Borrowing it
Nothing to install: this file belongs to davidmatousek/tachi. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/davidmatousek/tachi/main/.claude/skills/tachi-control-analysis/SKILL.mdgit clone --depth 1 https://github.com/davidmatousek/tachiWrote 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/davidmatousek/tachi/tachi-control-analysis)<a href="https://agentmods.dev/skills/davidmatousek/tachi/tachi-control-analysis"><img src="https://agentmods.dev/badge/skills/davidmatousek/tachi/tachi-control-analysis/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/davidmatousek/tachi/tachi-control-analysis"><img src="https://agentmods.dev/badge/skills/davidmatousek/tachi/tachi-control-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 22 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 | $0.00052 | $0.01216 |
| Opus 5 | $0.00026 | $0.00608 |
| Sonnet 5 | $0.00010 | $0.00243 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade A, and why
tachi-control-analysis 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 9d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tachi Control Analysis Skill
This skill contains the domain knowledge extracted from the tachi control-analyzer agent. It provides the reference data needed to detect compensating controls in a target codebase, classify their effectiveness, calculate residual risk, and generate remediation recommendations.
Domain Overview
The control analysis domain covers three areas:
-
Control Categories and Detection Patterns -- Definitions for the 8 compensating control categories (authentication, input-validation, rate-limiting, encryption, logging-audit, csrf-protection, csp-security-headers, access-control), their STRIDE-to-control mapping, pattern indicators for Phase A scanning, and common library/framework references.
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Evidence Criteria and Effectiveness Classification -- Phase B semantic analysis criteria (context checks, enforcement checks, strength assessments), evidence collection rules (snippet selection, deduplication, file path format), confidence level definitions (High/Medium/Low), and Phase 4 classification rules (found/partial/missing with multi-control resolution and cross-component handling).
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Residual Risk Calculation and Recommendations -- Recommendation generation rules for missing and partial controls (templates, effort calibration), residual score computation formula with the P0 binary reduction model (reduction factors by control status), severity band mapping for residual scores, and summary statistics calculations.
NIST AI RMF Relationship
Tachi's compensating-controls analyzer operates in a STRIDE+AI idiom, not a NIST idiom. The NIST AI Risk Management Framework (AI RMF 1.0, NIST AI 100-1) and its companion Generative AI Profile (NIST AI 600-1) are the U.S. federal reference vocabulary for managing AI system risk — Functions (Govern, Map, Measure, Manage), Subcategories, and 12 GAI risk categories. Tachi's posture toward both documents is documentation-only mapping per ADR-025: no schema field, no agent, no pipeline phase, and no SARIF tag emits NIST-keyed output. The strongest direct semantic overlap is MEASURE 2.7 ("AI system security and resilience are evaluated and documented") — essentially what compensating-controls.md already produces without NIST labeling. Adopters who must cite NIST mappings during procurement, audit, or examination workflows should consult the companion reference .claude/skills/tachi-shared/references/nist-ai-rmf-mapping.md and ADR-025 for the three-surface comparison (Functions × phases, Subcategories × control categories, GAI risks × STRIDE+AI), the full re-evaluation triggers, and the structural-fit rationale for choosing mapping over wired integration.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 79 lines · 52 tokens per session scan A 5f930ed65587
tachi-control-analysis is a skill published in the GitHub repository davidmatousek/tachi (91 stars, last pushed 26d ago), licensed Apache-2.0. It adds 52 tokens to every session and 1,216 once invoked, about $0.0003 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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