forage

A workflow for finding useful open-source repositories. It supports targeted searches for a specific need and broader searches when you are exploring without a fixed goal.

In plain words
What is it for?
Use it to discover candidate repositories, search from several angles at once, compare results, and choose projects worth closer inspection.
Why use it?
It narrows a noisy search into a ranked, duplicate-free shortlist, reducing the time spent evaluating irrelevant or abandoned repositories.

Skill for Claude CodeCodex

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 skills/iamk77/skill/forage
Any agent
npx skills add IamK77/Skill --skill forage
Clone the repo
git clone --depth 1 https://github.com/IamK77/Skill

Made for: Claude Code, Codex.

Per session 231 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,187 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 $0.00231 $0.05187
Opus 5 $0.00115 $0.02593
Sonnet 5 $0.00046 $0.01037
Haiku 4.5 $0.00023 $0.00519

Measured 2d ago against content hash c997fa89f43a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

forage 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.

skills/quarry/forage/SKILL.md · 156 lines

How it starts

The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.

forage

!checklist init ${CLAUDE_SKILL_DIR} --force

A useful repository is a vein of value in a commons that, from the trending page, looks like nothing but noise and abandoned star-farms. forage is the lens you hold over the open-source world to find that vein before you sink an afternoon into the wrong one — whether you arrive with a sharp need or just wander in to look around. It is the first skill of the quarry suite: it owns discovery — bringing back the few candidates worth a closer look. It audits (and guides you to run) a gated pipeline, and it will not advance past a GATE until the checklist tool clears it. That gate enforces order — each step done before the next — not the substance of the work inside it; the tool structures the discipline, it does not supply it, so the judgment is yours.

The one mental shift everything hangs on — the search syntax is no longer the skill. The human-era craft of finding repos was fluency: memorize the qualifiers, learn which awesome-list to grep, build the muscle for language:python stars:>1000 pushed:>2026-01-01. The agent already knows every qualifier and can run forty queries while you type one — so that fluency is commoditized, and clinging to it is optimizing the part the machine took over. What is left for you is the part it can't do: in targeted mode, saying precisely what you need (the sharper the spec, the better everything downstream — "a task queue" finds noise; "a lightweight Celery alternative with no broker dependency, still maintained in 2026" finds the answer); and in serendipitous mode, the taste to feel which find is worth a second look. The agent's job is to fan the seams out wide; yours is to aim them and to judge what comes back.

The agent is the means, not the judge. Here it has two jobs: the parallel miner (run every seam at once, targeted) and the wanderer (range the high-signal surfaces, serendipitous). It is not the arbiter of what's good. Its gradient points at handing you something that looks like an answer, and in this domain that gradient is dangerous in three specific ways: it trusts the most gameable signals — a big star count and a polished README — exactly the two an abandoned project or an AI-generated star-farm wears best; it invents repositories, or asserts that a real one "supports X" without ever opening it; and it flattens everything to "relevant", returning ten more of what you already know when serendipity needed the one orthogonal thing. So two disciplines hold from the first query: every candidate carries its provenance — which seam found it, which query — and no repo enters the list that the agent did not actually pull from a source; and stars and the README are treated as claims to be corroborated later by touchstone, never as proof of quality here.

What you cannot delegate — two judgments. The pipeline parallelizes the labor, but two points are yours and stay yours: (1) the need-spec (targeted) or the adjacency dial (serendipitous) — what you are hunting, and how far from your usual ground to roam, decides what can even be found; hand the agent a vague need and you have automated a thorough search of the wrong commons. And (2) the "worth a look" verdict — the 眼前一亮, the this one's interesting. The agent can rank by stars and recency; it cannot feel the pull that makes a project worth your weekend. Outsource that and you have a tidy list nobody actually wanted to open.

What "done" looks like — a captured shortlist, not an open tab graveyard. Foraging is over when you hold a small, categorized shortlist — each survivor with its provenance and a one-line reason it's there — recorded somewhere durable (a star-list, a notes file), not twenty browser tabs you'll close unread. Targeted foraging ends with a handful of genuine candidates framed for touchstone. Serendipitous foraging ends with the surprises captured cheaply into an "interesting, not sure why yet" list — because the value of a wander rarely lands the day you find it, but three months later when a problem arrives and you think "wait, I starred something for this." The terminus is the captured shortlist, not the number of repos you skimmed.

Read the full file on GitHub · 156 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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 · 156 lines · 231 tokens per session scan A c997fa89f43a

Subscribe to this mod's changes

forage is a skill published in the GitHub repository IamK77/Skill (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 231 tokens to every session and 5,187 once invoked, about $0.0012 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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