Primr

primr

Run a long, metered, autonomous research pipeline that turns a company URL into a structured strategic brief. primr is not another web-search tool. It combines DNS recon, multi-tier scraping, hiring-signal extraction, provider-backed AI synthesis, and structured report generation. A typical run produces a 23-section Strategic Overview plus an AI Strategy module by default, lands in output//, and feeds the rest of the user's analytical workflow.

When this is the right tool

Use primr when the user wants the full pipeline:

• "Run primr on Contoso" / "primr Contoso https://contoso.com"
• "Build me the full strategic dossier for Acme - I have a discovery in two weeks"
• "Generate the AI strategy module for the Acme report"
• "Reload the hypotheses we have on Acme and refresh weak ones"

Do not use primr for:

• A quick pre-call brief with no API budget - use the host's built-in web search and reasoning. primr is wrong for "give me two paragraphs on Acme."
• DNS / tenant / email-security only - primr's recon stage is bundled into the full pipeline, not a standalone path. For passive lookups, shell out to dig, host, or a passive-recon tool.
• Reviewing an existing primr report's quality - still primr (run_qa MCP tool or primr --qa CLI), but invoke that path directly without estimating a new run.

If the user is ambiguous ("research Acme"), default to the host's built-in research path and offer primr as the upgrade for "I want the full dossier." primr's cost and runtime mean it should never auto-fire on a vague trigger.

Before first invocation

Confirm primr is installed and configured before the first call in a session:

primr --version
primr doctor

If primr is not on PATH:

> "primr isn't installed. It's a Python CLI from github.com/blisspixel/primr. Use pip install primr on Python 3.12+. After install, run primr init to set provider keys. Grok + Gemini is the measured default, but OpenAI, Anthropic, and local OpenAI-compatible paths are also supported or tracked in the roadmap. Want me to walk through it?"

Wait for explicit approval before running pip install. If primr doctor reports missing keys, do not attempt to set them yourself - surface the gap and let the user run primr init or primr keys set .

Detecting MCP vs CLI

Look at your own available-tools list before choosing transport:

• If you see mcp__primr__* tools (mcp__primr__estimate_run, mcp__primr__research_company, etc.) → prefer MCP. It returns structured objects, exposes job state via resources, and the cost gate is enforced server-side.
• Otherwise → fall back to the primr CLI. Same workflow, file-based artifacts.

Do not call an MCP tool speculatively to test connectivity. To get from CLI-only to MCP, the user adds primr to their host's MCP config (the snippet is { "command": "primr", "args": ["mcp"] }; see the clients/ directory in the primr repo for per-host paths).

The cost gate (non-negotiable)

primr runs cost real money and real time. Never launch a run without:

1. Estimating first. MCP: estimate_run(company_url=..., mode=..., platforms=[...], strategy_type=...). CLI: append --dry-run to the exact command you intend to run.
2. Reporting the estimate verbatim - quoted dollars and minutes, plus what mode and what strategy.
3. Getting explicit user approval in the conversation. "Want me to launch it?" → wait for "yes" / "go" / equivalent. A user asking "how much would it cost" is not approval.

If the user pushes back on cost, suggest a cheaper mode (scrape ~$0.10, or --no-ai-strategy around ~$0.76-$0.79 on the measured xAI plus Gemini recipe) before walking away. If they want premium depth, surface --premium (~$5) and re-estimate.

The MCP server enforces this gate via primr://agent/governance; the CLI does not, so on CLI you are the gate.

