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.
git clone --depth 1 https://github.com/gonzalezpazmonica/pm-workspaceWrote 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.
[](https://agentmods.dev/commands/gonzalezpazmonica/pm-workspace/bias-check)<a href="https://agentmods.dev/commands/gonzalezpazmonica/pm-workspace/bias-check"><img src="https://agentmods.dev/badge/commands/gonzalezpazmonica/pm-workspace/bias-check/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.
<a href="https://agentmods.dev/commands/gonzalezpazmonica/pm-workspace/bias-check"><img src="https://agentmods.dev/badge/commands/gonzalezpazmonica/pm-workspace/bias-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00020 | $0.00845 |
| Opus 5 | $0.00010 | $0.00423 |
| Sonnet 5 | $0.00004 | $0.00169 |
| Haiku 4.5 | $0.00002 | $0.00085 |
Grade A, and why
bias-check 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 8d 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.
This is a copy
100% identical to bias-check — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/bias-check Command
Description: Runs a counterfactual audit on sprint assignments and communications to detect biases in project management across demographic groups.
Usage
/bias:check --project <name> [--sprint <sprint-id>]
Parameters
--project <name>: Required. Project name (e.g., pm-workspace)--sprint <sprint-id>: Optional. Specific sprint ID to audit (default: current sprint)
Process
1. Assignment Audit
- Load
equipo.mdto identify team demographics - Load sprint assignments from project metadata
- Apply counterfactual test: reassign tasks with names/demographics removed
- Calculate task-type distribution per person (features vs. bugs vs. maintenance)
- Identify segregation patterns: concentration of high-visibility vs. invisible work
2. Tone Audit
- Review all generated communications (standup updates, feedback, announcements)
- Verify uniform tone across team:
- Equal action verbs (designed, led, implemented, solved)
- Equal enthusiasm for achievements
- Equal practical solutions offered for obstacles
- Absence of paternalistic language
- Flag differential framing patterns
3. Metrics Audit
- Verify performance metrics use identical criteria for all team members
- Detect systematically softer/harsher evaluations
- Check for double standards in effort assessment
- Identify demographic-correlated evaluation variance
4. Output Generation
- Generate formatted report with distribution tables
- Include counterfactual analysis results
- Present tone analysis with examples
- Provide actionable recommendations
Example Output Format
╔════════════════════════════════════════════════════════════╗
║ BIAS AUDIT REPORT: pm-workspace Sprint 12 ║
║ Generated: 2026-03-04 ║
╚════════════════════════════════════════════════════════════╝
ASSIGNMENT DISTRIBUTION
┌─────────────────┬───────┬──────┬─────────────┬──────────┐
│ Team Member │ Total │ Bugs │ Features │ Maintain │
├─────────────────┼───────┼──────┼─────────────┼──────────┤
│ Alex (she/her) │ 8 │ 2 │ 4 │ 2 │
│ Jordan (he/him) │ 8 │ 6 │ 1 │ 1 │
│ Sam (they/them) │ 7 │ 3 │ 3 │ 1 │
└─────────────────┴───────┴──────┴─────────────┴──────────┘
COUNTERFACTUAL TEST RESULT
Bias likelihood: MODERATE
Reassigning with blinded demographics shows 73% likelihood
of identical distribution. Suggests systemic assignment pattern.
TONE ANALYSIS
⚠ FINDING: Communications to Jordan use 2.3x more
achievement-focused verbs ("pioneered", "orchestrated")
vs. Alex (routine verbs: "completed", "handled")
RECOMMENDATIONS
1. Implement blind task assignment review
2. Audit standup language for demographic patterns
3. Establish peer review for performance assessments
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.
- 8d ago First seen · 107 lines · 20 tokens per session scan A d78c52a3ec8b
bias-check is a command published in the GitHub repository gonzalezpazmonica/pm-workspace (49 stars, last pushed 6d ago), licensed MIT. It adds 20 tokens to every session and 845 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to bias-check, differing in 0 lines, and is treated as a copy.
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