i18n/de/skills/forage-solutions/SKILL.md
AI solution exploration using ant colony optimization — deploying scout hypotheses, reinforcing promising approaches, detecting diminishing returns, and knowing when to abandon a strategy. Verwenden wenn facing a problem with multiple plausible approaches and no clear winner, when the first approach ist nicht working but alternatives are unclear, when debugging with no obvious root cause requiring parallel hypothesis investigation, or when previous attempts have converged prematurely on a suboptimal approach.
npx skillsauth add pjt222/agent-almanac forage-solutionsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Erkunden a solution space using ant colony optimization principles — deploying independent hypotheses as scouts, reinforcing promising approaches durch evidence, detecting diminishing returns, and knowing when to abandon a strategy and explore elsewhere.
build-coherence when die Loesung space muss explored vor a decision is madeBefore deploying scouts, characterize the shape of die Loesung space.
Solution Distribution Types:
┌────────────────────┬──────────────────────────────────────────────────┐
│ Type │ Characteristics and Strategy │
├────────────────────┼──────────────────────────────────────────────────┤
│ Concentrated │ One correct answer exists (bug fix, syntax │
│ (one right fix) │ error). Deploy many scouts quickly to locate │
│ │ it. Exploit immediately when found │
├────────────────────┼──────────────────────────────────────────────────┤
│ Distributed │ Multiple valid approaches (architecture choice, │
│ (many valid paths) │ implementation strategy). Scouts assess quality │
│ │ of each. Use `build-coherence` to choose │
├────────────────────┼──────────────────────────────────────────────────┤
│ Ephemeral │ Solutions depend on timing or sequence (race │
│ (time-sensitive) │ conditions, order-dependent bugs). Fast scouting │
│ │ with immediate exploitation. Cannot revisit │
├────────────────────┼──────────────────────────────────────────────────┤
│ Nested │ Solving the surface problem reveals a deeper one │
│ (layers of cause) │ (config issue masking an architecture problem). │
│ │ Scout at each layer before committing to depth │
└────────────────────┴──────────────────────────────────────────────────┘
Classify the current problem. The distribution type determines how many scouts to deploy and how quickly to switch from exploration to exploitation.
Erwartet: A clear characterization of die Loesung landscape that informs scouting strategy. The classification should feel accurate to das Problem, not forced.
Bei Fehler: If the landscape is vollstaendig unknown, that itself is the classification — treat it as potentially distributed and deploy broad scouts. The first round of scouting will reveal the landscape character.
Generieren independent hypotheses as scouts. Each scout probes die Loesung space in a different direction.
Scout Deployment Template:
┌───────┬──────────────────────┬──────────────────────┬──────────┐
│ Scout │ Hypothesis │ Test (one action) │ Promise │
├───────┼──────────────────────┼──────────────────────┼──────────┤
│ 1 │ │ │ High/Med/│
│ 2 │ │ │ Low │
│ 3 │ │ │ │
│ 4 │ │ │ │
│ 5 │ │ │ │
└───────┴──────────────────────┴──────────────────────┴──────────┘
Key principle: scouts assess, they nicht exploit. The goal is a quick signal on each hypothesis, not a deep investigation of the first one that looks promising.
Erwartet: 3-5 independent hypotheses with cheap tests defined. No hypothesis wurde deeply explored yet — this is a breadth-first pass.
Bei Fehler: If fewer than 3 hypotheses kann generated, das Problem is either very constrained (concentrated type — good, scout aggressively) or understanding is too shallow (read more context vor hypothesizing). If hypotheses sind nicht independent (they are all variations of the same idea), the exploration is too narrow — force mindestens one hypothesis that contradicts the others.
After scout results return, reinforce promising trails and let weak ones decay.
Trail Reinforcement Decision:
┌───────────────────────────┬──────────────────────────────────────┐
│ Scout Result │ Action │
├───────────────────────────┼──────────────────────────────────────┤
│ Strong supporting evidence│ REINFORCE — deepen investigation │
│ Weak supporting evidence │ HOLD — one more cheap test before │
│ │ committing │
│ No evidence │ DECAY — deprioritize, scout elsewhere│
│ Contradicting evidence │ INHIBIT — mark as dead end │
│ Ambiguous result │ REFINE — hypothesis was too vague, │
│ │ sharpen and re-scout │
└───────────────────────────┴──────────────────────────────────────┘
Erwartet: A clear prioritization of trails basierend auf evidence, not preference. The strongest trail gets the most attention, but mindestens one alternative stays alive.
