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 agentmods add skills/matthewdigiuseppe/mstack/data-cleannpx skills add matthewdigiuseppe/MStack --skill data-cleangit clone --depth 1 https://github.com/matthewdigiuseppe/MStackWhat 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 | $0.00073 | $0.01004 |
| Opus 5 | $0.00036 | $0.00502 |
| Sonnet 5 | $0.00015 | $0.00201 |
| Haiku 4.5 | $0.00007 | $0.00100 |
Grade A, and why
data-clean scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/mstack:data-clean
Stage: build
Voice: data-engineer (anchored to r-coding-skills)
When to invoke
After /mstack:data-acquire has populated data/raw/ and the provenance log. Run before any analysis. Re-run when the analytic dataset changes.
Procedure
-
Read the provenance log at
data/raw/PROVENANCE.md(or whatever/mstack:data-acquirewrote). If missing, stop and tell the user to run/mstack:data-acquirefirst — undocumented raw data is not cleanable. -
Apply R conventions. Use the
r-coding-skillsskill if the user has it installed; otherwise follow${CLAUDE_PLUGIN_ROOT}/references/r-conventions.md. Either way: tidyverse style,here::here()paths, snake_case, nosetwd(), package versioning notes. -
Plan the pipeline. Before writing code, list (in chat) the cleaning steps:
- Reads (which raw files).
- Joins (key, type, expected row count).
- Recodes (variable, mapping).
- Drops (rule, expected row count, justification).
- Derived variables (formula).
- Final analytic unit (e.g., country-year, individual-wave).
- Output file(s).
Get user confirmation on the plan before writing code. If
.mstack/learnings.jsonlalready specifies conventions (variable names, exclusions), apply them automatically and note which. -
Write
code/01-clean.R. Conventions:- Header comment block: purpose, inputs, outputs, run order.
library()calls grouped at top.- Each step in its own block, preceded by a one-line comment.
- Every drop and join logged with
nrow()before/after and a stop-if-unexpected check (e.g.,stopifnot(nrow(df) == expected)). - Save final dataset(s) to
data/clean/as.rds(preferred) plus a.csvmirror for portability. - Save a session-info dump to
data/clean/session-info.txtfor replication.
-
Run the script with
Rscript code/01-clean.R. Capture stdout/stderr. If it errors, fix and re-run; do not declare done with errors outstanding. -
Sanity checks on the cleaned dataset:
- Row counts match the plan.
- No fully-missing columns.
- Key variables in expected ranges.
- Unit of analysis is unique (
stopifnot(!anyDuplicated(df[, key_cols]))). - Print a
summary()and a head/tail snapshot to a log file atdata/clean/clean-log.md.
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.
- 2d ago First seen · 77 lines · 73 tokens per session scan A 111f5e25e401
data-clean is a skill published in the GitHub repository matthewdigiuseppe/MStack (14 stars, last pushed 6d ago), licensed MIT. It adds 73 tokens to every session and 1,004 once invoked, about $0.0004 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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