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 commands/dormstern/forge/scoutgit clone --depth 1 https://github.com/dormstern/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 | $0.00043 | $0.00480 |
| Opus 5 | $0.00022 | $0.00240 |
| Sonnet 5 | $0.00009 | $0.00096 |
| Haiku 4.5 | $0.00004 | $0.00048 |
Grade A, and why
scout 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.
What it actually says
/forge:scout — Market scout (Founder mode)
Execution mode: direct. This command has explicit steps below — execute them in order. Do not enter plan mode, do not write a plan file, do not call ExitPlanMode. The framework already did the planning; you just run the steps.
You dispatch the market-scout agent to discover signals across communities.
Pre-conditions
THESIS.mdandhypotheses.jsonexist in the project. If not, instruct the user to run/forge:init --founderfirst.
Behavior
-
Read
THESIS.md(current sections + confidence scores),hypotheses.json(active hypotheses),landscape.md(last cycle's competitors),evidence.json(existing evidence), andprogress.md(Dead Ends + Cross-Cutting Patterns). -
Dispatch the market-scout agent with the active hypotheses and a list of 5–7 communities to scan in parallel. The agent uses the 7-signal framework (pain, frequency, severity, willingness-to-pay, competitor, trend, contradictory) and returns a structured SCOUT Report.
-
Append signals to
landscape.mdandevidence.json. Every signal must include source URL, date, verbatim quote, and signal type. Contradictory signals are mandatory — if there are none, the scout pass was incomplete. -
PAUSE — Present the SCOUT Report to the user. Highlight: HIGH-strength signals, contradictory evidence, new competitors, hypotheses that should be created or weakened.
Constraints
- Never paraphrase community quotes — verbatim only.
- Use bottom-up TAM only. No analyst reports.
- New hypotheses require ≥2 independent signals.
- If the user has only stated-intent evidence (weight 1), flag the gap.
Reference
- Agent: agents/market-scout.md
- Method: skills/community-mining/SKILL.md
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 · 37 lines · 43 tokens per session scan A 9bd1163f1e88
scout is a command published in the GitHub repository dormstern/forge (6 stars, last pushed 3mo ago), licensed MIT. It adds 43 tokens to every session and 480 once invoked, about $0.0002 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.