claude-code-my-workflow is a forkable setup for using Claude Code to produce and review academic papers, slides, data analyses, and replication packages. Researchers use its agents, skills, rules, hooks, and quality checks to coordinate these tasks and verify their results. The catalogue entries define the reusable workflow components for Claude Code.
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.
npx skills add pedrohcgs/claude-code-my-workflow --skill disclosure-checkgit clone --depth 1 https://github.com/pedrohcgs/claude-code-my-workflowWrote 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/skills/pedrohcgs/claude-code-my-workflow/disclosure-check)<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/disclosure-check"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/disclosure-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/skills/pedrohcgs/claude-code-my-workflow/disclosure-check"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/disclosure-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 131 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 132 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 132 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00147 | $0.02568 |
| Opus 5 | $0.00073 | $0.01284 |
| Sonnet 5 | $0.00029 | $0.00514 |
| Haiku 4.5 | $0.00015 | $0.00257 |
Grade A, and why
disclosure-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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/disclosure-check — Statistical-Disclosure-Limitation pre-screen
Scan analysis outputs built on restricted or confidential data (Census FSRDC, IRS SOI, administrative registers, linked health records, proprietary firm panels) for the disclosure-avoidance problems that get an export request rejected — before it reaches the data provider's official disclosure review. The skill is a pre-screen, not a substitute for that review.
Core principle: A single un-suppressed n=3 cell, an exact count that pins down one firm, or a p-percent dominance failure can re-identify a person or establishment. Catch it on your machine, not in the rejection email from the RDC analyst.
When to use
- Before requesting an export from a Census FSRDC / secure data enclave / RDC.
- Before depositing restricted-data results to openICPSR, a journal, or a co-author outside the enclave.
- Before sharing any figure, table, or log derived from confidential microdata.
- As a release gate. Pair with a pre-commit / pre-deposit invocation so no restricted-data output ships un-screened. This is the foundation of the data-management plan for any restricted-data project.
Inputs
$0— outputs directory to scan. Defaults toscripts/R/_outputs/. Recognised siblings:scripts/stata/_outputs/,scripts/python/_outputs/, or any export-staging directory (e.g., ato_review/folder the analyst stages for the RDC).--provider— selects which disclosure-rule profile to load (Phase 0). One ofcensus/irs/irb/generic. Providers differ — thresholds and rules are not interchangeable; defaultgenericis deliberately conservative.--threshold N— override the minimum cell count (defaultn<10). Census FSRDC commonly uses 10 for establishments; IRS and many IRBs differ. Always reconcile with your provider's written rules.
Workflow
Phase 0: Load the provider's disclosure rules
- Read
.claude/rules/confidential-data.mdfor the project's restricted-data handling contract and the rule-profile placeholder. - Load the
--providerprofile (a placeholder config the forker fills in from their signed agreement — Census, IRS, and IRB rules differ and supersede any default here):- min cell count (default
n<10), - dominance rules:
p-percent (a cell is unsafe if the largest respondents contribute >p% of the total) and(n,k)(topnunits >k% of total), - rounding required for sensitive statistics (counts, totals, ratios),
- top-coding / bottom-coding thresholds for extreme values,
- geographic minimum population for any geocoded statistic.
- min cell count (default
- If no signed-rule values are recorded, fall back to the conservative
genericprofile and flag prominently in the report that real provider thresholds must be substituted.
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.
- 10d ago First seen · 141 lines · 147 tokens per session scan A 9e2ea3484792
disclosure-check is a skill published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,569 stars, last pushed 16d ago), licensed MIT. It adds 147 tokens to every session and 2,568 once invoked, about $0.0007 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 2026-08-30.
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