Default workflow

1. Estimate. estimate_run (MCP) or primr "Name" url --dry-run [flags] (CLI). Capture the cost, time, page count, planned strategy.
2. Approve. Quote the estimate, ask for go-ahead. Stop. Do not proceed without an explicit "yes."
3. Launch. MCP: research_company(company_name=..., company_url=..., mode=..., platform=..., destination=...) → returns job_id. CLI: drop --dry-run and run the same command. Note the job_id or output directory.
4. Don't block. Runs take 35-120 minutes. Tell the user the job is running and what file path will hold the report. Do not poll synchronously in a loop.
5. Resume on next turn. When the user comes back ("is the Acme report done?"), check primr://research/status or check_jobs (MCP), or look for the markdown file at output//_Strategic_Overview_.md (CLI). If still running, report the stage and stage_progress_percent.
6. Confirm completion, then hand off. If using MCP, read primr://output/artifacts/by_job/{job_id} first to list the owned job's artifacts without loading report body content. If QA ran, read primr://output/qa_summary/by_job/{job_id} for compact score/status/count metadata. Read primr://output/usage_summary/by_job/{job_id} when you need cost, timing, approval, or artifact-count metadata without loading the full manifest. Read primr://output/source_summary/by_job/{job_id} when you need citation/source appendix counts, domains, missing citations, or duplicate URL metadata without loading report body content. Read primr://output/trace_summary/by_job/{job_id} when you need scrape trace health, tier attempts, latency, block, HTTP status, or validation metadata without loading URLs, raw trace entries, or page content. Read primr://output/verification_summary/by_job/{job_id} when you need claim verification trust score, claim counts, status counts, first-party downgrade counts, or source-reference counts without loading raw claims, source URLs, search queries, explanations, or report body content. Read primr://output/calibration_summary/by_job/{job_id} when you need label-calibration counts, evidence-review count buckets, judge provenance, or judge-agreement metadata without loading raw claims, source URLs, evidence reviews, rationales, or report body content. MCP resource reads and A2A skill calls are audit-logged without raw URI query values, raw resource bodies, raw message text, raw results, or caller ids. Then read the report path or preview needed for the handoff. Do NOT dump the full report into the conversation - it's ~21k words. Summarize the executive summary, list the section count, and offer downstream actions (see [references/downstream-handoff.md](references/downstream-handoff.md)).

Mode, tier, platform, strategy

primr exposes four orthogonal levers. Default is full mode, recon-driven platform selection, default --grok-tier, and the built-in AI Strategy module unless --no-ai-strategy is passed.

For the full decision matrix - when to pick each, cost and time per combination, multi-platform behavior - see [references/modes-and-strategies.md](references/modes-and-strategies.md). One-liner heuristics:

• Mode: full for almost everything. scrape if external research isn't needed. deep if the site is blocked. premium only when the user asks for board-grade depth.
• Platform: omit unless the user is positioning a specific cloud. Then pass exactly what they're selling against (--platform aws, --platform ms, etc.). It biases the AI strategy module, not the core report.
• Strategy type: omit for the default Strategic Overview plus AI Strategy. Pass --no-ai-strategy for the base report only. Use --strategy-type customer_experience, modern_security_compliance, data_fabric_strategy, cloud_migration, data_strategy, or ai_first_transformation when the user asks for a different strategy deliverable. Use primr --list-strategies or primr://strategies/available to enumerate.

Custom strategies

primr discovers any YAML file dropped into /prompts/strategies/ (or the user's override path). Author one when the user wants a recurring deliverable that doesn't fit the built-ins (e.g., "FinOps assessment for retail clients", "M&A integration playbook").

For the schema and a worked example, see [references/custom-strategy-yaml.md](references/custom-strategy-yaml.md). Keep custom strategies in version control; do not edit a built-in YAML in place.

Async monitoring

Long runs are the common case. Pick the lightest async pattern your host supports - never poll synchronously in a tight loop.

Preferred, in rough order:

1. Background launch with completion notification. If your host can run a command in the background and notify you when it exits, use that to launch primr. You get one event ~45 min later when the run finishes. This is the cleanest pattern.
2. Stream phase markers from the log. If your host can tail a file and emit one event per matching line, watch the run log for the phase boundaries (PHASE, Complete, Error) - about 6-8 events across a full run. Right density for "is it making progress?" without polling noise.
3. Schedule a single early sanity check at ~5 minutes. Most failures (rejected key, scrape pilot fails, no external sources) surface in the first phase. A one-shot check at +5min catches those before the user wastes 45 minutes.
4. Fallback (no async primitives at all). Tell the user "I'll check back in about an hour" and stop. When the next turn arrives, read state first (check_jobs / primr://research/status / the report file at output//_Strategic_Overview_.md), then summarize.

On every follow-up turn, regardless of how you got there: read state first. Never claim done until the report file exists and check_jobs reports status: completed. On failure, read primr://output/manifest/latest if available - it contains the audit trail (estimate, approval, execution, error). Surface the failure cause; do not silently re-launch.

What not to do:

• Don't poll in a tight loop or with sub-minute sleeps. primr stages take minutes; sub-minute polling burns context for no information.
• Don't promise a heartbeat cadence the host can't actually deliver. If you can't schedule wake-ups, "check back in an hour" is honest; "I'll update you every 10 minutes" is not.
• Don't treat the absence of completion as failure. A run that's been going 60 minutes is probably fine; check the log for the most recent phase marker before assuming the worst.

