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/tercel/spec-forge/analyzenpx skills add tercel/spec-forge --skill analyzegit clone --depth 1 https://github.com/tercel/spec-forgeWhat 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 | $0.00069 | $0.03497 |
| Opus 5 | $0.00034 | $0.01749 |
| Sonnet 5 | $0.00014 | $0.00699 |
| Haiku 4.5 | $0.00007 | $0.00350 |
Grade A, and why
analyze 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 2d 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 — 411 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze — Document Landscape Analysis & Knowledge Mapping
Map, understand, and evaluate a collection of documents as a body of knowledge. Identify structure, themes, gaps, redundancies, conflicts, and staleness. Produce an actionable analysis with reorganization recommendations.
Core Principles
- Landscape-first: Build the map before judging — understand what exists before critiquing
- Theme-driven: Group documents by what they're about, not just where they live
- Conflict detection: Find contradictions between documents that may confuse readers
- Staleness awareness: Identify documents that may be outdated based on content signals
- Non-destructive: Only reads and reports — never modifies source documents
- Reorganization as suggestion: Propose better structures, don't impose them
When to Use Analyze vs. Audit
| Situation | Use |
|---|---|
| Single project with docs/ and source code | /spec-forge:audit |
| Docs-only repo with mixed content | /spec-forge:analyze |
| Cross-repo documentation ecosystem | /spec-forge:analyze |
| Research notes, ideas, decision records | /spec-forge:analyze |
| API docs need checking against code | /spec-forge:audit |
| "I have a mess of docs and need to understand them" | /spec-forge:analyze |
Workflow
Step 1: Determine Scope
Parse the arguments to determine what to analyze:
- If a path argument is provided (e.g.,
/spec-forge:analyze ../../aipartnerup-docs), use that as the root - If no path, use the current working directory's
docs/directory - If multiple paths are provided, analyze all of them as one collection
Use AskUserQuestion to understand the user's goals:
- What is this collection? (e.g., "ecosystem docs for multiple products", "research notes", "mixed specs and decisions")
- What do you want to understand? Options:
- Full landscape analysis (recommended for first time)
- Find conflicts and contradictions
- Find gaps and missing coverage
- Suggest reorganization
- All of the above
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.
- 2d ago First seen · 411 lines · 69 tokens per session scan A fed1f952d16d
analyze is a skill published in the GitHub repository tercel/spec-forge (5 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 3,497 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 2026-08-31.
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