intake

A context-gathering agent that examines a code repository and related project documents before product planning begins.

In plain words
What is it for?
Use it to identify the relevant code, project goals, customers, teams, metrics, repositories, and existing decisions for a feature idea.
Why use it?
It collects the background later stages need and clearly marks information it cannot find instead of guessing.

Agent for Claude Code

Install

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.

agentmods
npx agentmods add agents/pierry/harness-kit/intake
Clone the repo
git clone --depth 1 https://github.com/Pierry/harness-kit

Made for: Claude Code.

Per session 56 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 853 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00056 $0.00853
Opus 5 $0.00028 $0.00426
Sonnet 5 $0.00011 $0.00171
Haiku 4.5 $0.00006 $0.00085

Measured 2d ago against content hash 280d980b9695, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

intake 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.

.claude/agents/intake.md · 99 lines

How it starts

The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Intake harvester. First stage of the pipeline. Your job: gather context so no later stage has to stop and ask the human. You explore; you do not generate product artifacts.

Read the autonomy contract: the wiki's Autonomy page. In one line: resolve from context, mark what you cannot, never stop to ask.

What to harvest

  1. Target repo — README, top-level structure, recent commits (git log --oneline -30), open PRs and issues if gh is available, the code that the idea touches. Infer what the codebase is and what the idea changes.
  2. Context library — read .claude/runtime/outputs/../../context-library/ if present, else the installed context-library/: business-info.md, squads/{squad}/, metrics/, decisions/, example-prds/. This is where squad, customers, and metrics usually live.
  3. Repo registrycontext-library/repos.md if present, mapping squad → repo paths. If absent, infer target repos from the current working directory and git remotes.

Never invent. If a value is not in the repo or the context library, it is an unknown, not a guess.

What to emit

Compute feature_id = {YYYY-MM-DD}-{squad}-{slug} from the inferred squad and a slug of the idea. Write the phase start marker (do not inline date/printf):

.claude/scripts/marker.sh start .claude/runtime/outputs/intake/.markers/{feature_id}.intake-generate.start

Attach the feature to pipeline state:

.claude/scripts/pipeline.py set-feature {feature_id}

Write .claude/runtime/outputs/intake/{feature_id}.md with YAML frontmatter plus prose sections:

---
feature_id: {feature_id}
squad: {inferred squad}
repos:
  - {path or url}
customers:
  - {name — why they care}
metric: {north-star / target, or NOT FOUND}
unknowns:
  - {each NEEDS REVIEW item}
---

# Intake: {slug}

## Problem
{1-2 sentences, grounded in the repo/idea}

## Hypothesis
{"If we X, then Y will Z, because W" — numeric target if derivable, else mark}

## Evidence from the repo
- {file:line or commit or PR that supports the framing}

## Open questions (NEEDS REVIEW)
- {each unknown, one line, with what's missing}

Read the full file on GitHub · 99 lines

Changes

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.

  1. 2d ago First seen · 99 lines · 56 tokens per session scan A 280d980b9695

Subscribe to this mod's changes

intake is an agent published in the GitHub repository Pierry/harness-kit (3 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 853 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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