Output handling

Reports land in output//:

• _Strategic_Overview_.md - primary deliverable
• _AI_Strategy_.md - only if --strategy-type ai (or another module) was selected
• scraped_content.txt, insights.json, dossier.json - pipeline intermediates
• run_manifest.json - audit trail

When the user asks "what did we get": for MCP, prefer primr://output/artifacts/by_job/{job_id} as the first inventory read; it returns file names, paths, sizes, hashes, timestamps, classifications, and missing-file state without report body content. If QA artifacts are attached, use primr://output/qa_summary/by_job/{job_id} for compact QA score/status/count metadata without detailed QA body text. Use primr://output/usage_summary/by_job/{job_id} for compact cost, timing, approval, and artifact-count metadata without full manifest content. Use primr://output/source_summary/by_job/{job_id} for compact citation/source appendix counts, domains, missing citations, duplicate URLs, and source URLs without report body content. Use primr://output/trace_summary/by_job/{job_id} for compact scrape trace counts, tier health, latency, block, HTTP status, and validation metadata without URLs, raw trace entries, or page content. Use primr://output/verification_summary/by_job/{job_id} for compact claim verification trust score, claim counts, status counts, first-party downgrade counts, and source-reference counts without raw claims, source URLs, search queries, explanations, or report body content. Use primr://output/calibration_summary/by_job/{job_id} for compact label-calibration counts, evidence-review count buckets, judge provenance, and judge-agreement metadata without raw claims, source URLs, evidence reviews, rationales, or report body content. MCP resource reads and A2A skill calls are audit-logged with hashed payload values, without raw URI query values, resource bodies, message text, raw results, or caller ids. Then list the artifacts, quote the executive summary, and note section count. Do not dump the full markdown unless they ask for the full text. Offer to convert to DOCX (primr writes both by default in full mode) or to feed it to a downstream consumer.

Downstream handoff

primr's outputs are inputs to the rest of the user's toolchain. Do not let a report sit unused. When a run completes, proactively offer the next step the user is likely to need.

For the mapping (Strategic Overview → which next action, AI Strategy module → which next action, hypotheses → which next action), see [references/downstream-handoff.md](references/downstream-handoff.md).

Hypothesis memory

primr persists durable research memory per company. Before launching a new run on a company you've researched before:

• MCP: call get_hypotheses(company=...) and read primr://memory/ to see what's already known.
• After the run completes, the pipeline saves new hypotheses automatically. If the user asks you to record a finding from a customer conversation, use save_hypothesis(company=..., statement=..., confidence=..., evidence=...).

Display confidence levels honestly (untested, validated, invalidated, confirmed). Do not promote hypotheses without new evidence.

How to talk about primr's output

primr's voice is hedged strategic analysis with cited sources. Mirror that:

• Surface confidence annotations the report already carries - don't strip them.
• Quote sources from the citation appendix when the user pushes on a specific claim.
• Say "primr's analysis suggests…" rather than asserting findings as facts; the report is one input, not ground truth.
• If the user asks a question the report doesn't cover, say so - do not extrapolate beyond what's written.

Gotchas

See references/gotchas.md for the living list of real observed failure modes (cost gate, async, thin evidence, etc.) and how to avoid them. This file is the primary place for updates.

Key highlights:

• Always estimate first and get explicit approval.
• Long runs are async; check state on next turn.
• primr is for full pipeline, not quick briefs or DNS-only.

Update references/gotchas.md from real failures when using the skill. Load it only when needed (progressive disclosure).

Hard rules

• Cost gate. Never launch a billable run without a fresh estimate and explicit approval in the same conversation turn.
• No synchronous waits. Long runs are async; check state on the next turn.
• No silent retries. A failed run gets reported back to the user with the manifest's error context - they decide whether to re-run.
• No re-estimating against an active job. If check_jobs shows the company already running, surface that and ask if they want to monitor it instead of starting a parallel run.
• Defer behaviorally, not by skill name. Vague "research X" with no budget → use the host's web search and reasoning. DNS-only → shell out to dig or a passive-recon tool. Quality-checking an existing report → run_qa directly, no new estimate.
• Never edit built-in strategy YAMLs. Custom strategies live in the user's override path; built-ins ship with the package.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: blisspixel
• Source: blisspixel/primr
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.