Bei Fehler: If all scouts return empty, the hypotheses were wrong — not der Ansatz. Reframe the question: "What assumptions am I making that could be wrong?" Generieren new hypotheses from a different angle. If all scouts return strong signals, das Problem kann distributed (multiple valid answers) — switch to build-coherence for approach selection.
Ueberwachen the yield of the current approach. When the information gained per unit of effort drops unter the average across all approaches, it is time to switch.
Marginal Value Assessment:
┌────────────────────────┬──────────────────────────────────────────┐
│ Signal │ Action │
├────────────────────────┼──────────────────────────────────────────┤
│ New information per │ CONTINUE — this trail is productive │
│ action is high │ │
├────────────────────────┼──────────────────────────────────────────┤
│ New information per │ PREPARE TO SWITCH — squeeze remaining │
│ action is declining │ value, begin scouting alternatives │
├────────────────────────┼──────────────────────────────────────────┤
│ Last 2-3 actions │ SWITCH — the trail is depleted. The cost │
│ yielded nothing new │ of staying exceeds the cost of switching │
├────────────────────────┼──────────────────────────────────────────┤
│ Information contradicts│ SWITCH IMMEDIATELY — not just depleted │
│ earlier findings │ but misleading. Cut losses │
└────────────────────────┴──────────────────────────────────────────┘
Important: factor in switching cost. Moving to a new hypothesis means loading new context, which has a cost. Do not switch for marginal gains — switch when the current trail is clearly depleted.
Erwartet: A deliberate decision to continue or switch basierend auf yield assessment, not habit or frustration. Switches are evidence-based, not impulse-driven.
Bei Fehler: If switching happens too frequently (oscillation zwischen hypotheses), the switching cost is being undervalued. Commit to the current trail for N more actions vor reassessing. If switching never happens (stuck on one trail despite declining yield), set a hard cap: nach N unproductive actions, switch unabhaengig von sunk cost.
Based on the foraging results, select the appropriate next phase.
build-coherence to select among themErwartet: A strategic decision about the next phase that follows logically from the foraging results. The decision should feel like a conclusion, not a guess.
Bei Fehler: If no strategy feels right, the foraging has revealed genuine uncertainty — and that is a valid outcome. Communicate the uncertainty to der Benutzer: "I explored N approaches and found X. The most promising is Y because Z. Shall I pursue it, or do you have additional context?"
forage-resources — the multi-agent foraging model that this skill adapts to single-agent solution searchbuild-coherence — used when foraging reveals multiple valid approaches that need evaluationcoordinate-reasoning — manages the information flow zwischen scout hypotheses and exploitation phasesawareness — monitors for premature convergence and tunnel vision waehrend foragingtesting
Launch all available agents in parallel waves for open-ended hypothesis generation on problems where the correct domain is unknown. Use when facing a cross-domain problem with no clear starting point, when single-agent approaches have stalled, or when diverse perspectives are more valuable than deep expertise. Produces a ranked hypothesis set with convergence analysis and adversarial refinement.
tools
Write integration tests for a Node.js CLI application using the built-in node:test module. Covers the exec helper pattern, output assertions, filesystem state verification, cleanup hooks, JSON output parsing, error case testing, and state restoration after destructive tests. Use when adding tests to an existing CLI, testing a new command, verifying adapter behavior across frameworks, or setting up CI for a CLI tool.
development
Screen a proposed trademark for conflicts and distinctiveness before filing. Covers trademark database searches (TMview, WIPO Global Brand Database, USPTO TESS), distinctiveness analysis using the Abercrombie spectrum, likelihood of confusion assessment using DuPont factors and EUIPO relative grounds, common law rights evaluation, and goods/services overlap analysis. Produces a conflict report with a risk matrix. Use before adopting a new brand name, logo, or slogan — distinct from patent prior art search, which uses different databases, legal frameworks, and analysis methods.
tools
Scaffold a new CLI command using Commander.js with options, action handler, three output modes (human-readable, quiet, JSON), and optional ceremony variant. Covers command naming, option design, shared context patterns, error handling, and integration testing. Use when adding a command to an existing Commander.js CLI, designing a new CLI tool from scratch, or standardizing command structure across a multi-command CLI.