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 footprintjs/agentfootprint --skill claude-codegit clone --depth 1 https://github.com/footprintjs/agentfootprintWrote 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/footprintjs/agentfootprint/claude-code)<a href="https://agentmods.dev/skills/footprintjs/agentfootprint/claude-code"><img src="https://agentmods.dev/badge/skills/footprintjs/agentfootprint/claude-code/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/footprintjs/agentfootprint/claude-code"><img src="https://agentmods.dev/badge/skills/footprintjs/agentfootprint/claude-code.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00072 | $0.05509 |
| Opus 5.5 | $0.00029 | $0.02204 |
| Sonnet 5.5 | $0.00014 | $0.01102 |
| Haiku 4.5 | $0.00007 | $0.00551 |
Grade A, and why
agentfootprint 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 3d 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.
This is a copy
100% identical to agentfootprint — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agentfootprint — The Explainable Agent Framework
agentfootprint structures AI agents as composable flowcharts, so every injection, read, write, decision and tool call becomes connected evidence as the run happens. Every concept takes an LLMProvider — swap mock({...}) for anthropic({...}) with zero code changes.
Core principles:
- Adapter-swap testing ($0 test runs, deterministic assertions)
- The ladder:
mock→ollama(free, local, real model) → a paid provider - Declare context (facts, steering, skills); the framework decides WHEN it fires and WHICH slot it lands in
- Collect during traversal, never post-process (inherited from footprintjs)
npm install agentfootprint footprintjs
Read this first — what does NOT exist
These are not hypothetical. A capable author, working from a correct mental model of this library, invented all four in one document. Treat them as the things you are most likely to get wrong here.
| You will reach for | The reality |
|---|---|
startRun(...) |
No such function. The door is agent.run(input, options?), where AgentInput = { message: string; identity?; continueFrom? } and AgentOutput = string. run() returns AgentOutput | RunnerPauseOutcome — a run paused for a human returns a checkpoint; discriminate with isPaused(result). |
RunStep as skill/route history |
RunStep is real and it is something else — the footprintjs flowchart TOPOLOGY slider, exported from agentfootprint/observe. Its kind is 'sequential' | 'fork' | 'merge' | 'decide' | 'iteration' | 'iteration-exit' | 'react'. Nothing in it concerns skills. Importing it succeeds, which is exactly why it is dangerous. For route history use routeRecorder() from the same door. |
| the LLM classifier as routing "tier 3" | It is a tier-2 strategy. Tier 1 = declared start rules. Tier 2 = the configured scorer — llmClassifier(provider) OR keywordScorer() OR embeddingScorer(e) OR the entry scorer; near-ties fall through rather than argmax. Tier 3 = a menu the model resolves in-band through read_skill's own description, reached only when tier 2 was NOT decisive. |
| a skill's tools being gated to that skill automatically | They are not, by default. defineSkill({ tools }) puts them in the agent's static tool list at build time — visible from iteration 1 whether the skill ever activates or not. Ask for the gate: .toolsFromActiveSkill() (agent-wide), skillGraph({ scopeTools: true }) (graph-wide), or autoActivate: 'currentSkill' (per skill). .tree() leaves are the one shape scoped by default. |
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.
- 3d ago Changed b5837bae4cdb
- 7d ago Changed 0a3b298e0f83
- 8d ago Changed · -2 lines b29d576b1d40
- 12d ago Changed 70b31550dfde
- 18d ago Changed b6a179aa4613
- 20d ago Changed · +13 lines 8a00eb6b5516
- 21d ago Changed · +23 lines f21f4a850e85
- 23d ago Changed · +23 lines 8c3ef4d2696f
- 1mo ago Changed 466584929dfd
- 1mo ago First seen · 317 lines · 72 tokens per session scan A 87a21592e178
agentfootprint is a skill published in the GitHub repository footprintjs/agentfootprint (21 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 5,509 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agentfootprint, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
continuum-recipes
Copy-pasteable Continuum patterns — RAG, plan-and-execute, ReAct, multi-tenant agents, FastAPI integration, structured output, prompt-injection scanning, custom containers. Invoke when the user asks "how do I do X with Continuum" and X is a common app pattern rather than a single API question.
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ReasoningBank with AgentDB
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AgentDB Memory Patterns
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AgentDB Vector Search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
V3 Memory Unification
Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).