not a suggestion.
of two job posts. The label isn't decoration — it tells the reader what to check before betting on it.
names the domain's temptations; honor it or the whole output is suspect.
of plan review. The gate exists because redirecting a plan is cheap and redirecting a report is not.
corroborate with money, hiring, or contracts before you re-plan around them.
delta monitor downstream now compares apples to a reorganized orchard.
channels agree — that's the stacking rule doing its job.
intelligence-collection-disciplines (Component) —the eight collection channels whose signals this protocol labels and stacks
workshop-facilitation (Interactive) — the sibling protocol forskills where the *user* holds the context
market-landscape-scan, competitive-research-snapshot,competitive-intel-watch, battle-card-builder (References section of each names this protocol)
https://github.com/deanpeters/product-manager-prompts repo.
Use when the user is doing AI/ML work in a scientific domain such as biology, chemistry, physics, astronomy, climate, genomics, materials, medicine, ecology, energy, engineering, math, drug discovery, protein design, weather modeling, theorem proving, single-cell, or PDE solving. Hugging Science is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces. This skill helps discover and use resources via `datasets`, `transformers`, the HF Inference API, `gradio_client`, and methodology citations.
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
Semantic search, similar content discovery, and structured research using Exa API. Use when you need semantic/embeddings-based search, finding similar content, or searching by category (company, people, research papers, etc.).
Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-checkers, knowledge bases. Triggers: rag, retrieval augmented generation, grounded ai, search and answer, research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
| Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
Assemble a submission-ready replication package to the AEA Data and Code Availability Standard (DCAS) / openICPSR / Social Science Reproduction Platform expectations — standard replication README, dataset manifest, computational-requirements capture, a Table/Figure → script:line map, and a confidential-data deposit plan. Use when user says "build the replication package", "prepare the openICPSR deposit", "make the AEA data and code package", "DCAS compliance", "assemble the deposit for the journal", or after a paper is accepted and the journal's data editor needs the package. NOT a numeric verifier — it calls /audit-reproducibility to confirm claims reproduce before packaging.
Take deanpeters/autonomous-investigation from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.