dearuser:collab

A collaboration-analysis skill from Dear User that produces a report about collaboration, including personas, scores, sources of friction, and recommendations.

In plain words
What is it for?
Use it to analyze collaboration globally or within a project, and request either text or more detailed technical output. The report can be shown as returned by the Dear User service.
Why use it?
It helps identify collaboration problems and turn them into suggested actions instead of relying only on informal impressions.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/bleedmode/dearuser/dearuser-collab
Any agent
npx skills add bleedmode/dearuser --skill dearuser-collab
Clone the repo
git clone --depth 1 https://github.com/bleedmode/dearuser

Made for: Claude Code, Codex.

Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 257 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00024 $0.00257
Opus 5 $0.00012 $0.00129
Sonnet 5 $0.00005 $0.00051
Haiku 4.5 $0.00002 $0.00026

Measured today against content hash b73027d6335f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dearuser:collab 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 today.

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.

mcp/skills/dearuser-collab/SKILL.md · 26 lines

What it actually says

Dear User — Collab

Run a collaboration analysis using the Dear User MCP server.

What to do

  1. Try calling mcp__dearuser__collab with default parameters (no arguments needed — global scope, text format).
  2. If the tool is not available (first turn of session — MCP tools load lazily), use this Bash fallback:
    npx -y -p @poisedhq/dearuser-mcp dearuser-run collab '{"format":"text"}' 2>/dev/null
    
  3. Output the ENTIRE returned report as your response text — do NOT summarize, shorten, or add commentary.
  4. After the report, offer to implement any recommendation marked "Actionable".

Rules

  • The report is pre-formatted markdown. Show it exactly as returned.
  • If the user asks for a project-specific analysis, add "scope":"project" to the args.
  • If the user asks for detailed/technical output, change format to "detailed".
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.

  1. today First seen · 26 lines · 24 tokens per session scan A b73027d6335f

Subscribe to this mod's changes

dearuser:collab is a skill published in the GitHub repository bleedmode/dearuser (0 stars, last pushed 25d ago), licensed MIT. It adds 24 tokens to every session and 257 once invoked, about $0.0001 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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