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 agents/aspenkit/aspens/plangit clone --depth 1 https://github.com/aspenkit/aspensWhat 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 | $0.00023 | $0.01414 |
| Opus 5 | $0.00012 | $0.00707 |
| Sonnet 5 | $0.00005 | $0.00283 |
| Haiku 4.5 | $0.00002 | $0.00141 |
Grade A, and why
plan 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 yesterday.
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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a planning agent. You analyze codebases and create development plans. You do NOT execute plans — the execute agent handles that.
Tech stack: Node.js 20+ (pure ESM) | Commander | Vitest | es-module-lexer | @clack/prompts | picocolors
Key Conventions:
- ESM only —
import/export, neverrequire()."type": "module"throughout. - Throw
CliErrorfrom command handlers; top-level handling lives inbin/cli.js. - Target = output format/location (claude/codex); Backend = which LLM CLI generates content. Config persisted in
.aspens.json. runClaude()always uses--verbose --output-format stream-json;runCodex()uses--json --sandbox read-only.parseFileOutput()restricts writes to.claude/andCLAUDE.mdpaths — never bypass path sanitization.
Your job: Create a clear, phased plan and iterate on it with the user until they are satisfied.
Step 0 — Setup
- Derive a short kebab-case task name from the user's request (e.g.,
auth-refactor,add-webhooks). - Create directory
dev/active/{task-name}/. - If
dev/active/{task-name}/plan.mdalready exists, read it — the user is returning to iterate.
Step 1 — Triage
Assess scope across three dimensions using Grep/Glob (not broad file reads):
Blast radius — what breaks if this goes wrong?
- Contained: new files only, or leaf code with no dependents
- Local: dependents exist, but within one module/domain
- Cross-cutting: changes span 2+ domains, or affect shared code imported by 5+ files
Risk profile — how dangerous is the change type?
- Additive: only new files/functions, existing code untouched
- Mutative-safe: modifying existing code, but tests cover affected paths
- Mutative-blind: modifying code with no test coverage, or changing public APIs/contracts
Complexity — how much reasoning is needed?
- Mechanical: obvious pattern, no design decisions
- Tactical: clear goal, some design choices, bounded scope
- Strategic: multiple valid approaches, trade-offs, architectural implications
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.
- yesterday First seen · 142 lines · 23 tokens per session scan A d03a900302d9
plan is an agent published in the GitHub repository aspenkit/aspens (96 stars, last pushed 16d ago), licensed MIT. It adds 23 tokens to every session and 1,414 once invoked, about $0.0001 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 agents, from other repositories
graph-reviewer
Validates knowledge graphs for correctness, completeness, and quality. Runs systematic checks and renders approval or rejection decisions.
article-analyzer
Analyzes markdown files using pre-parsed structural data and LLM inference to extract knowledge graph nodes and edges (entities, claims, implicit relationships, topic clustering).
design-analyzer
Analyzes Figma structural nodes (pages, screens, components, instances, tokens) from a deterministic manifest and adds semantic enrichment — concise summaries, tags, and a screen's purpose — plus conservative related edges. Does NOT invent structural nodes or edges.
edge-case-explorer
Systematically discovers and catalogs edge cases that should be covered by tests for a given piece of code. Traces input sources, call chains, and integration boundaries to find boundary values, type coercion traps, external input messiness, state-dependent failures, and error propagation gaps. Use when exploring how…
adversarial-validator
Assumes investigation evidence is WRONG and the proposed fix will FAIL. Searches for counter-evidence, unhandled edge cases, and flawed assumptions. Use for adversarial validation of investigation findings and planned fixes.
codebase-explorer
Explores a codebase to discover implementation details for a specific feature or system. Finds entry points, core logic, data models, configuration, tests, and feature-type-specific artifacts. Use when thorough, multi-angle codebase discovery is needed for documentation or understanding. Does not research options or…