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/identification-reviewnpx skills add matthewdigiuseppe/MStack --skill identification-reviewgit clone --depth 1 https://github.com/matthewdigiuseppe/MStackWrote 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/matthewdigiuseppe/mstack/identification-review)<a href="https://agentmods.dev/skills/matthewdigiuseppe/mstack/identification-review"><img src="https://agentmods.dev/badge/skills/matthewdigiuseppe/mstack/identification-review.svg" alt="Measured on agentmods" 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 | $0.00074 | $0.00966 |
| Opus 5 | $0.00037 | $0.00483 |
| Sonnet 5 | $0.00015 | $0.00193 |
| Haiku 4.5 | $0.00007 | $0.00097 |
Grade A, and why
identification-review 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 5d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/mstack:identification-review
Stage: map (before design lock-in) — re-run before submission Voice: methodologist
When to invoke
- After
/mstack:theory-buildand before/mstack:design-research: lock down identification before committing to a design. - Before submission: re-run on the actual specification to catch what you missed.
Procedure
-
Load context.
.mstack/research-question.mdfor the claim..mstack/lit-map.mdfor what the literature already disputes.paper/sections/methods.tex(if it exists) for the current spec.code/02-analyze.R(if it exists) for what is actually run.
-
State the identifying assumption in one sentence. If the user can't, the review fails before it starts; surface that as the top finding.
-
Run the prosecution checklist. For each item, name a concrete violation a reviewer could plausibly raise:
- Selection. Who is in the sample? Who isn't? Is selection on the dependent variable?
- Confounding. What is the most plausible omitted variable? Why does the design rule it out?
- Reverse causality. Could Y cause X? What evidence rules it out?
- Measurement. Is X measured pre-treatment? Is Y measured cleanly? What is the reliability?
- SUTVA / spillovers. Are units independent? If not, what is the dependence structure?
- Effective sample. What units actually contribute to identification (e.g., within-variation under FE)? Are they representative?
- Standard errors. What is the level of clustering? What is the dependence structure that justifies it?
- Multiple comparisons. How many tests? What is the family-wise error rate?
- Specification curve. How many reasonable specifications exist? Have they been run? Where does the headline sit?
- External validity. What is the population of generalization? Is the headline phrased to match?
-
Falsification tests. Name at least two:
- Placebo. A sample / time / outcome where the effect should be zero. Is it?
- Pre-trend / pre-treatment outcome. Does the relationship exist before treatment?
- Negative control. A predictor that should not predict the outcome — does it?
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.
- 5d ago First seen · 74 lines · 74 tokens per session scan A 392d4c1803f5
identification-review is a skill published in the GitHub repository matthewdigiuseppe/MStack (14 stars, last pushed 9d ago), licensed MIT. It adds 74 tokens to every session and 966 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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