What is company-research?

A workflow for researching a company through Primr, a research system that produces reports and related output files. It starts, monitors, and checks a research run.

Use it when researching a company or analysing a company website. It helps estimate and launch the research, monitor its progress, find its output files, and review the final report preview or usage details.

It ensures the current research settings, existing runs, estimated cost, and final output are checked before work begins. It also prevents starting a paid run without approval.

company-research — Skill for Claude Code and Codex by blisspixel

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add blisspixel/primr --skill company-research

Clone the repo

git clone --depth 1 https://github.com/blisspixel/primr

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/blisspixel/primr/company-research/github.svg)](https://agentmods.dev/skills/blisspixel/primr/company-research)

Your own site

<a href="https://agentmods.dev/skills/blisspixel/primr/company-research"><img src="https://agentmods.dev/badge/skills/blisspixel/primr/company-research/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/blisspixel/primr/company-research"><img src="https://agentmods.dev/badge/skills/blisspixel/primr/company-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,055 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00037	$0.01055
Opus 5	$0.00018	$0.00528
Sonnet 5	$0.00007	$0.00211
Haiku 4.5	$0.00004	$0.00105

Measured 9d ago against content hash 8962afe760c7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

company-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/company-research/SKILL.md · 152 lines

How it starts

The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Company Research

Purpose

Use this skill to start and monitor Primr research runs through MCP. Treat MCP as the source of truth for current modes, defaults, and outputs.

Workflow

Read primr://research/modes to confirm the current mode guidance.
Read primr://research/status to see whether a run is already active.
Call estimate_run before starting a new run. Supports platforms (array), strategy_type, and no_ai_strategy parameters. Returns estimated_time_range alongside estimated_time_minutes.
Present the estimate, state that the run incurs real API cost, and get explicit approval before calling research_company.
After starting a run, monitor with wait_for_status_change or check_jobs until terminal.
Read primr://output/artifacts/by_job/{job_id} to inventory completed-job artifacts without report body content, then read primr://output/latest only when a report preview is needed to present the result and next actions. Read primr://output/qa_summary/by_job/{job_id} when QA artifacts are attached and the user needs QA outcome metadata. Read primr://output/usage_summary/by_job/{job_id} when the user needs cost, timing, approval, or artifact-count metadata. Read primr://output/source_summary/by_job/{job_id} when the user needs citation/source appendix metadata. Read primr://output/trace_summary/by_job/{job_id} when the user needs scrape trace health metadata. Read primr://output/verification_summary/by_job/{job_id} when the user needs claim verification trust score, claim counts, status counts, first-party downgrade counts, or source-reference counts without raw claims, source URLs, search queries, explanations, or report body content. Read primr://output/calibration_summary/by_job/{job_id} when the user needs label-calibration counts, inference source-copy counts, evidence-review count buckets, judge provenance, or judge-agreement metadata without raw claims, source URLs, evidence reviews, rationales, or report body content. MCP resource reads are audit-logged with hashed URI/result values and normalized resource kind, without raw URI query values or resource bodies. A2A skill calls are audit-logged with hashed message/result payloads, hashed caller ids, granted scopes, duration, outcome, and job id when present, without raw message text, task ids, URLs, report paths, raw results, or caller ids.

Read the full file on GitHub · 152 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

references/modes.md 731 B

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

9d ago First seen · 152 lines · 37 tokens per session scan A 8962afe760c7

Subscribe to this mod's changes

company-research is a skill published in the GitHub repository blisspixel/primr (3 stars, last pushed yesterday), licensed Apache-2.0. It adds 37 tokens to every session and 1,055 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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autosearch:citation-index

Deduplicate URLs across all sources, assign stable citation numbers, and merge citations from multiple subagents / sections into one consistent reference list. Prevents "same URL cited as [3] in one paragraph and [17] in another" and "different URLs merged under [5]" bugs that come from per-section synthesis.

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Session-level policy for keeping the runtime AI's context window healthy across long research — keep-last-k tool results, offload older evidence to disk, trigger compaction at thresholds. Borrows MiroThinker's keeptoolresult, deepagents' summarization middleware, and deer-flow's SummarizationEvent pattern. Orthogonal…

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Define the execution contract for isolating a research sub-task — input schema, budget, return summary, evidence list, failure status. Complements decompose-task (which only splits the problem) by giving each split a bounded, auditable execution unit the runtime AI can farm out to a sub-agent or parallel session.