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 techygarg/lattice --skill requirement-qualitygit clone --depth 1 https://github.com/techygarg/latticeWrote 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/techygarg/lattice/requirement-quality)<a href="https://agentmods.dev/skills/techygarg/lattice/requirement-quality"><img src="https://agentmods.dev/badge/skills/techygarg/lattice/requirement-quality.svg" alt="Measured on agentmods" 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 Excessive Agency · line 35 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00106 | $0.01922 |
| Opus 5 | $0.00053 | $0.00961 |
| Sonnet 5 | $0.00021 | $0.00384 |
| Haiku 4.5 | $0.00011 | $0.00192 |
Grade A, and why
requirement-quality 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.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Requirement Quality
Config Resolution
Skill supports project-specific standards. Order:
- Look for
.lattice/config.yamlin the repo root - If found, check
paths.requirement_standardsfor a custom document path - If a custom document exists at that path, read it and check its YAML frontmatter for
mode:mode: override: the custom document has full precedence. Use it instead of the embedded defaults. It must be comprehensive — treat it as the sole reference.mode: overlay(or no mode field): read the embedded./references/defaults.mdfirst, then apply the custom document's sections on top. A custom section replaces the matching default section (matched by exact heading); new sections append after the defaults.
- If a custom path is configured but no document exists at it → tell the user which configured path is missing, then fall back to
./references/defaults.md - If there is no config file or no
paths.requirement_standardskey → read./references/defaults.md
Custom standards produced by requirement-forge-refiner → consumed by this atom → composed by requirement-forge molecule.
Self-Validation Checklist
STOP: Before writing any feature file, verify ALL checks. If a check clearly fails → fix before writing. If judgment call (see Ambiguity Signals) → flag and surface options.
If validating an existing spec (not generating), same checks apply — "fix before writing" means "fix before marking approved." Present findings as a quality report with severity.
Draft vs approved enforcement: For status: draft — items 1, 2, 10 are required. Items 3–9, 11, 12 are advisories: flag findings but do not block write. For status: approved — all items required, no exceptions.
- PROBLEM STATEMENT: Names a specific user need or pain — not a solution in disguise, not a vague improvement? Identifies WHO has the problem (specific user type or role, not "users")?
- SCOPE: Has explicit out-of-scope items — not just in-scope?
- BOUNDARY CONDITIONS: Feature-wide edge cases, system limits, and constraints documented?
- ASSUMPTIONS: Statements the team proceeds with as true are explicit — not buried in ACs or unstated? If an assumption proves wrong, affected scenarios are identifiable?
- SCENARIO NAMES: Each scenario has a verb-phrase name (sentence case) that describes the situation — not a feature name, not an AC?
- AC FORMAT: Each AC follows the agreed format (default: Given/When/Then)? Each has a clear pass/fail condition — a tester can write an automated check without asking a clarifying question?
- FAILURE COVERAGE: At least one scenario covers a failure, error, or edge case?
- SCENARIO COUNT: Feature has no more than the agreed max (default: 5) scenarios? If at or over → challenge whether this is one feature or two.
- AC COUNT: Each scenario has no more than the agreed max (default: 6) ACs? If at or over → challenge whether this scenario is too broad.
- INDEPENDENCE: Feature is self-contained — no unresolved external unknowns required before design-blueprint can begin? Any unresolved Open Questions affecting scope, behavior, or ACs are blockers unless each is marked non-blocking with a stated reason.
- IMPLEMENTATION NOTES: Slices ordered chronologically, at the "what" level — no technical implementation specifics?
- COHERENCE: All scenarios address the same user need in the Problem Statement? If a scenario serves a different need, it belongs in a separate feature.
What ships with it
1 file 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.
- 8d ago First seen · 80 lines · 106 tokens per session scan A ed07e16d0bb1
requirement-quality is a skill published in the GitHub repository techygarg/lattice (185 stars, last pushed today), licensed MIT. It adds 106 tokens to every session and 1,922 once invoked, about $0.0005 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.
Other skills, from other repositories
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run-smoke-tests
Inspect an unfamiliar repository, interpret a broad Markdown user journey at runtime, operate the real product through its supported web, API, CLI, desktop, or mobile surface, and produce an auditable pass, fail, or blocked judgment with screenshots, logs, recordings, and a step timeline when available. Use when asked…
code-standards
Apply a disciplined engineering workflow to any code change. Use whenever implementing a feature, fixing a bug, or refactoring — before writing code, not after. Walks orient → baseline → smallest change → test → verify → self-review, and enforces language-agnostic hard gates (don't mass-reformat, keep the linter and…
herdr-benchmark-pane
Run Skill Heaven benchmark probes and side-by-side posture comparisons in visible Herdr panes. Use whenever comparing postures (floor, product-floor, curated, native), doors (claude-zero, pi-zero, codex-zero), or harnesses, and the operator must be able to watch every arm run.
lab:autoresearch
Self-improving loop for plugin skills. Reads program.md, proposes one mutation per iteration, evaluates against deterministic scorer, keeps improvements via git, reverts failures. Targets weakest skill+dimension. Use with /loop for overnight runs.
verify
Verify Elixir/Phoenix changes — compile, format, and test in one loop. Use after implementation, before PRs, or after fixing bugs.