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/ghosteken/agent-harness/feature-docgit clone --depth 1 https://github.com/Ghosteken/agent-harnessWhat 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 | $0.00163 |
| Opus 5 | $0.00012 | $0.00081 |
| Sonnet 5 | $0.00005 | $0.00033 |
| Haiku 4.5 | $0.00002 | $0.00016 |
Grade A, and why
feature-doc 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.
What it actually says
Invoke the agent-harness:feature-doc skill.
Identify the feature to document, then locate and read the project's own technical/product docs before asking anything else. Interview the user to resolve every material unknown the docs don't already answer — actors, triggers, preconditions, data sources, edge cases — never assume or default anything feature-specific.
Produce three cross-referenced artifacts under docs/features/<feature-slug>/<actor-slug>/: feature-spec.md, implementation-guide.md, and test-cases.md, and update the index at docs/features/README.md.
State clearly that this output is a planning artifact — implementing the feature is a separate, later step.
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 · 12 lines · 23 tokens per session scan A 71ed6d3c652e
feature-doc is a command published in the GitHub repository Ghosteken/agent-harness (2 stars, last pushed 17d ago), licensed MIT. It adds 23 tokens to every session and 163 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 2026-08-31.
Other commands, from other repositories
build
Run full verification pipeline.
insights
Surface patterns from your pro-workflow learnings and session history.
blackboard
Read or write a Network-AI blackboard key (shared multi-agent state).
cost-tracker
Track session costs, understand token spend, and get optimization tips.
dispatcher
Pick the next-best repo to work on across the portfolio — rank free repos, recommend one, claim its lease atomically, and route to the entry command.
triage-issues
Launch the Issue Triage Agent (Haiku) to categorize and prioritize GitHub issues.